Unė G. Būtaitė, Hlib Kupianskyi, T. Čižmár, D. Phillips
When light propagates through multimode optical fibres (MMFs), the spatial information it carries is scrambled. Wavefront shaping reverses this scrambling, typically one spatial mode at a time—enabling deployment of MMFs as ultrathin microendoscopes. Here, we go beyond sequential wavefront shaping by showing how to simultaneously unscramble all spatial modes emerging from an MMF in parallel. We introduce a passive multiple-scattering element—crafted through the process of inverse design—that is complementary to an MMF and undoes its optical effects. This “optical inverter” makes possible single-shot widefield imaging and super-resolution imaging through MMFs. Our design consists of a cascade of diffractive elements, and can be understood from the perspective of both multi-plane light conversion, and as a physically inspired diffractive neural network. This physical architecture outperforms state-of-the-art electronic neural networks tasked with unscrambling light, as it preserves the phase and coherence information of optical signals flowing through it. We show, in numerical simulations, how to efficiently sort and tune the relative phase of up to ~400 step-index fibre modes, reforming incoherent images of scenes at arbitrary distances from the fibre facet. Our optical inverter can dynamically adapt to see through experimentally realistic flexible fibres—made possible by moulding optical memory effects into its design. The scheme is based on current fabrication technology so could be realised in the near future. Beyond imaging, these concepts open up a range of new avenues for optical multiplexing, communications, and computation in the realms of classical and quantum photonics.
{"title":"How to Build the “Optical Inverse” of a Multimode Fibre","authors":"Unė G. Būtaitė, Hlib Kupianskyi, T. Čižmár, D. Phillips","doi":"10.34133/2022/9816026","DOIUrl":"https://doi.org/10.34133/2022/9816026","url":null,"abstract":"When light propagates through multimode optical fibres (MMFs), the spatial information it carries is scrambled. Wavefront shaping reverses this scrambling, typically one spatial mode at a time—enabling deployment of MMFs as ultrathin microendoscopes. Here, we go beyond sequential wavefront shaping by showing how to simultaneously unscramble all spatial modes emerging from an MMF in parallel. We introduce a passive multiple-scattering element—crafted through the process of inverse design—that is complementary to an MMF and undoes its optical effects. This “optical inverter” makes possible single-shot widefield imaging and super-resolution imaging through MMFs. Our design consists of a cascade of diffractive elements, and can be understood from the perspective of both multi-plane light conversion, and as a physically inspired diffractive neural network. This physical architecture outperforms state-of-the-art electronic neural networks tasked with unscrambling light, as it preserves the phase and coherence information of optical signals flowing through it. We show, in numerical simulations, how to efficiently sort and tune the relative phase of up to ~400 step-index fibre modes, reforming incoherent images of scenes at arbitrary distances from the fibre facet. Our optical inverter can dynamically adapt to see through experimentally realistic flexible fibres—made possible by moulding optical memory effects into its design. The scheme is based on current fabrication technology so could be realised in the near future. Beyond imaging, these concepts open up a range of new avenues for optical multiplexing, communications, and computation in the realms of classical and quantum photonics.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"16 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72828496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.34133/icomputing.0003
R. Bartels, Gabe Murray, Jeff Field, J. Squier
A high-speed super-resolution computational imaging technique is introduced on the basis of classical and quantum correlation functions obtained from photon counts collected from quantum emitters illuminated by spatiotemporally structured illumination. The structured illumination is delocalized—allowing the selective excitation of separate groups of emitters as the modulation of the illumination light advances. A recorded set of photon counts contains rich quantum and classical information. By processing photon counts, multiple orders of Glauber correlation functions are extracted. Combinations of the normalized Glauber correlation functions convert photon counts into signals of increasing order that contain increasing spatial frequency information. However, the amount of information above the noise floor drops at higher correlation orders, causing a loss of accessible information in the finer spatial frequency content that is contained in the higher-order signals. We demonstrate an efficient and robust computational imaging algorithm to fuse the spatial frequencies from the low-spatial-frequency range that is available in the classical information with the spatial frequency content in the quantum signals. Because of the overlap of low spatial frequency information, the higher signal-to-noise ratio (SNR) information concentrated in the low spatial frequencies stabilizes the lower SNR at higher spatial frequencies in the higher-order quantum signals. Robust performance of this joint fusion of classical and quantum computational single-pixel imaging is demonstrated with marked increases in spatial frequency content, leading to super-resolution imaging, along with much better mean squared errors in the reconstructed images.
{"title":"Super-Resolution Imaging by Computationally Fusing Quantum and Classical Optical Information","authors":"R. Bartels, Gabe Murray, Jeff Field, J. Squier","doi":"10.34133/icomputing.0003","DOIUrl":"https://doi.org/10.34133/icomputing.0003","url":null,"abstract":"A high-speed super-resolution computational imaging technique is introduced on the basis of classical and quantum correlation functions obtained from photon counts collected from quantum emitters illuminated by spatiotemporally structured illumination. The structured illumination is delocalized—allowing the selective excitation of separate groups of emitters as the modulation of the illumination light advances. A recorded set of photon counts contains rich quantum and classical information. By processing photon counts, multiple orders of Glauber correlation functions are extracted. Combinations of the normalized Glauber correlation functions convert photon counts into signals of increasing order that contain increasing spatial frequency information. However, the amount of information above the noise floor drops at higher correlation orders, causing a loss of accessible information in the finer spatial frequency content that is contained in the higher-order signals. We demonstrate an efficient and robust computational imaging algorithm to fuse the spatial frequencies from the low-spatial-frequency range that is available in the classical information with the spatial frequency content in the quantum signals. Because of the overlap of low spatial frequency information, the higher signal-to-noise ratio (SNR) information concentrated in the low spatial frequencies stabilizes the lower SNR at higher spatial frequencies in the higher-order quantum signals. Robust performance of this joint fusion of classical and quantum computational single-pixel imaging is demonstrated with marked increases in spatial frequency content, leading to super-resolution imaging, along with much better mean squared errors in the reconstructed images.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"10 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79171505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1108/IJICC-08-2021-0167
B. Jagan, S. Rao
{"title":"Measure of nonlinearity for underwater target tracking using hull-mounted sensor","authors":"B. Jagan, S. Rao","doi":"10.1108/IJICC-08-2021-0167","DOIUrl":"https://doi.org/10.1108/IJICC-08-2021-0167","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"15 1","pages":"333-344"},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62693028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.34133/icomputing.0001
Akhil Kallepalli, L. Viani, D. Stellinga, E. Rotunno, R. Bowman, G. Gibson, Ming-jie Sun, P. Rosi, S. Frabboni, R. Balboni, A. Migliori, V. Grillo, M. Padgett
Solving challenges of enhanced imaging (resolution or speed) is a continuously changing frontier of research. Within this sphere, ghost imaging (and the closely related single-pixel imaging) has evolved as an alternative to focal plane detector arrays owing to advances in detectors and/or modulation devices. The interest in these techniques is due to their robustness to varied sets of patterns and applicability to a broad range of wavelengths and compatibility with compressive sensing. To achieve a better control of illumination strategies, modulators of many kinds have long been available in the optical regime. However, analogous technology to control of phase and amplitude of electron beams does not exist. We approach this electron microscopy challenge from an optics perspective, with a novel approach to imaging with non-orthogonal pattern sets using ghost imaging. Assessed first in the optical regime and subsequently in electron microscopy, we present a methodology that is applicable at different spectral regions and robust to non-orthogonality. The distributed illumination pattern sets also result in a reduced peak intensity, thereby potentially reducing damage of samples during imaging. This imaging approach is potentially translatable beyond both regimes explored here, as a single-element detector system.
{"title":"Challenging Point Scanning across Electron Microscopy and Optical Imaging using Computational Imaging","authors":"Akhil Kallepalli, L. Viani, D. Stellinga, E. Rotunno, R. Bowman, G. Gibson, Ming-jie Sun, P. Rosi, S. Frabboni, R. Balboni, A. Migliori, V. Grillo, M. Padgett","doi":"10.34133/icomputing.0001","DOIUrl":"https://doi.org/10.34133/icomputing.0001","url":null,"abstract":"Solving challenges of enhanced imaging (resolution or speed) is a continuously changing frontier of research. Within this sphere, ghost imaging (and the closely related single-pixel imaging) has evolved as an alternative to focal plane detector arrays owing to advances in detectors and/or modulation devices. The interest in these techniques is due to their robustness to varied sets of patterns and applicability to a broad range of wavelengths and compatibility with compressive sensing. To achieve a better control of illumination strategies, modulators of many kinds have long been available in the optical regime. However, analogous technology to control of phase and amplitude of electron beams does not exist. We approach this electron microscopy challenge from an optics perspective, with a novel approach to imaging with non-orthogonal pattern sets using ghost imaging. Assessed first in the optical regime and subsequently in electron microscopy, we present a methodology that is applicable at different spectral regions and robust to non-orthogonality. The distributed illumination pattern sets also result in a reduced peak intensity, thereby potentially reducing damage of samples during imaging. This imaging approach is potentially translatable beyond both regimes explored here, as a single-element detector system.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81089778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-10467-1
{"title":"Intelligent Computing: Proceedings of the 2022 Computing Conference, Volume 3","authors":"","doi":"10.1007/978-3-031-10467-1","DOIUrl":"https://doi.org/10.1007/978-3-031-10467-1","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"48 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81376229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.34133/icomputing.0004
Ofri Goldenberg, Boris Ferdman, E. Nehme, Yael Shalev Ezra, Y. Shechtman
Measuring the 3-dimensional (3D) distance between 2 spots is a common task in microscopy, because it holds information on the degree of colocalization in a variety of biological systems. Often, the 2 spots are labeled with 2 different colors, as each spot represents a different labeled entity. In computational microscopy, neural networks have been employed together with point spread function (PSF) engineering for various imaging challenges, specifically for localization microscopy. This combination enables “end-to-end” design of the optical system’s hardware and software, which is learned simultaneously, optimizing both the image acquisition and reconstruction together. In this work, we employ such a strategy for the task of direct measurement of the 3D distance between 2 emitters, labeled with differently colored fluorescent labels, in a single shot, on a single optical channel. Specifically, we use end-to-end learning to design an optimal wavelength-dependent phase mask that yields an image that is most informative with regards to the 3D distance between the 2 spots, followed by an analyzing net to decode this distance. We utilize the fact that only the distance between the 2 spots is of interest, rather than their absolute positions; importantly, the use of 2 colors, instead of 1, inherently enables subdiffraction distance estimation. We demonstrate our approach experimentally by distance measurement between pairs of fluorescent beads, as well as between 2 fluorescently tagged DNA loci in yeast cells. Our results represent an appealing demonstration of the usefulness of neural nets in task-specific microscopy design and in optical system optimization in general.
{"title":"Learning Optimal Multicolor PSF Design for 3D Pairwise Distance Estimation","authors":"Ofri Goldenberg, Boris Ferdman, E. Nehme, Yael Shalev Ezra, Y. Shechtman","doi":"10.34133/icomputing.0004","DOIUrl":"https://doi.org/10.34133/icomputing.0004","url":null,"abstract":"Measuring the 3-dimensional (3D) distance between 2 spots is a common task in microscopy, because it holds information on the degree of colocalization in a variety of biological systems. Often, the 2 spots are labeled with 2 different colors, as each spot represents a different labeled entity. In computational microscopy, neural networks have been employed together with point spread function (PSF) engineering for various imaging challenges, specifically for localization microscopy. This combination enables “end-to-end” design of the optical system’s hardware and software, which is learned simultaneously, optimizing both the image acquisition and reconstruction together. In this work, we employ such a strategy for the task of direct measurement of the 3D distance between 2 emitters, labeled with differently colored fluorescent labels, in a single shot, on a single optical channel. Specifically, we use end-to-end learning to design an optimal wavelength-dependent phase mask that yields an image that is most informative with regards to the 3D distance between the 2 spots, followed by an analyzing net to decode this distance. We utilize the fact that only the distance between the 2 spots is of interest, rather than their absolute positions; importantly, the use of 2 colors, instead of 1, inherently enables subdiffraction distance estimation. We demonstrate our approach experimentally by distance measurement between pairs of fluorescent beads, as well as between 2 fluorescently tagged DNA loci in yeast cells. Our results represent an appealing demonstration of the usefulness of neural nets in task-specific microscopy design and in optical system optimization in general.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84524504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1108/IJICC-10-2021-0226
S. Gafoor, T. Padma
{"title":"Intelligent approach of score-based artificial fish swarm algorithm (SAFSA) for Parkinson's disease diagnosis","authors":"S. Gafoor, T. Padma","doi":"10.1108/IJICC-10-2021-0226","DOIUrl":"https://doi.org/10.1108/IJICC-10-2021-0226","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"15 1","pages":"540-561"},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62693177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1108/IJICC-06-2021-0105
Guduru Naga Divya, S. K. Rao
{"title":"Prevalence of shifted Rayleigh filter for passive surveillance in underwater","authors":"Guduru Naga Divya, S. K. Rao","doi":"10.1108/IJICC-06-2021-0105","DOIUrl":"https://doi.org/10.1108/IJICC-06-2021-0105","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"15 1","pages":"110-123"},"PeriodicalIF":4.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62692964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-28DOI: 10.34133/icomputing.0037
Qi Gao, Gavin O. Jones, M. Sugawara, Takao Kobayashi, Hiroki Yamashita, Hideaki Kawaguchi, Shu Tanaka, Naoki Yamamoto
This study describes a hybrid quantum-classical computational approach for designing synthesizable deuterated $Alq_3$ emitters possessing desirable emission quantum efficiencies (QEs). This design process has been performed on the tris(8-hydroxyquinolinato) ligands typically bound to aluminum in $Alq_3$. It involves a multi-pronged approach which first utilizes classical quantum chemistry to predict the emission QEs of the $Alq_3$ ligands. These initial results were then used as a machine learning dataset for a factorization machine-based model which was applied to construct an Ising Hamiltonian to predict emission quantum efficiencies on a classical computer. We show that such a factorization machine-based approach can yield accurate property predictions for all 64 deuterated $Alq_3$ emitters with 13 training values. Moreover, another Ising Hamiltonian could be constructed by including synthetic constraints which could be used to perform optimizations on a quantum simulator and device using the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA) to discover a molecule possessing the optimal QE and synthetic cost. We observe that both VQE and QAOA calculations can predict the optimal molecule with greater than 0.95 probability on quantum simulators. These probabilities decrease to 0.83 and 0.075 for simulations with VQE and QAOA, respectively, on a quantum device, but these can be improved to 0.90 and 0.084 by mitigating readout error. Application of a binary search routine on quantum devices improves these results to a probability of 0.97 for simulations involving VQE and QAOA.
{"title":"Quantum-Classical Computational Molecular Design of Deuterated High-Efficiency OLED Emitters","authors":"Qi Gao, Gavin O. Jones, M. Sugawara, Takao Kobayashi, Hiroki Yamashita, Hideaki Kawaguchi, Shu Tanaka, Naoki Yamamoto","doi":"10.34133/icomputing.0037","DOIUrl":"https://doi.org/10.34133/icomputing.0037","url":null,"abstract":"This study describes a hybrid quantum-classical computational approach for designing synthesizable deuterated $Alq_3$ emitters possessing desirable emission quantum efficiencies (QEs). This design process has been performed on the tris(8-hydroxyquinolinato) ligands typically bound to aluminum in $Alq_3$. It involves a multi-pronged approach which first utilizes classical quantum chemistry to predict the emission QEs of the $Alq_3$ ligands. These initial results were then used as a machine learning dataset for a factorization machine-based model which was applied to construct an Ising Hamiltonian to predict emission quantum efficiencies on a classical computer. We show that such a factorization machine-based approach can yield accurate property predictions for all 64 deuterated $Alq_3$ emitters with 13 training values. Moreover, another Ising Hamiltonian could be constructed by including synthetic constraints which could be used to perform optimizations on a quantum simulator and device using the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA) to discover a molecule possessing the optimal QE and synthetic cost. We observe that both VQE and QAOA calculations can predict the optimal molecule with greater than 0.95 probability on quantum simulators. These probabilities decrease to 0.83 and 0.075 for simulations with VQE and QAOA, respectively, on a quantum device, but these can be improved to 0.90 and 0.084 by mitigating readout error. Application of a binary search routine on quantum devices improves these results to a probability of 0.97 for simulations involving VQE and QAOA.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"20 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74262703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-20DOI: 10.34133/icomputing.0051
J. Seong, J. Bae
Entanglement, a direct consequence of elementary quantum gates such as controlled-NOT or Toffoli gates, is a key resource that leads to quantum advantages. In this work, we establish the framework of certifying entanglement generation in cloud-based quantum computing services. Namely, we present the construction of quantum circuits that certify entanglement generation in a circuit-based quantum computing model. The framework relaxes the assumption of the so-called qubit allocation, which is the step in a cloud service to relate physical qubits in hardware to a circuit proposed by a user. Consequently, the certification is valid no matter how unsuccessful qubit allocations may be in cloud computing or how untrustful the service may be in qubit allocations. We then demonstrate the certification of entanglement generation on two and three qubits in the IBMQ and IonQ services. Remarkably, entanglement generation is successfully certified in the IonQ service that does not provide a command of qubit allocations. The capabilities of entanglement generation in the circuits of IBMQ and IonQ are also quantified. We envisage that the proposed framework is applied when cloud-based quantum computing services are exploited for practical computation and information tasks, for which our results would find if it is possible to achieve quantum advantages.
{"title":"Detecting Entanglement-Generating Circuits in Cloud-Based Quantum Computing","authors":"J. Seong, J. Bae","doi":"10.34133/icomputing.0051","DOIUrl":"https://doi.org/10.34133/icomputing.0051","url":null,"abstract":"Entanglement, a direct consequence of elementary quantum gates such as controlled-NOT or Toffoli gates, is a key resource that leads to quantum advantages. In this work, we establish the framework of certifying entanglement generation in cloud-based quantum computing services. Namely, we present the construction of quantum circuits that certify entanglement generation in a circuit-based quantum computing model. The framework relaxes the assumption of the so-called qubit allocation, which is the step in a cloud service to relate physical qubits in hardware to a circuit proposed by a user. Consequently, the certification is valid no matter how unsuccessful qubit allocations may be in cloud computing or how untrustful the service may be in qubit allocations. We then demonstrate the certification of entanglement generation on two and three qubits in the IBMQ and IonQ services. Remarkably, entanglement generation is successfully certified in the IonQ service that does not provide a command of qubit allocations. The capabilities of entanglement generation in the circuits of IBMQ and IonQ are also quantified. We envisage that the proposed framework is applied when cloud-based quantum computing services are exploited for practical computation and information tasks, for which our results would find if it is possible to achieve quantum advantages.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"31 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82050526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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