Pub Date : 2024-08-09DOI: 10.1109/TQE.2024.3441229
John D. Malcolm;Alexander Roth;Mladjan Radic;Pablo Martín-Ramiro;Jon Oillarburu;Borja Aizpurua;Román Orús;Samuel Mugel
In this article, we apply a quantum optimization algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector. Using the quantum annealer provided by D-Wave Systems, we analyze the performance of the quantum and quantum–classical hybrid solvers and compare them to deterministic- and random-algorithm classical benchmark solvers. The continued evolution of the quantum technology, indicating an expectation for even greater relevance in the future, is discussed, and the revolutionary potential it could have in the manufacturing sector is highlighted.
{"title":"Multidisk Clutch Optimization Using Quantum Annealing","authors":"John D. Malcolm;Alexander Roth;Mladjan Radic;Pablo Martín-Ramiro;Jon Oillarburu;Borja Aizpurua;Román Orús;Samuel Mugel","doi":"10.1109/TQE.2024.3441229","DOIUrl":"https://doi.org/10.1109/TQE.2024.3441229","url":null,"abstract":"In this article, we apply a quantum optimization algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector. Using the quantum annealer provided by D-Wave Systems, we analyze the performance of the quantum and quantum–classical hybrid solvers and compare them to deterministic- and random-algorithm classical benchmark solvers. The continued evolution of the quantum technology, indicating an expectation for even greater relevance in the future, is discussed, and the revolutionary potential it could have in the manufacturing sector is highlighted.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10632778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230840","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 : 2024-08-07DOI: 10.1109/TQE.2024.3440192
Hany Khalifa;Riku Jäntti;Gheorghe Sorin Paraoanu
Microwave quantum information networks require reliable transmission of single-photon propagating modes over lossy channels. In this article, we propose a microwave noiseless linear amplifier (NLA) suitable to circumvent the losses incurred by a flying photon undergoing an amplitude damping channel (ADC). The proposed model is constructed by engineering a simple 1-D four-node cluster state. Contrary to conventional NLAs based on quantum scissors (QS), single-photon amplification is realized without the need for photon number resolving detectors. Entanglement between nodes comprising the device's cluster is achieved by means of a controlled phase gate. Furthermore, photon measurements are implemented by quantum nondemolition detectors, which are currently available as a part of the circuit quantum electrodynamics toolbox. We analyze the performance of our device practically by considering detection inefficiency and dark count probability. We further examine the potential usage of our device in low-power quantum sensing applications and remote secret key generation (SKG). Specifically, we demonstrate the device's ability to prepare loss-free resources offline, and its capacity to overcome the repeaterless bound of SKG. We compare the performance of our device against a QS-NLA for the aforementioned applications, and highlight explicitly the operating conditions under which our device can outperform a QS-NLA. The proposed device is also suitable for applications in the optical domain.
{"title":"Fault-Tolerant One-Way Noiseless Amplification for Microwave Bosonic Quantum Information Processing","authors":"Hany Khalifa;Riku Jäntti;Gheorghe Sorin Paraoanu","doi":"10.1109/TQE.2024.3440192","DOIUrl":"https://doi.org/10.1109/TQE.2024.3440192","url":null,"abstract":"Microwave quantum information networks require reliable transmission of single-photon propagating modes over lossy channels. In this article, we propose a microwave noiseless linear amplifier (NLA) suitable to circumvent the losses incurred by a flying photon undergoing an amplitude damping channel (ADC). The proposed model is constructed by engineering a simple 1-D four-node cluster state. Contrary to conventional NLAs based on quantum scissors (QS), single-photon amplification is realized without the need for photon number resolving detectors. Entanglement between nodes comprising the device's cluster is achieved by means of a controlled phase gate. Furthermore, photon measurements are implemented by quantum nondemolition detectors, which are currently available as a part of the circuit quantum electrodynamics toolbox. We analyze the performance of our device practically by considering detection inefficiency and dark count probability. We further examine the potential usage of our device in low-power quantum sensing applications and remote secret key generation (SKG). Specifically, we demonstrate the device's ability to prepare loss-free resources offline, and its capacity to overcome the repeaterless bound of SKG. We compare the performance of our device against a QS-NLA for the aforementioned applications, and highlight explicitly the operating conditions under which our device can outperform a QS-NLA. The proposed device is also suitable for applications in the optical domain.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10629178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143741","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 : 2024-08-06DOI: 10.1109/TQE.2024.3439135
Kentaro Tamura;Yohichi Suzuki;Rudy Raymond;Hiroshi C. Watanabe;Yuki Sato;Ruho Kondo;Michihiko Sugawara;Naoki Yamamoto
Relaxation is a common way for dealing with combinatorial optimization problems. Quantum random-access optimization (QRAO) is a quantum-relaxation-based optimizer that uses fewer qubits than the number of bits in the original problem by encoding multiple variables per qubit using quantum random-access code (QRAC). Reducing the number of qubits will alleviate physical noise (typically, decoherence), and as a result, the quality of the binary solution of QRAO may be robust against noise, which is, however, unknown. In this article, we numerically demonstrate that the mean approximation ratio of the (3, 1)-QRAC Hamiltonian, i.e., the Hamiltonian utilizing the encoding of three bits into one qubit by QRAC, is less affected by noise compared with the conventional Ising Hamiltonian used in the quantum annealer and the quantum approximate optimization algorithm. Based on this observation, we discuss a plausible mechanism behind the robustness of QRAO under depolarizing noise. Finally, we assess the number of shots required to estimate the values of binary variables correctly under depolarizing noise and show that the (3, 1)-QRAC Hamiltonian requires less shots to achieve the same accuracy compared with the Ising Hamiltonian.
{"title":"Noise Robustness of Quantum Relaxation for Combinatorial Optimization","authors":"Kentaro Tamura;Yohichi Suzuki;Rudy Raymond;Hiroshi C. Watanabe;Yuki Sato;Ruho Kondo;Michihiko Sugawara;Naoki Yamamoto","doi":"10.1109/TQE.2024.3439135","DOIUrl":"https://doi.org/10.1109/TQE.2024.3439135","url":null,"abstract":"Relaxation is a common way for dealing with combinatorial optimization problems. Quantum random-access optimization (QRAO) is a quantum-relaxation-based optimizer that uses fewer qubits than the number of bits in the original problem by encoding multiple variables per qubit using quantum random-access code (QRAC). Reducing the number of qubits will alleviate physical noise (typically, decoherence), and as a result, the quality of the binary solution of QRAO may be robust against noise, which is, however, unknown. In this article, we numerically demonstrate that the mean approximation ratio of the (3, 1)-QRAC Hamiltonian, i.e., the Hamiltonian utilizing the encoding of three bits into one qubit by QRAC, is less affected by noise compared with the conventional Ising Hamiltonian used in the quantum annealer and the quantum approximate optimization algorithm. Based on this observation, we discuss a plausible mechanism behind the robustness of QRAO under depolarizing noise. Finally, we assess the number of shots required to estimate the values of binary variables correctly under depolarizing noise and show that the (3, 1)-QRAC Hamiltonian requires less shots to achieve the same accuracy compared with the Ising Hamiltonian.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117786","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}
Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement of quantum repeaters and memories. In this article, we present a comprehensive framework for network planning, aiming to efficiently distribute quantum repeaters across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We apply our framework to several cases including a preliminary illustration of a dumbbell network topology and real-world cases of the SURFnet and ESnet. We explore the effect of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, uncovering their impacts on real-time network performance.
{"title":"Resource Placement for Rate and Fidelity Maximization in Quantum Networks","authors":"Shahrooz Pouryousef;Hassan Shapourian;Alireza Shabani;Ramana Kompella;Don Towsley","doi":"10.1109/TQE.2024.3432390","DOIUrl":"https://doi.org/10.1109/TQE.2024.3432390","url":null,"abstract":"Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement of quantum repeaters and memories. In this article, we present a comprehensive framework for network planning, aiming to efficiently distribute quantum repeaters across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We apply our framework to several cases including a preliminary illustration of a dumbbell network topology and real-world cases of the SURFnet and ESnet. We explore the effect of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, uncovering their impacts on real-time network performance.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10607917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045247","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}
Detectors capable of resolving the number of photons are essential in many applications, ranging from classic photonics to quantum optics and quantum communication. In particular, photon-number-resolving detectors based on arrays of superconducting nanostrips can offer a high detection efficiency, a low dark count rate, and a recovery time of a few nanoseconds. In this work, we use a detector of this kind for the unbiased generation of random numbers by following two different methods based on the detection of photons. In the former, we exploit the property that the light is equally distributed on each strip of the entire detector, whereas in the latter, we exploit the fact that, for a high average number of photons, the parity of the Poisson distribution of the number of photons emitted by the laser tends to be zero. In addition, since these two methods are independent, it is possible to use them at the same time.
{"title":"Superconducting Nanostrip Photon-Number-Resolving Detector as an Unbiased Random Number Generator","authors":"Pasquale Ercolano;Mikkel Ejrnaes;Ciro Bruscino;Syed Muhammad Junaid Bukhari;Daniela Salvoni;Chengjun Zhang;Jia Huang;Hao Li;Lixing You;Loredana Parlato;Giovanni Piero Pepe","doi":"10.1109/TQE.2024.3432070","DOIUrl":"https://doi.org/10.1109/TQE.2024.3432070","url":null,"abstract":"Detectors capable of resolving the number of photons are essential in many applications, ranging from classic photonics to quantum optics and quantum communication. In particular, photon-number-resolving detectors based on arrays of superconducting nanostrips can offer a high detection efficiency, a low dark count rate, and a recovery time of a few nanoseconds. In this work, we use a detector of this kind for the unbiased generation of random numbers by following two different methods based on the detection of photons. In the former, we exploit the property that the light is equally distributed on each strip of the entire detector, whereas in the latter, we exploit the fact that, for a high average number of photons, the parity of the Poisson distribution of the number of photons emitted by the laser tends to be zero. In addition, since these two methods are independent, it is possible to use them at the same time.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10606064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993932","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 : 2024-07-18DOI: 10.1109/TQE.2024.3430215
Daniel Hothem;Kevin Young;Tommie Catanach;Timothy Proctor
Accurately predicting a quantum computer's capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and use. In this work, we propose a hardware-agnostic method to efficiently construct scalable predictive models of a quantum computer's capability for almost any class of circuits and demonstrate our method using convolutional neural networks (CNNs). Our CNN-based approach works by efficiently representing a circuit as a 3-D tensor and then using a CNN to predict its success rate. Our CNN capability models obtain approximately a 1% average absolute prediction error when modeling processors experiencing both Markovian and non-Markovian stochastic Pauli errors. We also apply our CNNs to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around 2–5%), and we highlight the challenges to building better neural network capability models.
{"title":"Learning a Quantum Computer's Capability","authors":"Daniel Hothem;Kevin Young;Tommie Catanach;Timothy Proctor","doi":"10.1109/TQE.2024.3430215","DOIUrl":"https://doi.org/10.1109/TQE.2024.3430215","url":null,"abstract":"Accurately predicting a quantum computer's capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and use. In this work, we propose a hardware-agnostic method to efficiently construct scalable predictive models of a quantum computer's capability for almost any class of circuits and demonstrate our method using convolutional neural networks (CNNs). Our CNN-based approach works by efficiently representing a circuit as a 3-D tensor and then using a CNN to predict its success rate. Our CNN capability models obtain approximately a 1% average absolute prediction error when modeling processors experiencing both Markovian and non-Markovian stochastic Pauli errors. We also apply our CNNs to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around 2–5%), and we highlight the challenges to building better neural network capability models.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10603420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090791","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 : 2024-07-17DOI: 10.1109/TQE.2024.3429451
Nikiforos Paraskevopoulos;Carmen G. Almudever;Sebastian Feld
As quantum computing devices increase in size with respect to the number of qubits, two-qubit interactions become more challenging, necessitating innovative and scalable qubit routing solutions. In this work, we introduce beSnake, a novel algorithm specifically designed to address the intricate qubit routing challenges in scalable spin-qubit architectures. Unlike traditional methods in superconducting architectures that solely rely on swap operations, beSnake also incorporates the shuttle operation to optimize the execution time and fidelity of quantum circuits and achieves fast computation times of the routing task itself. Employing a simple breadth-first search approach, beSnake effectively manages the restrictions created by diverse topologies and qubit positions acting as obstacles for up to 72% qubit density. It also has the option to adjust the level of optimization and to dynamically tackle parallelized routing tasks, all the while maintaining noise awareness. Our simulations demonstrate beSnake's advantage over an existing routing solution on random circuits and real quantum algorithms with up to 1000 qubits, showing an average improvement of up to 80% in gate overhead, 54% in depth overhead, and up to 8.33 times faster routing times.
{"title":"BeSnake: A Routing Algorithm for Scalable Spin-Qubit Architectures","authors":"Nikiforos Paraskevopoulos;Carmen G. Almudever;Sebastian Feld","doi":"10.1109/TQE.2024.3429451","DOIUrl":"https://doi.org/10.1109/TQE.2024.3429451","url":null,"abstract":"As quantum computing devices increase in size with respect to the number of qubits, two-qubit interactions become more challenging, necessitating innovative and scalable qubit routing solutions. In this work, we introduce beSnake, a novel algorithm specifically designed to address the intricate qubit routing challenges in scalable spin-qubit architectures. Unlike traditional methods in superconducting architectures that solely rely on \u0000<sc>swap</small>\u0000 operations, beSnake also incorporates the shuttle operation to optimize the execution time and fidelity of quantum circuits and achieves fast computation times of the routing task itself. Employing a simple breadth-first search approach, beSnake effectively manages the restrictions created by diverse topologies and qubit positions acting as obstacles for up to 72% qubit density. It also has the option to adjust the level of optimization and to dynamically tackle parallelized routing tasks, all the while maintaining noise awareness. Our simulations demonstrate beSnake's advantage over an existing routing solution on random circuits and real quantum algorithms with up to 1000 qubits, showing an average improvement of up to 80% in gate overhead, 54% in depth overhead, and up to 8.33 times faster routing times.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117787","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}
In this article, we generalize the approximate quantum compiling algorithm into a new method for cnot -depth reduction, which is apt to process wide target quantum circuits. Combining this method with state-of-the-art techniques for error mitigation and circuit compiling, we present a ten-qubit experimental demonstration of iterative amplitude estimation on a quantum computer. The target application is a derivation of the expected value of contract portfolios in the energy industry. In parallel, we also introduce a new variant of the quantum amplitude estimation algorithm, which we call dynamic amplitude estimation, as it is based on the dynamic circuit capability of quantum devices. The algorithm achieves a reduction in the circuit width in the order of the binary precision compared to the typical implementation of quantum amplitude estimation, while simultaneously decreasing the number of quantum–classical iterations (again in the order of the binary precision) compared to the iterative amplitude estimation. The calculation of the expected value, value at risk, and conditional value at risk of contract portfolios on quantum hardware provides a proof of principle of the new algorithm.
{"title":"Energy Risk Analysis With Dynamic Amplitude Estimation and Piecewise Approximate Quantum Compiling","authors":"Kumar Ghosh;Kavitha Yogaraj;Gabriele Agliardi;Piergiacomo Sabino;Marina Fernández-Campoamor;Juan Bernabé-Moreno;Giorgio Cortiana;Omar Shehab;Corey O'Meara","doi":"10.1109/TQE.2024.3425969","DOIUrl":"https://doi.org/10.1109/TQE.2024.3425969","url":null,"abstract":"In this article, we generalize the approximate quantum compiling algorithm into a new method for \u0000<sc>cnot</small>\u0000-depth reduction, which is apt to process wide target quantum circuits. Combining this method with state-of-the-art techniques for error mitigation and circuit compiling, we present a ten-qubit experimental demonstration of iterative amplitude estimation on a quantum computer. The target application is a derivation of the expected value of contract portfolios in the energy industry. In parallel, we also introduce a new variant of the quantum amplitude estimation algorithm, which we call dynamic amplitude estimation, as it is based on the dynamic circuit capability of quantum devices. The algorithm achieves a reduction in the circuit width in the order of the binary precision compared to the typical implementation of quantum amplitude estimation, while simultaneously decreasing the number of quantum–classical iterations (again in the order of the binary precision) compared to the iterative amplitude estimation. The calculation of the expected value, value at risk, and conditional value at risk of contract portfolios on quantum hardware provides a proof of principle of the new algorithm.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10592808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645567","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 : 2024-07-04DOI: 10.1109/TQE.2024.3421294
Bryce Fuller;Charles Hadfield;Jennifer R. Glick;Takashi Imamichi;Toshinari Itoko;Richard J. Thompson;Yang Jiao;Marna M. Kagele;Adriana W. Blom-Schieber;Rudy Raymond;Antonio Mezzacapo
Combinatorial problems are formulated to find optimal designs within a fixed set of constraints and are commonly found across diverse engineering and scientific domains. Understanding how to best use quantum computers for combinatorial optimization remains an ongoing area of study. Here, we propose new methods for producing approximate solutions to quadratic unconstrained binary optimization problems, which are based on relaxations to local quantum Hamiltonians. We look specifically at approximating solutions for the maximum cut problem and its weighted version. These relaxations are defined through commutative maps, which in turn are constructed borrowing ideas from quantum random access codes. We establish relations between the spectra of the relaxed Hamiltonians and optimal cuts of the original problems, via two quantum rounding protocols. The first one is based on projections to random magic states. It produces average cuts that approximate the optimal one by a factor of least 0.555 or 0.625, depending on the relaxation chosen, if given access to a quantum state with energy between the optimal classical cut and the maximal relaxed energy. The second rounding protocol is deterministic and is based on the estimation of Pauli observables. The proposed quantum relaxations inherit memory compression from quantum random access codes, which allowed us to test the performances of the methods presented for 3-regular random graphs and a design problem motivated by industry for sizes up to 40 nodes, on superconducting quantum processors.
{"title":"Approximate Solutions of Combinatorial Problems via Quantum Relaxations","authors":"Bryce Fuller;Charles Hadfield;Jennifer R. Glick;Takashi Imamichi;Toshinari Itoko;Richard J. Thompson;Yang Jiao;Marna M. Kagele;Adriana W. Blom-Schieber;Rudy Raymond;Antonio Mezzacapo","doi":"10.1109/TQE.2024.3421294","DOIUrl":"https://doi.org/10.1109/TQE.2024.3421294","url":null,"abstract":"Combinatorial problems are formulated to find optimal designs within a fixed set of constraints and are commonly found across diverse engineering and scientific domains. Understanding how to best use quantum computers for combinatorial optimization remains an ongoing area of study. Here, we propose new methods for producing approximate solutions to quadratic unconstrained binary optimization problems, which are based on relaxations to local quantum Hamiltonians. We look specifically at approximating solutions for the maximum cut problem and its weighted version. These relaxations are defined through commutative maps, which in turn are constructed borrowing ideas from quantum random access codes. We establish relations between the spectra of the relaxed Hamiltonians and optimal cuts of the original problems, via two quantum rounding protocols. The first one is based on projections to random magic states. It produces average cuts that approximate the optimal one by a factor of least 0.555 or 0.625, depending on the relaxation chosen, if given access to a quantum state with energy between the optimal classical cut and the maximal relaxed energy. The second rounding protocol is deterministic and is based on the estimation of Pauli observables. The proposed quantum relaxations inherit memory compression from quantum random access codes, which allowed us to test the performances of the methods presented for 3-regular random graphs and a design problem motivated by industry for sizes up to 40 nodes, on superconducting quantum processors.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964711","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 : 2024-06-27DOI: 10.1109/TQE.2024.3419773
Hyunwoo Jung;Inayat Ali;Jeongseok Ha
To perform reliable information processing in quantum computers, quantum error correction (QEC) codes are essential for the detection and correction of errors in the qubits. Among QEC codes, topological QEC codes are designed to interact between the neighboring qubits, which is a promising property for easing the implementation requirements. In addition, the locality to the qubits provides unusual tolerance to local errors. Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. In this work, we propose a new decoding algorithm for surface codes, i.e., a type of topological codes, by using convolutional neural networks (CNNs) tailored for the topological lattice structure of the surface codes. In particular, the proposed algorithm takes advantage of the syndrome pattern, which is represented as a part of a rectangular lattice given to the CNN as its input. The remaining part of the rectangular lattice is filled with a carefully selected incoherent value for better logical error rate performance. In addition, we introduce how to optimize the hyperparameters in the CNN, according to the lattice structure of a given surface code. This reduces the overall decoding complexity and makes the CNN-based decoder computationally more suitable for implementation. The numerical results show that the proposed decoding algorithm effectively improves the decoding performance in terms of logical error rate as compared to the existing algorithms on various quantum error models.
{"title":"Convolutional Neural Decoder for Surface Codes","authors":"Hyunwoo Jung;Inayat Ali;Jeongseok Ha","doi":"10.1109/TQE.2024.3419773","DOIUrl":"https://doi.org/10.1109/TQE.2024.3419773","url":null,"abstract":"To perform reliable information processing in quantum computers, quantum error correction (QEC) codes are essential for the detection and correction of errors in the qubits. Among QEC codes, topological QEC codes are designed to interact between the neighboring qubits, which is a promising property for easing the implementation requirements. In addition, the locality to the qubits provides unusual tolerance to local errors. Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. In this work, we propose a new decoding algorithm for surface codes, i.e., a type of topological codes, by using convolutional neural networks (CNNs) tailored for the topological lattice structure of the surface codes. In particular, the proposed algorithm takes advantage of the syndrome pattern, which is represented as a part of a rectangular lattice given to the CNN as its input. The remaining part of the rectangular lattice is filled with a carefully selected incoherent value for better logical error rate performance. In addition, we introduce how to optimize the hyperparameters in the CNN, according to the lattice structure of a given surface code. This reduces the overall decoding complexity and makes the CNN-based decoder computationally more suitable for implementation. The numerical results show that the proposed decoding algorithm effectively improves the decoding performance in terms of logical error rate as compared to the existing algorithms on various quantum error models.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169623","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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