Pub Date : 2024-03-16DOI: 10.1109/TQE.2024.3402085
Willers Yang;Patrick Rall
In superconducting architectures, limited connectivity remains a significant challenge for the synthesis and compilation of quantum circuits. We consider models of entanglement-assisted computation where long-range operations are achieved through injections of large Greenberger–Horne–Zeilinger (GHZ) states. These are prepared using ancillary qubits acting as an “entanglement bus,” unlocking global operation primitives such as multiqubit Pauli rotations and fan-out gates. We derive bounds on the circuit size for several well-studied problems, such as CZ circuit, CX circuit, and Clifford circuit synthesis. In particular, in an architecture using one such entanglement bus, we give a synthesis scheme for arbitrary Clifford operations requiring at most $2n+1$ layers of entangled state injections, which can be computed classically in $O(n^{3})$ time. In a square-lattice architecture with two entanglement buses, we show that a graph state can be synthesized using at most $lceil frac{1}{2}nrceil +1$ layers of GHZ state injections, and Clifford operations require only $lceil frac{3}{2} n rceil + O(sqrt{n})$ layers of GHZ state injections.
{"title":"Harnessing the Power of Long-Range Entanglement for Clifford Circuit Synthesis","authors":"Willers Yang;Patrick Rall","doi":"10.1109/TQE.2024.3402085","DOIUrl":"https://doi.org/10.1109/TQE.2024.3402085","url":null,"abstract":"In superconducting architectures, limited connectivity remains a significant challenge for the synthesis and compilation of quantum circuits. We consider models of entanglement-assisted computation where long-range operations are achieved through injections of large Greenberger–Horne–Zeilinger (GHZ) states. These are prepared using ancillary qubits acting as an “entanglement bus,” unlocking global operation primitives such as multiqubit Pauli rotations and fan-out gates. We derive bounds on the circuit size for several well-studied problems, such as CZ circuit, CX circuit, and Clifford circuit synthesis. In particular, in an architecture using one such entanglement bus, we give a synthesis scheme for arbitrary Clifford operations requiring at most \u0000<inline-formula><tex-math>$2n+1$</tex-math></inline-formula>\u0000 layers of entangled state injections, which can be computed classically in \u0000<inline-formula><tex-math>$O(n^{3})$</tex-math></inline-formula>\u0000 time. In a square-lattice architecture with two entanglement buses, we show that a graph state can be synthesized using at most \u0000<inline-formula><tex-math>$lceil frac{1}{2}nrceil +1$</tex-math></inline-formula>\u0000 layers of GHZ state injections, and Clifford operations require only \u0000<inline-formula><tex-math>$lceil frac{3}{2} n rceil + O(sqrt{n})$</tex-math></inline-formula>\u0000 layers of GHZ state injections.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-13DOI: 10.1109/TQE.2024.3376721
Tatsuhiko Shirai;Nozomu Togawa
In this article, we propose a postprocessing variationally scheduled quantum algorithm (pVSQA) for solving constrained combinatorial optimization problems (COPs). COPs are typically transformed into ground-state search problems of the Ising model on a quantum annealer or gate-based quantum device. Variational methods are used to find an optimal schedule function that leads to high-quality solutions in a short amount of time. Postprocessing techniques convert the output solutions of the quantum devices to satisfy the constraints of the COPs. The pVSQA combines the variational methods and the postprocessing technique. We obtain a sufficient condition for constrained COPs to apply the pVSQA based on a greedy postprocessing algorithm. We apply the proposed method to two constrained NP-hard COPs: the graph partitioning problem and the quadratic knapsack problem. The pVSQA on a simulator shows that a small number of variational parameters is sufficient to achieve a (near-) optimal performance within a predetermined operation time. Then, building upon the simulator results, we implement the pVSQA on a quantum annealer and a gate-based quantum device. The experimental results demonstrate the effectiveness of our proposed method.
{"title":"Postprocessing Variationally Scheduled Quantum Algorithm for Constrained Combinatorial Optimization Problems","authors":"Tatsuhiko Shirai;Nozomu Togawa","doi":"10.1109/TQE.2024.3376721","DOIUrl":"https://doi.org/10.1109/TQE.2024.3376721","url":null,"abstract":"In this article, we propose a postprocessing variationally scheduled quantum algorithm (pVSQA) for solving constrained combinatorial optimization problems (COPs). COPs are typically transformed into ground-state search problems of the Ising model on a quantum annealer or gate-based quantum device. Variational methods are used to find an optimal schedule function that leads to high-quality solutions in a short amount of time. Postprocessing techniques convert the output solutions of the quantum devices to satisfy the constraints of the COPs. The pVSQA combines the variational methods and the postprocessing technique. We obtain a sufficient condition for constrained COPs to apply the pVSQA based on a greedy postprocessing algorithm. We apply the proposed method to two constrained NP-hard COPs: the graph partitioning problem and the quadratic knapsack problem. The pVSQA on a simulator shows that a small number of variational parameters is sufficient to achieve a (near-) optimal performance within a predetermined operation time. Then, building upon the simulator results, we implement the pVSQA on a quantum annealer and a gate-based quantum device. The experimental results demonstrate the effectiveness of our proposed method.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140546499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-10DOI: 10.1109/TQE.2024.3399609
Lorenzo Valentini;René Bødker Christensen;Petar Popovski;Marco Chiani
The reliable provision of entangled qubits is an essential precondition in a variety of schemes for distributed quantum computing. This is challenged by multiple nuisances, such as errors during the transmission over quantum links, but also due to degradation of the entanglement over time due to decoherence. The latter can be seen as a constraint on the latency of the quantum protocol, which brings the problem of quantum protocol design into the context of latency–reliability constraints. We address the problem through hybrid schemes that combine: indirect transmission based on teleportation and distillation, and direct transmission, based on quantum error correction (QEC). The intuition is that, at present, the quantum hardware offers low fidelity, which demands distillation; on the other hand, low latency can be obtained by QEC techniques. It is shown that, in the proposed framework, the distillation protocol gives rise to asymmetries that can be exploited by asymmetric quantum error correcting code, which sets the basis for unique hybrid distillation and coding design. Our results show that ad hoc asymmetric codes give, compared with conventional QEC, a performance boost and codeword size reduction both in a single-link and in a quantum network scenario.
{"title":"Reliable Quantum Communications Based on Asymmetry in Distillation and Coding","authors":"Lorenzo Valentini;René Bødker Christensen;Petar Popovski;Marco Chiani","doi":"10.1109/TQE.2024.3399609","DOIUrl":"https://doi.org/10.1109/TQE.2024.3399609","url":null,"abstract":"The reliable provision of entangled qubits is an essential precondition in a variety of schemes for distributed quantum computing. This is challenged by multiple nuisances, such as errors during the transmission over quantum links, but also due to degradation of the entanglement over time due to decoherence. The latter can be seen as a constraint on the latency of the quantum protocol, which brings the problem of quantum protocol design into the context of latency–reliability constraints. We address the problem through hybrid schemes that combine: indirect transmission based on teleportation and distillation, and direct transmission, based on quantum error correction (QEC). The intuition is that, at present, the quantum hardware offers low fidelity, which demands distillation; on the other hand, low latency can be obtained by QEC techniques. It is shown that, in the proposed framework, the distillation protocol gives rise to asymmetries that can be exploited by asymmetric quantum error correcting code, which sets the basis for unique hybrid distillation and coding design. Our results show that ad hoc asymmetric codes give, compared with conventional QEC, a performance boost and codeword size reduction both in a single-link and in a quantum network scenario.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10528897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.1109/TQE.2024.3374879
Sara Ayman Metwalli;Rodney Van Meter
This article introduces a process framework for debugging quantum circuits, focusing on three distinct types of circuit blocks: amplitude–permutation, phase-modulation, and amplitude–redistribution circuit blocks. Our research addresses the critical need for specialized debugging approaches tailored to the unique properties of each circuit type. For amplitude–permutation circuits, we propose techniques to correct amplitude–permutations mimicking classical operations. In phase-modulation circuits, our proposed strategy targets the precise calibration of phase alterations essential for quantum computations. The most complex amplitude–redistribution circuits demand advanced methods to adjust probability amplitudes. This research bridges a vital gap in current methodologies and lays the groundwork for future advancements in quantum circuit debugging. Our contributions are twofold: we present a comprehensive unit testing tool (Cirquo) and debugging approaches tailored to the unique demands of quantum computing, and we provide empirical evidence of its effectiveness in optimizing quantum circuit performance. This work is a crucial step toward realizing robust quantum computing systems and their applications in various domains.
{"title":"Testing and Debugging Quantum Circuits","authors":"Sara Ayman Metwalli;Rodney Van Meter","doi":"10.1109/TQE.2024.3374879","DOIUrl":"https://doi.org/10.1109/TQE.2024.3374879","url":null,"abstract":"This article introduces a process framework for debugging quantum circuits, focusing on three distinct types of circuit blocks: amplitude–permutation, phase-modulation, and amplitude–redistribution circuit blocks. Our research addresses the critical need for specialized debugging approaches tailored to the unique properties of each circuit type. For amplitude–permutation circuits, we propose techniques to correct amplitude–permutations mimicking classical operations. In phase-modulation circuits, our proposed strategy targets the precise calibration of phase alterations essential for quantum computations. The most complex amplitude–redistribution circuits demand advanced methods to adjust probability amplitudes. This research bridges a vital gap in current methodologies and lays the groundwork for future advancements in quantum circuit debugging. Our contributions are twofold: we present a comprehensive unit testing tool (Cirquo) and debugging approaches tailored to the unique demands of quantum computing, and we provide empirical evidence of its effectiveness in optimizing quantum circuit performance. This work is a crucial step toward realizing robust quantum computing systems and their applications in various domains.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140550145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.1109/TQE.2024.3398410
Carlo Mastroianni;Francesco Plastina;Jacopo Settino;Andrea Vinci
Modern cloud/edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed edge/fog nodes, centralized data centers, and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this article, we explore the possibility of solving this problem with variational quantum algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performance, in terms of success probability, of two algorithms, i.e., quantum approximate optimization algorithm and variational quantum eigensolver (VQE). The simulation experiments, performed for a set of simple problems, show that the VQE algorithm ensures better performance when it is equipped with appropriate circuit ansatzes that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential.
{"title":"Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture","authors":"Carlo Mastroianni;Francesco Plastina;Jacopo Settino;Andrea Vinci","doi":"10.1109/TQE.2024.3398410","DOIUrl":"https://doi.org/10.1109/TQE.2024.3398410","url":null,"abstract":"Modern cloud/edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed edge/fog nodes, centralized data centers, and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this article, we explore the possibility of solving this problem with variational quantum algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performance, in terms of success probability, of two algorithms, i.e., quantum approximate optimization algorithm and variational quantum eigensolver (VQE). The simulation experiments, performed for a set of simple problems, show that the VQE algorithm ensures better performance when it is equipped with appropriate circuit \u0000<italic>ansatzes</i>\u0000 that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.1109/TQE.2024.3374251
Giovanni Acampora;Michele Grossi;Michael Schenk;Roberto Schiattarella
Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.
{"title":"Quantum Fuzzy Inference Engine for Particle Accelerator Control","authors":"Giovanni Acampora;Michele Grossi;Michael Schenk;Roberto Schiattarella","doi":"10.1109/TQE.2024.3374251","DOIUrl":"https://doi.org/10.1109/TQE.2024.3374251","url":null,"abstract":"Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10462538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.1109/TQE.2024.3373903
Wenbin Yu;Lei Yin;Chengjun Zhang;Yadang Chen;Alex X. Liu
Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum–classical hybrid architecture. This architecture utilizes a pretrained multilingual bidirectional encoder representations from transformer (BERT) model to obtain vector representations of words and combines the proposed batch upload quantum recurrent neural network (BUQRNN) and parameter nonshared batch upload quantum recurrent neural network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.
{"title":"Application of Quantum Recurrent Neural Network in Low-Resource Language Text Classification","authors":"Wenbin Yu;Lei Yin;Chengjun Zhang;Yadang Chen;Alex X. Liu","doi":"10.1109/TQE.2024.3373903","DOIUrl":"https://doi.org/10.1109/TQE.2024.3373903","url":null,"abstract":"Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum–classical hybrid architecture. This architecture utilizes a pretrained multilingual bidirectional encoder representations from transformer (BERT) model to obtain vector representations of words and combines the proposed batch upload quantum recurrent neural network (BUQRNN) and parameter nonshared batch upload quantum recurrent neural network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10461108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim at investigating the propagation of logical-shift errors (i.e., the unexpected modification of the qubit state) in QNNs. We propose a bottom–up evaluation reporting data from 13 322 547 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logical-shift error propagation through the qubits, channels, and subgrids, identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.
{"title":"Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks","authors":"Marzio Vallero;Emanuele Dri;Edoardo Giusto;Bartolomeo Montrucchio;Paolo Rech","doi":"10.1109/TQE.2024.3372880","DOIUrl":"https://doi.org/10.1109/TQE.2024.3372880","url":null,"abstract":"Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim at investigating the propagation of logical-shift errors (i.e., the unexpected modification of the qubit state) in QNNs. We propose a bottom–up evaluation reporting data from 13 322 547 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logical-shift error propagation through the qubits, channels, and subgrids, identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10458381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140546351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1109/TQE.2024.3368073
Qing Zhou;Xueming Tang;Songfeng Lu;Hao Yang
In this article, we develop a generic controlled alternate quantum walk model by combining parity-dependent quantum walks with distinct arbitrary memory lengths and propose a hash function (called QHFM-P) based on this model. The statistical properties of the proposed scheme are stable with respect to the coin parameters of the underlying controlled quantum walks, and with certain parameter values, the collision resistance property of QHFM-P is better than that of the state-of-the-art hash functions based on discrete quantum walks. Moreover, the proposed hash function can also maintain near-ideal statistical performance when the input message is of small length. In addition, we derive a type of inappropriate initial states of hash functions based on 1-D one-particle quantum walks (with ordinary shift operator) on cycles, with which all messages will be mapped to the same hash value, regardless of the angles adopted by the coin parameters.
{"title":"A Stable Hash Function Based on Parity-Dependent Quantum Walks With Memory (August 2023)","authors":"Qing Zhou;Xueming Tang;Songfeng Lu;Hao Yang","doi":"10.1109/TQE.2024.3368073","DOIUrl":"https://doi.org/10.1109/TQE.2024.3368073","url":null,"abstract":"In this article, we develop a generic controlled alternate quantum walk model by combining parity-dependent quantum walks with distinct arbitrary memory lengths and propose a hash function (called QHFM-P) based on this model. The statistical properties of the proposed scheme are stable with respect to the coin parameters of the underlying controlled quantum walks, and with certain parameter values, the collision resistance property of QHFM-P is better than that of the state-of-the-art hash functions based on discrete quantum walks. Moreover, the proposed hash function can also maintain near-ideal statistical performance when the input message is of small length. In addition, we derive a type of inappropriate initial states of hash functions based on 1-D one-particle quantum walks (with ordinary shift operator) on cycles, with which all messages will be mapped to the same hash value, regardless of the angles adopted by the coin parameters.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140192066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-19DOI: 10.1109/TQE.2024.3367234
Ryan L'Abbate;Anthony D'Onofrio;Samuel Stein;Samuel Yen-Chi Chen;Ang Li;Pin-Yu Chen;Juntao Chen;Ying Mao
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this article, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. In addition, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
{"title":"A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity","authors":"Ryan L'Abbate;Anthony D'Onofrio;Samuel Stein;Samuel Yen-Chi Chen;Ang Li;Pin-Yu Chen;Juntao Chen;Ying Mao","doi":"10.1109/TQE.2024.3367234","DOIUrl":"https://doi.org/10.1109/TQE.2024.3367234","url":null,"abstract":"Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this article, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. In addition, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10439653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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