Results for two alternative actions using the QASM simulator, the Tenerife device (ibmqx4) and the Melbourne device (ibmq_16_melbourne); in each case, 8192 shots were used, with \n
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7649",leadTitle:null,fullTitle:"Nanorods and Nanocomposites",title:"Nanorods and Nanocomposites",subtitle:null,reviewType:"peer-reviewed",abstract:"The book, Nanorods and Nanocomposites aims to provide the reader with an overview of the recent advances made on the synthesis of nanorods and nanocomposites and their emerging applications for a better lifestyle. The nanorods are a surprising gift to materials science from the research field of nanoscale materials. Nanorods promise to serve as a building block of the next-generation electronic and optoelectronic devices. Nanocomposite materials are multiphase solid materials that have one organic or inorganic nanoarchitectured compound with various nanostructures, such as nanoparticles, nanowires, nanorods, and nano-films, etc., or with multiphase solid materials (metals, oxides, polymers, and carbon). Due to the progressive physical, chemical, electrical, thermal, optical, electrochemical, and catalytic properties of nanocomposites, they exhibit multi-functional characteristics in a variety of engineering applications such as piezoelectrics, thermoresistors, sensors, energy-related technologies, water purification catalysts, electro-photonics, and so on. Despite the wide variety of applications due to their unique nanostructures, the fabrication of nanocomposites and the realization of their applications in different fields remains a challenging task. The focus of this book is to provide a platform for presentation of the latest knowledge and recent progress in synthesis, functionalization, and applications of nanocomposite materials. It is expected that this book presents the most attractive and versatile technological developments in the field of nanorods and nanocomposite materials and their applications that will provide a better understanding of the currently ongoing research in related fields.",isbn:"978-1-78984-469-6",printIsbn:"978-1-78984-468-9",pdfIsbn:"978-1-78985-564-7",doi:"10.5772/intechopen.77453",price:119,priceEur:129,priceUsd:155,slug:"nanorods-and-nanocomposites",numberOfPages:280,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"4ec1066a1d642f736d04932ded52ab44",bookSignature:"Morteza Sasani Ghamsari and Soumen Dhara",publishedDate:"March 11th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/7649.jpg",numberOfDownloads:13848,numberOfWosCitations:13,numberOfCrossrefCitations:31,numberOfCrossrefCitationsByBook:2,numberOfDimensionsCitations:56,numberOfDimensionsCitationsByBook:4,hasAltmetrics:1,numberOfTotalCitations:100,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 24th 2018",dateEndSecondStepPublish:"November 14th 2018",dateEndThirdStepPublish:"January 13th 2019",dateEndFourthStepPublish:"April 3rd 2019",dateEndFifthStepPublish:"June 2nd 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"64949",title:"Prof.",name:"Morteza",middleName:null,surname:"Sasani Ghamsari",slug:"morteza-sasani-ghamsari",fullName:"Morteza Sasani Ghamsari",profilePictureURL:"https://mts.intechopen.com/storage/users/64949/images/system/64949.jpg",biography:"Dr. Morteza Sasani Ghamsari is a senior researcher in the Photonics and Quantum Technologies Research School of Iranian Nuclear Science and Technology Research Institute. His research focuses on photonic materials including metamaterials, quantum\ndots, and plasmonic nanomaterials that can be used in a wide range of nanophotonics applications. His recent interests also include nano-bioimaging, 3D printing, nanostructures for tissue engineering (ZnO, TiO2, etc.) and biomaterials including carbon, graphene, and\ndiamond quantum dots. He is an editorial board member and reviewer for different\ninternational journals and has collaborated with local and international academics/\nresearchers on post-graduate research projects. He has edited four books and published four chapters and more than 105 articles in scientific journals and reviewed\nconference proceedings. His papers have been cited more than 2100 times with\nh-index 26 and i-10 index 46 (Google Scholar).",institutionString:"Photonics and Quantum Technologies Research School",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"4",institution:null}],equalEditorOne:{id:"196334",title:"Dr.",name:"Soumen",middleName:null,surname:"Dhara",slug:"soumen-dhara",fullName:"Soumen Dhara",profilePictureURL:"https://mts.intechopen.com/storage/users/196334/images/system/196334.jpeg",biography:"Dr. Dhara received his Ph. D in Physics in 2012 from Indian Institute of Technology Guwahati, India. Presently, he is associated with the Faculty of Science, Sri Sri University, India as an Assistant Professor in Physics. Prior to joining the current\naffiliation, he was a postdoctoral fellow at different renowned institutions, Kobe University Japan, S. N. Bose National Centre for Basic Sciences, India and Cardiff University, United Kingdom. He was awarded prestigious JSPS postdoctoral fellowship based on his research contribution on semiconducting nanowires. He has published more than 32 research articles including 1 review article in high profile international journals and 3 book chapters to his credit. His research trust areas of interests are semiconductor nanostructures, optoelectronics, solid state lighting and light sensors, spectroscopy of nanomaterials, thin-film transistors (TFTs) etc.",institutionString:"Sri Sri University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Sri Sri University",institutionURL:null,country:{name:"India"}}},equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"208",title:"Material Science",slug:"nanotechnology-and-nanomaterials-material-science"}],chapters:[{id:"66630",title:"Prologue: Nanorods – Recent Advances and Future Perspective",doi:"10.5772/intechopen.85837",slug:"prologue-nanorods-recent-advances-and-future-perspective",totalDownloads:628,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Soumen Dhara",downloadPdfUrl:"/chapter/pdf-download/66630",previewPdfUrl:"/chapter/pdf-preview/66630",authors:[{id:"196334",title:"Dr.",name:"Soumen",surname:"Dhara",slug:"soumen-dhara",fullName:"Soumen Dhara"}],corrections:null},{id:"67093",title:"Nanomaterials: An Overview of Nanorods Synthesis and Optimization",doi:"10.5772/intechopen.84550",slug:"nanomaterials-an-overview-of-nanorods-synthesis-and-optimization",totalDownloads:2156,totalCrossrefCites:10,totalDimensionsCites:25,hasAltmetrics:1,abstract:"Nanorods are nanostructures that are the object of fundamental and applied research. They may be prepared from carbon, gold, zinc oxide, and many other materials. They are bigger than individual atoms (measured in angstroms, 1 Å = 10−10 m) and also than small molecules. The turning point for nanomaterials research was the discovery of carbon nanotubes in 1991. Their mechanical, electrical, and optical properties depend upon their size, allowing for multiple applications. Also, nanorods may be functionalized for different applications. In this Chapter, the methods of synthesis and analysis, and the applications of carbon, zinc oxide, gold, and magnetic nanorods are reviewed.",signatures:"Alsultan Abdulkareem Ghassan, Nurul-Asikin Mijan and Yun Hin Taufiq-Yap",downloadPdfUrl:"/chapter/pdf-download/67093",previewPdfUrl:"/chapter/pdf-preview/67093",authors:[{id:"215489",title:"Dr.",name:"Alsultan Abdulkareem",surname:"Ghassan",slug:"alsultan-abdulkareem-ghassan",fullName:"Alsultan Abdulkareem Ghassan"},{id:"284157",title:"Dr.",name:"Nurul-Asikin",surname:"Mijan",slug:"nurul-asikin-mijan",fullName:"Nurul-Asikin Mijan"},{id:"284158",title:"Prof.",name:"Yun Hin",surname:"Taufiq-Yap",slug:"yun-hin-taufiq-yap",fullName:"Yun Hin Taufiq-Yap"}],corrections:null},{id:"66569",title:"ZnO Nanorods for Gas Sensors",doi:"10.5772/intechopen.85612",slug:"zno-nanorods-for-gas-sensors",totalDownloads:995,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"ZnO nanorods have been widely used to detect low-concentration gases due to its range of conductance variability, response toward both oxidative and reductive gases, and highly sensitive and selective properties. In this chapter, the fabrication methods of ZnO nanorods, their controllable growth, their different configurations, their modification for improving sensing property, and their composites for gas sensors are thoroughly introduced. The synthesis methods to fabricate ZnO nanorods consist of hydrothermal method, microemulsion synthesis, microwave-assisted hydrolysis preparation, gas-solution-solid method, spray pyrolysis, sonochemical route, simple solution route, and so on. The controllable fabrication of ZnO nanorods can be realized by control growth, selective growth, and diameter regulation. Different structures formed by ZnO nanorods include cross-linked configuration, flowerlike structure, and multishelled hollow spheres and hollow microsemispheres, as influence their sensing properties. ZnO nanorods can be modified by doping, functionalization, decoration, and sensitization for enhancing the sensing property. ZnO can be combined with graphene, carbon nanotubes, SnO2, In2O3, and Fe2O3 to form core-shell composites for gas sensor.",signatures:"Yanmin Wang",downloadPdfUrl:"/chapter/pdf-download/66569",previewPdfUrl:"/chapter/pdf-preview/66569",authors:[{id:"289339",title:"Dr.",name:"Yanmin",surname:"Wang",slug:"yanmin-wang",fullName:"Yanmin Wang"}],corrections:null},{id:"69864",title:"Study of Structural and Melting Properties of Gold Nanorods",doi:"10.5772/intechopen.84682",slug:"study-of-structural-and-melting-properties-of-gold-nanorods",totalDownloads:733,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"MD simulations combined with the embedded-atom method have been applied to study the structural and melting properties of gold nanorods (AuNRs) of different sizes. The simulation results for the actual structure of AuNRs obtained after energy minimization processes revealed that the AuNRs with largest cohesive energies tend to be structurally more stable than those with smallest ones. Then, it was found that each actual structure of AuNR is classified as an irregular structure composed of a crystalline gold core covered by an amorphous gold shell. In addition, the results showed that the melting of the AuNR surface is an inhomogeneous, gradually occurring process. Besides, it was established that the premelting ratio is inversely correlated with the AuNR size, indicating that the premelting phenomenon is more pronounced in large NP sizes than in small ones.",signatures:"Rida Essajai",downloadPdfUrl:"/chapter/pdf-download/69864",previewPdfUrl:"/chapter/pdf-preview/69864",authors:[{id:"272126",title:"Dr.",name:"Rida",surname:"Essajai",slug:"rida-essajai",fullName:"Rida Essajai"}],corrections:null},{id:"66494",title:"Nanostructure Technology for EO/IR Detector Applications",doi:"10.5772/intechopen.85741",slug:"nanostructure-technology-for-eo-ir-detector-applications",totalDownloads:1049,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"This chapter covers recent advances in the development of nanostructure-based material technologies to benefit next-generation electro-optical (EO) and infrared (IR) sensor and imager applications. Nanostructured materials can now be integrated into a variety of technological platforms, offering novel optoelectrical properties that greatly enhance device performance in many practical applications. Use of novel carbon nanotube (CNT) based materials has enabled new approaches for applying nanostructure design methodologies that can offer enhanced performance for low-cost bolometers for IR detection and imaging applications. We will discuss the development of carbon nanostructure based infrared detectors and arrays, including concepts that will provide high performance, high frame rate, and uncooled microbolometers for mid-wave infrared (MWIR) and long-wave infrared (LWIR) band detection. In addition, nanostructured antireflection (AR) coatings are being developed that significantly enhance transmission over a broad spectrum, providing substantial improvements in device performance compared to conventional thin film AR coatings. These nanostructured AR coatings have been demonstrated over visible to LWIR spectral bands on various substrates. In this chapter, we discuss both theoretical and measured results of these diverse nanostructure technologies to advance sensing performance over a wide range of spectral bands for defense, space, and commercial applications.",signatures:"Ashok K. Sood, John W. Zeller, Gopal G. Pethuraja, Roger E. Welser, Nibir K. Dhar and Priyalal S. Wijewarnasuriya",downloadPdfUrl:"/chapter/pdf-download/66494",previewPdfUrl:"/chapter/pdf-preview/66494",authors:[{id:"23657",title:"Dr.",name:"Ashok K.",surname:"Sood",slug:"ashok-k.-sood",fullName:"Ashok K. Sood"},{id:"210274",title:"Dr.",name:"John W.",surname:"Zeller",slug:"john-w.-zeller",fullName:"John W. Zeller"},{id:"277696",title:"Dr.",name:"Gopal G.",surname:"Pethuraja",slug:"gopal-g.-pethuraja",fullName:"Gopal G. Pethuraja"},{id:"277697",title:"Dr.",name:"Roger E.",surname:"Welser",slug:"roger-e.-welser",fullName:"Roger E. Welser"},{id:"294682",title:"Dr.",name:"Nibir K.",surname:"Dhar",slug:"nibir-k.-dhar",fullName:"Nibir K. Dhar"},{id:"294683",title:"Dr.",name:"Priyalal S.",surname:"Wijewarnasuriya",slug:"priyalal-s.-wijewarnasuriya",fullName:"Priyalal S. Wijewarnasuriya"}],corrections:null},{id:"70674",title:"Metallo-Dielectric Colloidal Films as SERS Substrate",doi:"10.5772/intechopen.90313",slug:"metallo-dielectric-colloidal-films-as-sers-substrate",totalDownloads:708,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:1,abstract:"Along this chapter, we probe that the discrete dipole approximation models fairly well the optical response of periodic systems. Herein, we use it to model the reflectance and transmittance, at normal incidence, of colloidal films made of SiO2 spheres. As the thickness increases from 1 to 12 layers, the photonic band gap shifts to the blue tending to the value corresponding to a 3D opal, 442 nm. A film with more than eight layers resembles the bulk properties of a 3D opal. Our results are compared to a real sample. Besides, we show that taking advantage of the wide and asymmetrical absorbance spectrum of an opal with Au NPs is possible to identify the contribution of each component in the overall spectrum, through a deconvolution analysis. Finally, we present the electric field intensity as the content of metal NP increases in a monolayer. We consider NPs one order of magnitude smaller than the silica spheres, and then, 6, 9, and 17 NPs are hosted in the void. Similar average electric field intensities, about 11 times the incident intensity, are obtained with Au and Ag NPs. But, the spots with these intensities cover a bigger area with Ag NPs than with Au NPs.",signatures:"Ana L. González, Arturo Santos Gómez and Miller Toledo-Solano",downloadPdfUrl:"/chapter/pdf-download/70674",previewPdfUrl:"/chapter/pdf-preview/70674",authors:[{id:"309184",title:"Dr.",name:"Ana L.",surname:"González",slug:"ana-l.-gonzalez",fullName:"Ana L. González"},{id:"309343",title:"Dr.",name:"Miller",surname:"Toledo-Solano",slug:"miller-toledo-solano",fullName:"Miller Toledo-Solano"},{id:"309344",title:"MSc.",name:"Arturo",surname:"Santos Gómez",slug:"arturo-santos-gomez",fullName:"Arturo Santos Gómez"}],corrections:null},{id:"67284",title:"Kinetic Features of Synthesis of Epoxy Nanocomposites",doi:"10.5772/intechopen.85137",slug:"kinetic-features-of-synthesis-of-epoxy-nanocomposites",totalDownloads:1089,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Kinetic features of the formation of epoxy nanocomposites with carbon (nanotubes, graphene, and graphite), metal-containing, and aluminosilicate (montmorillonite and halloysite) fillers are considered. In contrast to linear polymers, epoxy nanocomposites are obtained only via the curing of epoxy oligomers in the presence of filler or the corresponding precursor. These additives may affect the kinetics of the process and the properties of the resulting matrix. A high reactivity of epoxy groups and a thermodynamic miscibility of epoxy oligomers with many substances make it possible to use diverse curing agents and to accomplish curing reactions under various technological conditions. The mutual effect of both a matrix and nanoparticles on the kinetics of the composite formation is discussed.",signatures:"Vadim Irzhak",downloadPdfUrl:"/chapter/pdf-download/67284",previewPdfUrl:"/chapter/pdf-preview/67284",authors:[{id:"282050",title:"Prof.",name:"Vadim",surname:"Irzhak",slug:"vadim-irzhak",fullName:"Vadim Irzhak"}],corrections:null},{id:"67438",title:"Graphene-Like Nanocomposites",doi:"10.5772/intechopen.85513",slug:"graphene-like-nanocomposites",totalDownloads:1086,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"After discovering graphene and its extraordinary intrinsic, other graphene-like nanomaterials (GLNs) became a topic of interest to many scientists of the time. Recently, GLNs, nanosheets of sp2-hybridized atoms arranged in a two-dimensional lattice with impressive thermal, mechanical, and electrical properties, has attracted both academic and industrial interest because it can produce dramatic improvements in properties at very low filler content. Many studies have been performed on GLNs with various applications, including boron nitride nanosheets, transition metal dichalcogenides, and other two-dimensional (2D) nanomaterials. This rapid advance provides a strong appetence for further research on properties of GLNs, including mechanical, electrical and thermal properties and their potential applications in the nanocomposites industry.",signatures:"Zahra Rafiei-Sarmazdeh and Seyed Javad Ahmadi",downloadPdfUrl:"/chapter/pdf-download/67438",previewPdfUrl:"/chapter/pdf-preview/67438",authors:[{id:"289558",title:"Ph.D.",name:"Zahra",surname:"Rafiei-Sarmazdeh",slug:"zahra-rafiei-sarmazdeh",fullName:"Zahra Rafiei-Sarmazdeh"},{id:"295566",title:"Prof.",name:"Seyed Javad",surname:"Ahmadi",slug:"seyed-javad-ahmadi",fullName:"Seyed Javad Ahmadi"}],corrections:null},{id:"67547",title:"Polymer Nanocomposite-Based Electrochemical Sensors and Biosensors",doi:"10.5772/intechopen.86826",slug:"polymer-nanocomposite-based-electrochemical-sensors-and-biosensors",totalDownloads:759,totalCrossrefCites:6,totalDimensionsCites:9,hasAltmetrics:0,abstract:"Polymer nanocomposites (PNCs) play a significant role in modern day life and are widely studied for extensive properties which make them appealing to numerous applications. They are synthesized with scalable processing procedures with several nanoscale variations of fillers and forms leading to specific sensing applications. In this chapter, PNC-based electrochemical sensors and biosensors like DNA biosensors and immunosensors are discussed. These sensors related PNC applications uses nanofillers of various combinations like conductive polymers with graphene (Grp), carbon nanotubes (CNTs), and metal nanoparticles, which endow high electrical conductivity, effective surface area, and fast electron transfer rate. Currently, wearable devices based on electrochemical Sensors and biosensors have been of great interest in the detection of both physiological and environmental analytes.",signatures:"Baiju John",downloadPdfUrl:"/chapter/pdf-download/67547",previewPdfUrl:"/chapter/pdf-preview/67547",authors:[{id:"285158",title:"Dr.",name:"Baiju",surname:"John",slug:"baiju-john",fullName:"Baiju John"}],corrections:null},{id:"66281",title:"Nanocomposite-Based Graphene for Nanosensor Applications",doi:"10.5772/intechopen.85136",slug:"nanocomposite-based-graphene-for-nanosensor-applications",totalDownloads:1149,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Nanocomposites based on carbon nanomaterial particularly in graphene oxide, graphene quantum dots, and doped graphene quantum dots with improved biocompatibility have been increasing interests in the field of drug delivery, biosensor, energy, imaging and electronic. These nanomaterials as new kinds of fluorescent probes and electrochemical sensors all display ultrasmall size, good photostability, and excellent biocompatibility. In this chapter, we summarize an updated advance in the development of graphene and its related derivatives of synthesis methods and biomedical applications as nanosensors for detection of metal ions, inorganic ions, amino acids, proteins, saccharides and small molecules, drug molecules, and so on.",signatures:"Rumei Cheng and Shengju Ou",downloadPdfUrl:"/chapter/pdf-download/66281",previewPdfUrl:"/chapter/pdf-preview/66281",authors:[{id:"285444",title:"Dr.",name:"Rumei",surname:"Cheng",slug:"rumei-cheng",fullName:"Rumei Cheng"},{id:"285449",title:"Dr.",name:"Shengju",surname:"Ou",slug:"shengju-ou",fullName:"Shengju Ou"}],corrections:null},{id:"69446",title:"Mechanical and Tribological Properties of Epoxy Nano Composites for High Voltage Applications",doi:"10.5772/intechopen.88236",slug:"mechanical-and-tribological-properties-of-epoxy-nano-composites-for-high-voltage-applications",totalDownloads:917,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The tribological and mechanical properties of organomodified montmorillonite (oMMT)-incorporated Epoxy (Epoxy-oMMT), vinyl ester (vinyl ester-oMMT) and titanium dioxide (TiO2)-filled Epoxy (Epoxy-TiO2) nanocomposites are discussed below. Implications of introducing oMMT and TiO2 nanoparticles on mechanical and dry sliding wear properties are presented using micrographs of cast samples and through observations of wear affected surface of nanocomposites. Distribution of nanoparticles and their influence on properties are being emphasized for understanding the wear properties. The data on mechanical and tribological properties determined experimentally are compared with published literature. The main focus is to highlight the importance of nanofillers in the design of wear-resistant thermoset polymer composites. A detailed study of strength and moduli of Epoxy-oMMT, Epoxy-TiO2 and vinyl ester-oMMT nanocomposites was taken up as a part of the investigation. A discussion on density, hardness, tensile, flexural test data, and friction and wear of nanocomposites and analysis of results by comparison with prevalent theoretical models and published results of experiments are presented.",signatures:"Rashmi Aradhya and Nijagal M. Renukappa",downloadPdfUrl:"/chapter/pdf-download/69446",previewPdfUrl:"/chapter/pdf-preview/69446",authors:[{id:"227309",title:"Dr.",name:"Rashmi",surname:"Aradhya",slug:"rashmi-aradhya",fullName:"Rashmi Aradhya"},{id:"227469",title:"Dr.",name:"Nijagal M.",surname:"Renukappa",slug:"nijagal-m.-renukappa",fullName:"Nijagal M. 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Particularly, this opens up prospects for designing the new materials from immiscible metallic elements, which is the main topic in designing of alloys as well as materials science progression.",signatures:"Hussein Shokrvash, Rahim Yazdani Rad, Abouzar Massoudi and Reza Shokrvash",downloadPdfUrl:"/chapter/pdf-download/68260",previewPdfUrl:"/chapter/pdf-preview/68260",authors:[{id:"297264",title:"Dr.",name:"Hussein",surname:"Shokrvash",slug:"hussein-shokrvash",fullName:"Hussein Shokrvash"},{id:"297268",title:"Dr.",name:"Rahim",surname:"Yazdani Rad",slug:"rahim-yazdani-rad",fullName:"Rahim Yazdani Rad"},{id:"297269",title:"Dr.",name:"Abouzar",surname:"Massoudi",slug:"abouzar-massoudi",fullName:"Abouzar Massoudi"},{id:"318203",title:"Dr.",name:"Reza",surname:"Shokrvash",slug:"reza-shokrvash",fullName:"Reza Shokrvash"}],corrections:null},{id:"69037",title:"Classification of Electrospinning Methods",doi:"10.5772/intechopen.88654",slug:"classification-of-electrospinning-methods",totalDownloads:990,totalCrossrefCites:4,totalDimensionsCites:8,hasAltmetrics:0,abstract:"Electrospun nanofibers are being used in a variety of performance apparel applications where their unique properties add to their functionality. Those properties include, small fiber diameter, high surface area, potential to combine chemistry, layer thinness, high porosity, filtration properties, and low basis weight. Electrospinning has been considered as an efficient technique for nanofiber web formation. Polymers have been electrospun into nanofibers mostly after being dissolved in solvent and melted. This chapter presents a comprehensive summary of existing electrospinning methods. Electrospinning methods are classified into different categories depend upon jet formation.",signatures:"Muhammad Waqas Munir and Usman Ali",downloadPdfUrl:"/chapter/pdf-download/69037",previewPdfUrl:"/chapter/pdf-preview/69037",authors:[{id:"226060",title:"Dr.",name:"Muhammad Waqas",surname:"Munir",slug:"muhammad-waqas-munir",fullName:"Muhammad Waqas Munir"},{id:"317775",title:"Dr.",name:"Usman",surname:"Ali",slug:"usman-ali",fullName:"Usman Ali"}],corrections:null},{id:"69529",title:"Computational Analysis of Nanostructures for Li-Ion Batteries",doi:"10.5772/intechopen.88712",slug:"computational-analysis-of-nanostructures-for-li-ion-batteries",totalDownloads:950,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Due to the energy crisis, the focus on the study of new materials has increased vastly. For the increasing demand of renewable energy, there are different ways suggested to attain that, which include the rechargeable batteries and the need to achieve them at smaller costs and for longtime use. Lithium ion batteries have gained a lot of attention for that specific reason. Along with the experiments, the easier way to understand and increase the efficiency of these materials for LIBs is to study them through simulations and theoretically. Density functional theory (DFT)-based study gives us an insight into the internal workings of the compounds used in lithium ion batteries (LIBs). In this chapter, an analysis of different structures is presented for use in LIBs, which mainly includes carbon nanostructures or nanotubes as well as 2D material graphene. The various ways in which the carbon-based structure is enhanced include doping into the structure, heterostructure of graphene with other 2D materials, and adsorption of atoms like Si onto the surface. 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Amino acids constitute an impressive and mysterious class of organic molecules, impressive because they form all proteins of living beings and mysterious because of their simplicity. In fact, amino acids — in their α form and in the
As it is well established, every cell of the living being on the Earth uses a set of 20 amino acids to produce all kinds of proteins [1, 2]. The 20 standard amino acids can be classified as (i) unpolar (alanine, valine, leucine, isoleucine, methionine, proline, phenylalanine, and tryptophan) and (ii) polar (glycine, arginine, asparagine, cysteine, glutamine, lysine, aspartic acid, glutamic acid, serine, threonine, tyrosine, and histidine). Additionally, some compendia include selenocysteine and pyrrolysine as belonging to the group of proteinogenic amino acids, but this is not unanimity yet. In the last years, a series of studies have investigated the vibrational and structural properties of amino acid crystals [3–42].
\nOne of the main techniques to investigate vibrational properties of materials, whatever it is, is Raman spectroscopy. The technique consists in the interaction of light from a laser source with the material and further scattering of the light. The scattered light carries information about the rotational, vibrational, and, eventually, electronic states of the material. Concerning the Raman scattering effect, a pivotal concept is the scattering cross-section, σ, which represents a likelihood of a scattering event to occur [43]. It is defined as the rate at which energy is removed from the incident photon by the scattering substance, divided by the rate at which energy in the incident photon crosses a unit area perpendicular to its direction of propagation [43], expressed as in Eq. (1):
\nIn this equation,
with the sum performed over all possible eigenstates
In this expression, the kets |
In this chapter, we summarize the main studies on Raman spectroscopy applied to proteinogenic amino acid molecules and crystals and point new perspectives on the subject.
\nIn the present work, we have used two experimental set-ups: an instrument using Fourier transform mechanism and a dispersive (conventional) spectrometer. On the one hand, FT-Raman spectra were recorded using a Bruker RFS100/S FTR system and a D418-T detector, with the sample excited by means of the 1064 nm line of a Nd:YAG laser. In these cases, the spectral resolution was 4 cm−1. On the other hand, the conventional Raman spectra were excited with the 514.5 nm line of argon ion lasers and the scattered light was analyzed in a Jobin-Yvon T64000 spectrometer equipped with the nitrogen cooled CCD system. Typically, the spectral resolution in the conventional Raman experiments was 2 cm−1. Theoretically, the bands appearing in the Raman spectrum are independent of the excitation energy of the laser, although the intensity of the scattered light is proportional to
In the discussion, we will separate the discussion according to the polarity of the lateral groups of the amino acids. We begin with the apolar amino acids. This group is composed of the following amino acids: glycine, alanine, valine, leucine, isoleucine, phenylalanine, methionine, tryptophan, and proline.
\nPolarized Raman spectra of glycine (α-form) for two scattering geometries in the high wavenumber region of the spectrum.
Although the chirality itself is a theme of great relevance, in the present chapter we discuss only the properties of the l-chiral sister of amino acid crystals (those present in the proteins) and do not furnish further information about the phenomenon. The simplest chiral amino acid is
Raman spectrum of
FT-Raman spectra of
Figure 3 shows the FT-Raman spectrum of
Raman spectra of
Another amino acid with nonpolar characteristics is
FT-Raman spectra of
Figure 5 presents the FT-Raman spectrum of
FT-Raman spectra of
The Raman spectra of
Among the polar amino acids, serine, cysteine, asparagine, glutamine, threonine, and tyrosine are neutral. Figure 7 shows the Raman spectra of l-cysteine.HCl, l-serine, l-glutamine, and l-asparagine.H2O as obtained through a Fourier-transform Raman spectrometer in the spectral range from 50 to 3500 cm−1. From 1800 to 2800 cm−1 no mode is observed, except in the spectrum of l-cysteine.HCl, where a stretching vibration of SH appears at ~ 2550 cm−1. In fact, as mentioned in the previous paragraph, cysteine is the only proteinogenic amino acid that presents an S-H bond and, as a consequence, is the only amino acid to present a peak in this region.
\nFT-Raman spectra of polar (neutral) amino acids
It is important to remember that
Obviously, if you change the sulfur atom by an oxygen atom, the hydrogen bonds involving SH groups cease to exist. As a molecule,
The radical NH2-(C=O)-CH2 characterizes the amino acid l-
FT-Raman spectrum of
The FT-Raman spectrum of
FT-Raman spectrum of
Figure 9 shows the FT-Raman spectrum of
Figure 10 shows the FT-Raman spectra of
FT-Raman spectra of
l-histidine was investigated through Raman spectroscopy in a recent paper that explored the vibrational behavior of the crystal under cryogenic conditions [41]. l-histidine can crystallize in two different polymorphs with monoclinic or orthorhombic symmetry. The work of reference [41] has investigated the orthorhombic form of the crystal that presents a
Raman spectra of
Up to now, the Raman scattering investigations have furnished an interesting picture about the vibrational aspects of diverse amino acids. Some studies have even studied the behavior of the crystals under extreme conditions, low temperature or high pressure. However, a complete understanding involving connections, for example, between the hydrogen bonds and the physical properties of the crystal is still lacking. Obviously, some preliminary attempts are already known, such as a possible connection between the dimensions of hydrogen bonds and the behavior of torsional vibration of NH3+ under high pressure (for l-alanine, l-threonine, and taurine [17]). A fundamental question in biochemistry is to realize why the proteins of all living beings are formed by the l-form of amino acids (the d-form is found only isolated in the plasma of certain cells). Some glimpses were given by Abdus Salam who speculates the occurrence of a phase transition explained through BCS theory, gauge field theory, and Higgs mechanism [44]. There is also suggestion that ultraviolet radiation should be able to select one of the chiral forms of the amino acid, but, in fact, all these suggestions are suppositions waiting for confirmation. This problem deserves future investigations. But, is the behavior of d-amino acid crystals the same of l-amino acids under extreme conditions? At first, the answer to this question should be positive because both l- and d-forms of the amino acids are equivalent from an energetic point of view. However, some preliminary results point to different behavior for the two forms in some special cases, but we do not have space to discuss such intriguing point in this chapter. Maybe, surprising information is waiting for us in the coming years.
\nThe success obtained by the investigation of amino acids has incentivized the study of other simple organic molecules of living beings. After furnishing a more or less closed picture about amino acids, the next natural step is the study of peptides, but we will not discuss them in this chapter. We prefer to analyze another natural choose, molecules involved in the DNA structure. One example we will explore in this chapter is thymidine, a nucleoside constituted of a deoxyribose and the pyrimidine base thymine. It is found in the DNA of all living organisms. The Raman spectrum presents a very intense set of bands in the low wavenumber region that are associated with the lattice modes (Figure 12). This is very interesting because in future analysis of the crystal under extreme conditions, the behavior of the lattice modes should be a pivotal point in order to understand eventual structural modification. A strong band observed at 1665 cm−1 is assigned as in-plane vibration involving C = O and C = C and a band at 1690 cm−1 is assigned as stretching C = O, ν(C=O). Bending of CH3, δ(CH3), is identified as the band at 1438, 1457, and 1480 cm−1. The band observed at 1031 cm−1 is associated with bending of CNH, δ(CNH), and the band at 1000 cm−1 is associated with bending OCH, δ(OCH). An out-of-plane vibration involving CH is observed at 972 cm−1 and a pyrimidine ring breathing is observed at 773 cm−1. Additionally, out-of-plane vibration involving CCH3 group is observed at 396 and 378 cm−1 and in plane vibration involving the same group is observed at 276 and 306 cm−1. In the high wavenumber region of the Raman spectrum is possible to observe a series of bands, among them one observed at 3298 cm−1 that was assigned as stretching of OH, ν(OH). A series of bands is observed at 2952, 2965, 2973, and 2991 cm−1 and they are classified as stretching of CH, CH2, and CH3 units. Finally, let us single out an important point related to the study of thymidine, its behavior as a function of temperature. In order to make the presentation of this section more complete, we have performed study of thymidine crystal under low temperature. Analysis of the Raman spectra of thymidine showed that the wavenumber of several bands presents jumps at about 160 K, suggesting the occurrence of a conformational modification due change of hydrogen bonds. A comparison with the behavior of amino acid crystals will be welcome, and we hope that in a few time we will have an overview of the subject.
\nRaman spectrum of thymidine; in the inset a representation of the molecule.
In résumé, in this chapter, a complete picture about the Raman spectra of the 20 proteinogenic amino acid crystals was furnished and some aspects related to the modification of these spectra under extreme conditions were also discussed. As additional information we discussed the Raman spectrum of thymidine, an organic molecule involved in the formation of DNA.
\nThe authors acknowledge financial support from CNPq and FUNCAP through PRONEX program.
\nResearch on quantum neural machine learning has, until recently, mostly been a theoretical effort, anticipating a future where quantum computers would become available and sufficiently advanced to support quantum neural machine learning [1, 2, 3, 4, 5]. However, we now have quantum computers that are capable of implementing quantum artificial neural networks (QUANNs) experimentally, and one is able to access these computers via cloud. This brings QUANNs from the purely theoretical realm to the experimental realm, setting up the new stage for the expansion of quantum connectionism. In the current chapter, we address this issue, by implementing different QUANNs on IBM’s quantum computers using the IBM Q Experience cloud-based access.
\nThe chapter is divided into three sections. In Section 2, we address the basic properties of quantum neural computation, the connection with the quantum circuit computation model, and how different interpretations of quantum mechanics may address the basic computational dynamics involved.
\nIn Section 3, we discuss how the IBM quantum computers can be considered QUANNs, illustrating with an example of a QUANN applied to the problem of the XOR Boolean function computation, implemented experimentally on two of IBM’s devices (Section 3.1); afterward (Section 3.2), we turn to the experimental implementation of quantum robotics and quantum decision with a more complex form of quantum neural computation in the form of a variant of quantum neural reinforcement learning (QNRL), applied to a problem of decision under risk, where the agent must learn the optimal action that leads to the highest expected reward in a classical gamble.
\nThe problem is first addressed in terms of the fundamental equations which employ quantum adaptive computation, namely quantum adaptive gates; then, we implement it experimentally on IBM’s quantum computers and, afterward, we address the main Python code that was used to run the algorithm on these computers, thus, introducing quantum object-oriented programming (QOOP) and reflecting on its relevance for research on quantum artificial intelligence.
\nWhile, in Section 3.1, the main goal is to illustrate the implementation of QUANNs in a case where QUANNs exhibit a greater efficiency over classical ANNs, in Section 3.2, our main goal is not to address the speed-up of quantum algorithms over classical ones or even the greater efficiency of quantum algorithms over classical ones, but rather to provide for a reflection on the first steps for a possible future where quantum computation is incorporated in different (classical) robotic systems by way of the internet of things and cloud-based access to quantum devices, and the role that quantum adaptive computation may play in such a future.
\nIn particular, in Section 3.2, we illustrate how a QUANN can become adaptive with respect to a problem that is given to it, in this case, a decision problem under risk, therefore, allowing us to address how QOOP can be employed to simulate an artificial agent, with a QUANN as its cognitive architecture, that must make a decision when presented a problem of classical decision under risk; therefore, our main goal in Section 3.2, from a computer science standpoint, is to address how a quantum artificially intelligent system decides when faced with a classical decision under risk problem, using QUANNs and QOOP.
\nIn Section 4, we conclude with a chapter review and a reflection on future directions for cloud-based quantum-enabled technologies and QOOP.
\nIn order to address quantum neural computation, we need to first introduce some notation, which is commonly used in quantum computation, namely, we use the standard Dirac’s
with the corresponding
The unit operator on the two-dimensional Hilbert space, spanned by the basis \n
The Walsh-Hadamard transform unitary operator is, in turn, given by:
We also use the usual notation for the ket vectors \n
Besides the above notation, we denote the binary alphabet by \n
Using this notation, we are now ready to address some basic general properties of quantum neural computation.
\nThe basic computational unit of a QUANN is a neuron with a two-level firing dynamics that can be described by the neural firing operator [5, 6]:
where \n
The eigenvectors for this operator are given by:
Therefore, the eigenvector \n
Therefore, the eigenvector \n
For a neural network with
and any pair of neural firing operators commute; that is, for \n
which leads to the eigenvalue spectrum for the neural network:
The general computational dynamics of a
Formally, then, an
The sequence is read from right to left and such that \n
Formally, given a general initial density operator, representing the initial neural field dynamics of the QUANN, expressed as follows:
the quantum computation can be addressed in terms of the propagation:
The firing patterns, in Eq. (15), \n
For the cases where there is a mismatch between the final output firing dynamics, that is, when \n
However, when \n
This means that the neural field computes each alternative final firing pattern \n
From a computer science standpoint, this two-directional propagation, which is a basic result of the quantum circuit-based computation (generalizable to any type of quantum computer), exhibits a form of forward propagation and backward propagation, where the forward and backward amplitudes can be, from a computer science standpoint, addressed in terms of a probe and response dynamics, respectively; returning to Eq. (15), each amplitude \n
When the two output firing patterns do not match, \n
These dynamics are simultaneous, that is, the QUANN processes in both the forward and backward directions simultaneously to arrive at the final result.
\nThe above fundamental computational dynamics is characteristic of quantum mechanics, and not limited to QUANNs or quantum computation, nor is it dependent on one’s interpretation of quantum mechanics. It arises when one considers the structure of a general density operator for a quantum system [6].
\nIndeed, as an example, let us consider a general density operator for a quantized observable \n
The off-diagonal components of such an operator are such that there is no matching between the corresponding eigenvalues, only in the diagonal do we find a matching between the eigenvalues. The
In this way, only when a probed alternative eigenvalue finds a matching response eigenvalue do we have an
It is important to stress that this
Therefore, embedded within quantum mechanics’ formalism, we find an account of Born’s probability rule. Furthermore, given a Hamiltonian operator for the quantum system \n
In the case of the illustrative general example, given in Eq. (18), we get:
where \n
Cramer was, however, the first to fully address the consequences of this dynamics and propose the concept of
While Cramer [7] addresses the
It is important to clarify what an interpretation of quantum mechanics is and why there are different interpretations of the same theoretical body and equations. It turns out that the main interpretations do not disagree on the formalism, methods, and how the mathematics is built and applied for prediction of experimental results. The interpretations do not stem from any ambiguity or lack of robustness in the formalism and in the application of the formalism, they stem from the fact that not everything is accounted for by the formalism, and that is where the interpretations come in.
\nTo better frame this issue, one must consider the nature of the theory that one is dealing with, what it explains, and what is outside its theoretical scope.
\nQuantum mechanics is, in fact, a probabilistic theory of the quantized dynamics of fundamental physical fields, fields that work at the level of the building blocks of physical nature. The physical theory and methods that form the basic structure of quantum mechanics developed progressively from empirical observations and statistical findings on fundamentally random outcomes of physical experiments dealing with the quantum level.
\nThis means that physicists found the basic rules for (dynamical) probability assignments that robustly capture the main probabilistic dynamics of quantum fields.
\nTo understand the nature of the theory, it is important to stress that it was born out of laboratory experiments, that it was built out of the statistical patterns found in an observed stochastic dynamics, and that it was aimed at predicting the statistical distributions of that stochastic dynamics. The current formulation of quantum mechanics essentially encompasses a set of rules for obtaining the probabilities associated with the dynamics of quantum systems.
\nThe theory does not state anything beyond that. A point that allowed many physicists to pragmatically take the theory as it is, not dwelling on the
When one starts to ask on the
In the pragmatic stance, one just takes the formalism as a recipe, calculates the
One way out of the ontological questioning would be to assume that we are dealing with human representations, that we cannot speak of a reality independent of human representations and experiments, that is, that the question of what reality really is outside those representations and experiments cannot be answered and, therefore, one just postulates that the field follows the
Contrasting with the Copenhagen school are the ontological schools, so called because they assume a reality independent of human representations and experiments.
\nQuantum mechanics itself does not state anything about this, so there is room for proposals; Cramer [7], for instance, considers these interpretations as actually new physical theories that go beyond the strict formalism and introduce new conjecture that cannot be tested under the formalism itself. The ontological interpretations that include the Bohmian and Everettian lines are all consistent with the formalism, that is, they agree with the formalism and mathematical methods of quantum mechanics and, therefore, cannot be tested using just the formalism.
\nIn the case of Cramer, his proposed transactional interpretation (TI) of quantum mechanics [7] considers a probabilistic selection in terms of a
Everett [8] assumed that all alternatives for a quantum system are actualized simultaneously in different cosmic branches. This led to the many worlds interpretation (MWI). MWI’s proposal is, thus, that reality is multidimensional and the formalism is considered to be describing such a multidimensional reality that is a single Cosmos with many worlds (many branching lines). This conjecture cannot be tested empirically; it is consistent with the formalism and agrees with the predictions of quantum mechanics. Namely, the statistical measure associated with repeated experiments made on quantum systems tends to coincide with the
Bohm initially worked on the pilot wave model for quantum mechanics but just as a first approximation. Indeed, in [9], the author addressed the pilot wave model as a first approximation but then criticized it, in particular, in regard to the assumption of a particle being separate from the field; even more, in [9], Bohm defended that, at a lower level, the particle does not move as a permanently existing entity, but is formed in a random way by suitable concentrations of the field’s energy. Furthermore, he considered that any quantum field was characterized by a nonlocal dynamics, and that the equations of quantum mechanics were just an approximation, an average that emerged at the quantum level, proposing the concept of quantum force and hypothesizing the existence of a subquantum level, so that both the quantum and subquantum levels play a fundamental role in the field’s dynamics.
\nGonçalves in [6] addressed the relation between the
In this interpretation, the
As argued in [6], the intensity (modulated)
This quantum computational dynamics, present in quantum mechanics’ formalism, works as a basic form of
There is a consequence that comes from assuming the Bohmian framework, namely, from the Bohm’s conjecture that a subquantum level randomness averages out at the quantum level, but may lead to small deviations from the theoretical probabilities [9, 10]; if such a conjecture holds, then deviations in quantum physical experiments with actual quantum computers may always take place, such that, even if we were to reduce the interaction with the environment to zero (or close to zero), we could still have deviations due to subquantum level fluctuations, so that the field would tend to follow the lines of force with probabilities that would hold on average but with some deviations that might occur in each case.
\nWhile Bohm’s proposal is potentially testable, at the present stage of scientific and technological development, we have not yet found a way to test the subquantum proposal regarding quantum physical systems, and, in particular, to test, empirically, the possibility that deviations from the main lines of force that agree with a theory’s prediction are not due to environmental noise and, rather, to subquantum level fluctuations.
\nAll main interpretations, as reviewed above, agree with quantum mechanics’ general predictions, even Bohm, who considers that the predictions will hold empirically on average, therefore, the interpretations do not have, at present, a direct consequence on the results of technological implementation of quantum computers, as long as one is not dealing with fundamental ontological issues regarding the computational nature of quantum fields, but rather with the technological application of quantum algorithms, one is free to choose any interpretation since it is consistent with the main formalism and results.
\nWe consider, nonetheless, that future research directions on Bohm’s conjectural line may prove fruitful both at a theoretical and technological level, concerning the issue of quantum errors. This point, however, goes beyond the current chapter’s scope. The results that follow, as of any work using the formalism of quantum mechanics, hold for any interpretation of the theory. However, having made that point, we will return to Bohm’s conjecture regarding some of the results obtained in the next section, regarding the issue of quantum computing errors.
\nThe development of quantum computing devices has opened up the possibility of transitioning from the purely theoretical approach to QUANNs to an experimental implementation of these networks. A particular example is IBM’s quantum processors, available via cloud, under IBM Q Experience, using
The term
IBM has different
The “IBM Q 5 Tenerife” device is a 5
The “IBM Q 16 Melbourne” device is a 14
From a computational model standpoint, we can treat the network connections and resulting quantum computing framework, provided by these physical devices, as a form of QUANN, where the conditional neural gates must obey the quantum device’s basic topology in what regards the possible quantum controlled gates.
\nThis is so because the quantum registers are linked in specific topologies that limit how conditional quantum operations are implemented; this is a main characteristic of QUANNs, namely, the conditional unitary gates implemented in neural computational circuits are dependent upon the topology and links between the different artificial neurons.
\nFor the simplest algorithms, we can use just a few registers and connections, which means that each quantum device can simulate different QUANNs, within the restrictions of their respective topologies.
\nFor a QUANN using all the quantum registers in the device, the types of algorithms are limited by the device structure, which can only implement a specific neural network topology and link direction.
\nIn Figures 1 and 2, we, respectively, show the connectivity structure of the “IBM Q 5 Tenerife” and the “IBM Q 16 Melbourne” devices.
\nIBM Q 5 Tenerife (ibmqx4) connectivity structure.
IBM Q 16 Melbourne (ibmq_16_melbourne) connectivity structure.
Having introduced the two devices, we now exemplify the theoretical and experimental implementation of a QUANN, on these devices, for a basic problem: the XOR Boolean function representation. This is a relevant example in the artificial neural network (ANN) literature, since the classical feedforward ANN needs a hidden layer to solve this problem, while its quantum counterpart does not [4].
\nNamely, a three-neuron QUANN with two input neurons feeding forward to a single output neuron is capable of representing the XOR function, while, in the classical case, we need an additional hidden layer comprised of two neurons. This is a feature of QUANNs that is generalizable to other Boolean functions as discussed in [4] regarding the computational efficiency of QUANNs over classical ANNs.
\nThe reason for the greater efficiency is linked to entanglement, namely, the output neuron’s firing dynamics can become entangled with the input layer’s firing dynamics by way of the implementation of conditional NOT (CNOT) gates, providing for an example of the importance of entanglement in the efficiency of quantum computation over classical computation, a point that was object of detailed discussion in [4] regarding the relevance of entanglement for quantum neural computational efficiency.
\nThe XOR Boolean function representation problem is such that we want an output neuron to fire when the input neurons’ firing patterns are reversed and to remain nonfiring when the input neurons’ firing patterns are aligned. This means that the neural network’s output follows the XOR truth table with the output neuron firing when the XOR function evaluates to “True” and not firing otherwise.
\nIn this case, as shown in [4], the XOR function representation problem can be solved by a standard quantum feedforward neural network with no hidden layer, by taking advantage of quantum entanglement dynamics.
\nFormally, the quantum circuit, in the forward direction, can be represented by the following chain:
with the gates, respectively, given by:
We begin with all three neurons in a nonfiring dynamics; then, the propagation from input to output (in the forward direction of the computational circuit) yields:
The result in Eq. (25) means that the only probed final alternatives are those where the XOR rule \n
Likewise, back propagation from the output to the input yields the same result, that is, the only responses come from outputs where the XOR rule \n
Replacing Eqs. (25) and (26) in the general Eq. (17) yields, for this quantum circuit, the
That is, the forward and back propagation is such that the
The Figure 3 shows the theoretical results from the above equations, the simulation in the IBM quantum assembly language (QASM) simulator and the experimental implementation on the Tenerife (ibmqx4) and Melbourne (ibmq_16_melbourne) devices.
\nTheoretical and experimental implementation of the XOR representation problem on the QASM simulator, Tenerife and Melbourne devices, with 8192 shots.
The QASM simulation expresses, as expected, the basic random results from the repeated experiments, which is associated with the fundamental stochastic dynamics underlying quantum processing; however, the simulator results agree with the theoretical results, so that the basic XOR computation holds, that is, in each case, the output neuron exhibits the firing pattern that is consistent with the XOR rule.
\nIn the case of experiments, the XOR rule is predominant, that is, the dominant frequencies are those consistent with the circuit; there are, however, also a few residual cases that deviate from the XOR rule, all with low relative frequencies. These deviations are to be expected on the actual physical devices. For the Tenerife device, the relative frequency of cases that follow the XOR rule is 0.857; for the Melbourne device, this relative frequency is 0.835.
\nOne of the main problems in physical implementation of quantum computation is the presence of errors. Indeed, the equations are derived for an isolated circuit so that the only
Of course, if Bohm’s conjecture regarding the subquantum dynamics [9, 10] is right, then, even for a sufficiently isolated circuit, small deviations coming from the subquantum level may be present and lead to
While we cannot rule out Bohm’s subquantum hypothesis, we cannot also confirm it for now, since one never has a completely isolated circuit, and both conjectural lines (Bohmian and others) agree that some deviations on physical devices will always be present due to the environment.
\nThe differences between the two conjectural lines, for quantum computer science, are worth considering regarding quantum error correction; however, for now, in regard to the technological issue of quantum error correction, we cannot yet make use of Bohm’s conjecture that the quantum probabilities are average quantities and that subquantum fluctuations may introduce small deviations that average out at the quantum level to lead to the main experimental agreement with the theory.
\nHaving provided, through the XOR problem, an example of how quantum neural computation can be run experimentally on IBM’s quantum devices, we now address artificial intelligence (AI) applications; we are interested in the theoretical and experimental implementation of a form of reinforcement learning using QUANNs, namely the quantum neural reinforcement learning (QNRL) and its connection to quantum robotics and quantum adaptive computation.
\nQuantum robotics involves the need for the development of quantum adaptive algorithms that allow the robot to process alternatives and select appropriate actions using quantum rules [6, 13, 14, 15], that is, to incorporate decisions in quantum AI. In this context, there are two major types of artificial agents that one may consider:
classical agents that implement classical actions but whose cognitive substrate is quantum computational;
quantum agents that implement quantum operations on a quantum target.
The first type of agent is addressed as a classical robot dealing with problems at a classical level but whose computational substrate is run via cloud access on a quantum computer, thus, pointing toward a possible future where quantum computation is incorporated on different robotic systems by way of the internet of things and cloud-based access to quantum devices.
\nThe second type of agents corresponds to
This second type of agents forms the basis for AI solutions aiming at intelligent quantum computing systems with application in quantum internet technologies and, also, possible quantum adaptive error correction.
\nThis latter point (quantum adaptive error correction) must draw specifically on the empirical implementation in physical devices, since it is this implementation that may ultimately test the best adaptive algorithms for quantum error correction. A basic direction, in this case, regards
We do not address this last point here, but rather illustrate the implementation of the first type of agent in the context of an adaptive computation of a classical gamble, namely, optimal action selection in a classical gambling problem through quantum neural reinforcement learning (QNRL).
\nIn this case, the artificial agent is dealing with a classical problem and implementing its decision processing on a QUANN, namely, the agent has an action set described by \n
Now, given each alternative action, the agent is offered a classical gamble on a measurable space \n
Now, for each action genetic code \n
The goal for the agent is to select the action that maximizes this conditional expected reward, that is:
To solve the optimization problem in Eq. (29), we use a variant of QNRL, which applies modular networked learning [16], in the sense that, instead of a single neural network for a single problem, we expand the cognitive architecture and work with a modular system of neural networks.
\nModular networked learning (MNL) was addressed in [16] and applied to financial market prediction, where, instead of a single problem and a single target, one uses an expanded cognitive architecture to work on multiple targets with a module assigned to each target and possible links between the modules used to map links between subproblems of a more complex problem.
\nFor modular neural networks, the resulting cognitive architecture resembles an artificial brain with specialized “brain regions” devoted to different tasks and connections between different neural modules corresponding to connections between different brain regions. In the present case, the agent’s “artificial brain” (as shown in Figure 4) is comprised of three “brain regions” connected with each other for a specific functionality, where the first module (first brain region) corresponds to the action exploration region, the second module (second brain region) corresponds to the reward processing region, and the third module to the decision region.
\nModular structure for the reward maximization problem.
The connections between the modules follow the hierarchical process associated with the necessary quantum reinforcement learning for each action, Figure 4 expresses this relation. The reinforcement learning, in this case, is a form of quantum search, implemented on the above modular structure, that proceeds in two stages: the exploration stage and the exploitation stage.
\nIn the exploration stage, the agent’s first brain region, taking advantage of quantum superposition, explores with equal weights, in parallel, each alternative initial action and the second brain region processes the conditional expected rewards; this last processing is based on optimizing quantum circuits [6], where the unitary operator for the second brain region incorporates the optimization itself.
\nThe second brain region will work as a form of oracle in the remaining adaptive computation and allows for the agent’s artificial brain to implement an optimal expected reward-seeking dynamics.
\nNow, in the second phase of the exploration stage, the synaptic connections from the first to the second brain region are activated, leading to a quantum entangled dynamics between the two brain regions, where the first region acts as the control (input layer) and the second as the target (output layer).
\nThus, at the end of the exploration stage, the first two brain regions exhibit an entangled dynamics. This is a basic point of quantum strategic cognition, in the sense that the processing of the alternative courses of action is not localized in a specific neuron or neurons, but rather it leads to quantum correlations between different brain regions; these connections allow the artificial brain to efficiently select the best course of action, from the evaluation of the alternatives and rewards.
\nIn the exploitation stage, the synaptic connections from the first brain region (the action exploration region) to the third brain region (the decision region) are activated first, so that the decision region is first processing the explored alternative actions, becoming entangled with the action exploration region; then, the synaptic connections between the reward processing region and the decision region are activated for the conditional expected reward processing by the decision module. In this way, the decision module makes the transition for the optimal action, consulting the “oracle” (which is the reward processing module) only once.
\nThe artificial brain thus takes advantage of quantum entanglement in order to adaptively output the optimal action. Formalizing this dynamics, the artificial brain is initialized in a nonfiring probe and response dynamics so that the initial density is:
Now, we denote by \n
Using this notation, the first phase of the exploration stage is given by the unitary operator:
The operator incorporates the optimization dynamics into the conditional quantum gates’ parameters themselves. Since we have:
after the first phase of the of exploration stage, the resulting density is given by:
Thus, the neural field is probing, for the first brain region, each alternative neural pattern (each alternative action) with equal weight, the response dynamics also comes, for the first brain region, from each alternative neural pattern with equal weight, which means that the
On the other hand, for the second brain region, the neural field exhibits a reward-seeking dynamics that is adaptive with respect to the optimal action; that is, the probing dynamics is directed toward the optimal action and the response dynamics also comes from the optimal action, so that, due to the adaptive unitary propagation, the second brain region is projecting over the optimum value, and this is the only
The third brain region still has a projective dynamics toward the nonfiring neural activity.
\nNow, for the second phase of the exploration stage, we have the operator:
which leads to the density after the second phase of the exploration stage:
Thus, after the second phase, the first and second brain regions exhibit an entangled probe and response dynamics, where the neural field, for second brain region, is effectively computing both the rewards and the explored actions.
\nNext comes the exploitation stage with the neural processing for the decision module (the third brain region).
\nThe first step of the exploitation stage is the processing of the initially explored actions, by way of the operator:
which leads to the density:
That is, the probe and response dynamics for the third brain region are correlated and coincident with the probe and response dynamics for the first brain region, so that the third brain region is effectively computing the initially explored actions.
\nNow, the second step for the third brain region results from the activation of the synaptic links with the second brain region, leading to the conditional unitary operator:
Under this operator, we get the final density:
Since we have the Boolean equality \n
The third brain region’s computation takes advantage of the entangled dynamics between the first and second brain regions to learn the optimal action. For the final density, while the first and second brain regions exhibit an entangled probe and response dynamics, the third brain region is always projecting over the optimum.
\nIt is important to stress how QNRL takes advantage of quantum entanglement such that the neural field for the third brain region followed each alternative action and then the reward processing dynamics to find the optimum in all these alternative paths, so that the optimal action is always followed by the agent.
\nAs an example of the above problem, let us consider the case where we the reward set is \n
Device | \nAction | \n|
---|---|---|
0 | \n1 | \n|
QASM | \n0 | \n1 | \n
Tenerife | \n0.222 | \n0.778 | \n
Melbourne | \n0.353 | \n0.647 | \n
Results for two alternative actions using the QASM simulator, the Tenerife device (ibmqx4) and the Melbourne device (ibmq_16_melbourne); in each case, 8192 shots were used, with \n
As expected, the QASM simulator always selects the action \n
Device | \nAction | \n|
---|---|---|
0 | \n1 | \n|
QASM | \n1 | \n0 | \n
Tenerife | \n0.857 | \n0.143 | \n
Melbourne | \n0.814 | \n0.186 | \n
Results for two actions using the QASM simulator, the Tenerife device (ibmqx4) and the Melbourne device (ibmq_16_melbourne); in each case, 8192 shots were used, with \n
In Figure 5, we show the Melbourne device’s results4 when we have four actions for the same rewards profile, and the probabilities are \n
Results for four actions using the Melbourne device (“ibmq_16_melbourne”), with 8192 shots used, and probability profiles given by:
In this case, if we run the experiment on the QASM backend, with 8192 shots, we get the action encoded by the string 11 with relative frequency equal to 1, which is the optimal action. If we run the experiment with the same number of shots on the Melbourne device, then, as shown in Figure 5, the output 11 is still the dominant action, however, with a proportion of 0.370, the second dominant action being non-residual and with a value of 0.309 occurs for the output 10.
\nTherefore, the first
The above algorithm was implemented using Qiskit and Python’s Object Oriented Programming (OOP); the code, shown in the appendix, exemplifies how OOP can be integrated with quantum computation for implementing quantum AI on any terminal, due to the cloud access to IBM’s quantum resources, constituting an example of Quantum Object Oriented Programming (QOOP) using Qiskit.
\nThe code defines the class “Agent” with an attribute that is the quantum neural network; in this case, the attribute will be assigned a quantum circuit with the required quantum and classical registers.
\nThere are two methods that any instance of the class Agent must be able to implement: the first method manages the cloud access to IBM’s resources, the second method implements the action selection and the quantum algorithm.
\nThe inputs for the first method are the accounts to be loaded, for the classical computer to be able to access quantum computer via the cloud service, and the backend code, which, by default, is set to the QASM simulator but can be changed to any of the devices. The method returns the backend to be used.
\nThe second method, for the action selection, has a structure that is specific to the problem in question; that is, the agent is offered a set of rewards and probabilities associated with each alternative action, and must choose the action that maximizes the conditional expected reward.
\nThus, the probabilities are known to the agent and form part of the gamble that is offered to it; therefore, we are dealing with a decision problem under risk, and wish to address how the agent’s QUANN can exhibit an adaptive computation with respect to this problem.
\nWhile, in the above equations, the adaptive nature of the quantum neural circuit was introduced in the unitary operator’s parameters themselves, the Python code for the method must use the gamble’s inputs to make the quantum circuit adaptive; that is, the method must be such that the agent designs its own cognitive architecture (updating its qnnet attribute) and quantum circuit using the inputs to the method, and, then, the agent must implement the cloud-based access to run, in IBM’s quantum computers, the corresponding quantum algorithm.
\nThe inputs for the method are, then, given by a list of probability distributions, where each line corresponds to a different probability gamble profile associated with each action, for instance, in the case of Table 1, the distributions are given by ([0.6, 0.4],[0.4, 0.6]). In the case of Tables 1 and 2 and Figure 5, the rewards list is [−1, 1].
\nThe other two inputs for the method are the backend used which allows the agent that is instantiated in a classical computer to access via cloud the quantum computer, using the backend code (backend_used) and repeatedly running the algorithm on the respective device for a number of shots (num_shots).
\nThe choose_action method’s step zero is the extraction of the expected values and of the corresponding parameters for the adaptive gates, namely, the expected values array associated with each action is extracted by the agent using the Python library NumPy’s dot product applied to the distributions and rewards lists.
\nThe number of actions and dimension \n
Now, the next step is to set up the QUANN, including the three modules, the classical registers for the measurement of the final actions to be chosen and updating the agent’s qnnet attribute, assigning it the corresponding Qiskit’s quantum circuit object.
\nThe last step implements the QNRL algorithm, following the inter-module links as per the main equations introduced in this section, and defines the quantum measurement for the decision module, executing the algorithm on the backend (taking advantage of the cloud access) and plotting the histogram to extract the main experimental relative frequencies obtained from the repeated experiments (the number of shots).
\nCloud-based access to quantum computers opens up a major point: the empirical testing of algorithms and the implementation of computer programs in a quantum computational substrate has become feasible.
\nThe IBM Q Experience constitutes an example of how a programmer can use Python programming language and IBM’s Python Qiskit package for building programs that use quantum computation, limited only by the specific device resources, namely the number of quantum registers available.
\nFor quantum AI and machine learning, this provides a way to effectively bring the algorithms from the theoretical level to the test level, allowing one to
We showed how one can address IBM’s superconducting
We exemplified how basic Boolean functions’ representation, in this case the XOR function, can be implemented on a (physical) quantum computer using the cloud access to Tenerife and Melbourne devices and compared the experimental results with the theoretical derivation; a relevant point of this is that we only needed three quantum registers and no hidden layer to solve the XOR problem, a point already raised about this function and generalized to other functions in [4], regarding the theoretical efficiency of QUANNs.
We addressed how a form of quantum adaptive computation, incorporating a reward-seeking behavior and a variant of QNRL, can be implemented, in the context of quantum robotics and AI, on different quantum devices.
The three main points above help strengthen two core arguments: the first is that quantum machine learning can now be tested on actual quantum computers, making it feasible to empirically test the algorithms; the second is that, in the near future, with further advancements in quantum computation and quantum hardware, quantum adaptive computation may be implemented on actual robots with a quantum cognitive architecture that is based on cloud access to a quantum computer.
\nThe present work addresses both core arguments by exemplifying how a form of QNRL can be employed to implement quantum adaptive computation on a physical QUANN with cloud-based access, employing QOOP and addressing, experimentally, a decision under risk problem.
\n# Import NumPy and Qiskit’s main functionalities
\nimport numpy as np
\nfrom qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit
\nfrom qiskit import execute
\nfrom qiskit import IBMQ
\nfrom qiskit.tools.visualization import plot_histogram
\nclass Agent:
\ndef __init__(self, qnnet):
\nself.qnnet = qnnet # agent’s Quantum Neural Network
\ndef get_backend(self,
\nload_accounts = True, # if accounts are to be loaded
\nbackend_code = ‘ibmq_qasm_simulator’ # backend code
\n):
\n# Load IBM account if needed
\nif load_accounts == True:
\nIBMQ.load_accounts()
\n# Get the backend to use in the computation
\nbackend_used = IBMQ.get_backend(backend_code)
\n# If one is not using the QASM simulator get the backend status
\nif backend_code! = ‘ibmq_qasm_simulator’:
\nprint(backend_used.status())
\n# Return the backend used
\nreturn backend_used
\ndef choose_action(self,
\ndistributions, # probability distributions
\nrewards, # reward system
\nbackend_used, # backed to be used
\nnum_shots): # number of shots to run in quantum computer
\n# Step 0: get the expected values and unitary parameters:
\n# Get the expected values
\nexpected_values = np.dot(distributions,rewards)
\n# Get the number of actions involved
\nnum_actions = len(expected_values)
\n# Get the base number that we will need for the network size
\ndim = int.(np.log2(num_actions))
\n# Get the parameters for the adaptive gate
\nmaxstring = np.binary_repr(np.argmax(expected_values), width = dim)
\n# Step 1: Setup the Quantum Artificial Neural Network:
\n# Get the number of quantum registers
\nq = QuantumRegister(3*dim)
\n# Get the number of classical registers
\nc = ClassicalRegister(dim)
\n# Setup the quantum neural network
\nself.qnnet = QuantumCircuit(q, c)
\n# Step 2: Implement the Reinforcement Learning Algorithm:
\n# Exploration Stage
\nfor i in range(0,dim):
\nself.qnnet.h(q[i])
\nfor j in range(0,dim):
\nself.qnnet.u3(float(maxstring[j])*np.pi,0,0,q[dim+j])
\nfor k in range(0,dim):
\nself.qnnet.cx(q[k],q[dim+k])
\n# Exploitation Stage
\nfor l in range(0,dim):
\nself.qnnet.cx(q[l],q[2*dim+l])
\nself.qnnet.cx(q[dim+l],q[2*dim+l])
\n# Quantum Measurement
\nfor m in range(0,dim):
\nself.qnnet.measure(q[2*dim+m], c[dim-1-m])
\n# Execute the algorithm on the backend
\njob_exp = execute(self.qnnet, backend = backend_used, shots = num_shots)
\n# Plot the histogram
\nplot_histogram(job_exp.result().get_counts(self.qnnet))
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
\n\n\n\nWith the purpose of protecting our Authors' copyright and the transparent reuse of Open Access content, IntechOpen has developed an Attribution Policy for works published under Creative Commons licenses.
\n\n\n\nIntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
\n\nIn line with publication ethics practices recommended by COPE, ICMJE, and other similar organizations, IntechOpen's contributing Authors, Academic Editors, and Peer Reviewers are required to declare fully all possible conflicts of interest.
\n\n\n\nIntechOpen's Authorship Policy is based on ICMJE criteria for authorship. In order to be identified as an Author, the following requirements must be met:
\n\nAll scientific works are subject to Peer Review prior to publishing. IntechOpen is a member of the Committee on Publication Ethics (COPE) and all participating referees and Academic Editors are expected to review submitted scientific works in line with the COPE Ethical Guidelines for Peer Reviewers where applicable.
\n\n\n\nThe Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\n\n\n\nTo identify instances of fraud and misconduct during the publishing process, IntechOpen implements a robust policy governing such occurrences. In line with our general commitment to openness, and in order to maintain the highest scientific standards, we are committed to transparency about our editorial policy regarding retractions and corrections.
\n\n\n\nWhen faced with potential misconduct, IntechOpen accepts its responsibility to maintain the integrity of the academic record. For particularly complex cases, IntechOpen might ask for the assistance of formal industry bodies or seek advice from an appropriate team of advisors.
\n\nIntechOpen's advisors are professionals and scholars with broad knowledge and understanding of different aspects of the scientific publishing process: editorial, authorship, and reviewing roles; publication ethics, copyright, and general legal issues; as well as bibliographic and technical standards.
\n\nIn order to provide us with unbiased insights, without compromising the privacy of third parties, IntechOpen presents problematic cases to its advisors in an anonymized format.
\n\nIntechOpen publishes books in the English language. If you are interested in the translation of Book Chapters, please check IntechOpen's Translation Policy.
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\n\nOnline First Chapters are considered published on the day they are posted and are citable from that date.
\n\nChapters will remain listed as Online First until the final versions of the books are published online. Following publication of the full monograph, Chapters will be redirected from the Online First version and will be available only through the final link of the official published page.
\n\nYou are invited to download, use, reproduce, make derivative works of, display, distribute and cite the Online First works. You can find "How to Cite and Reference" by following the link at the end of each online book chapter. Please be aware that it is possible that further editing and changes might be made before the final release of the book.
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\n\n\n\n\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. In the Engineering side, Digital Signal Processing, Computer Architecture, Electronics Devices, Digital Filtering and Engineering Management.\nApart from his Academic Interest and activities he loves sport especially, Cricket, Football, Snooker and Squash. He plays cricket for Esbjerg city in the second division team as an opener wicket keeper batsman. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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