M. Bowman, P. Gokhale, Jeffrey Larson, Ji Liu, Martin Suchara
While quantum computing holds great potential in combinatorial optimization, electronic structure calculation, and number theory, the current era of quantum computing is limited by noisy hardware. Many quantum compilation approaches can mitigate the effects of imperfect hardware by optimizing quantum circuits for objectives such as critical path length. Few approaches consider quantum circuits in terms of the set of vendor-calibrated operations (i.e., native gates) available on target hardware. This manuscript expands the analytical and numerical approaches for optimizing quantum circuits at this abstraction level. We present a procedure for combining the strengths of analytical native gate-level optimization with numerical optimization. Although we focus on optimizing Toffoli gates on the IBMQ native gate set, the methods presented are generalizable to any gate and superconducting qubit architecture. Our optimized Toffoli gate implementation demonstrates an 18% reduction in infidelity compared with the canonical implementation as benchmarked on IBM Jakarta with quantum process tomography. Assuming the inclusion of multi-qubit cross-resonance (MCR) gates in the IBMQ native gate set, we produce Toffoli implementations with only six multi-qubit gates, a 25% reduction from the canonical eight multi-qubit implementations for linearly connected qubits.
{"title":"Hardware-Conscious Optimization of the Quantum Toffoli Gate","authors":"M. Bowman, P. Gokhale, Jeffrey Larson, Ji Liu, Martin Suchara","doi":"10.1145/3609229","DOIUrl":"https://doi.org/10.1145/3609229","url":null,"abstract":"While quantum computing holds great potential in combinatorial optimization, electronic structure calculation, and number theory, the current era of quantum computing is limited by noisy hardware. Many quantum compilation approaches can mitigate the effects of imperfect hardware by optimizing quantum circuits for objectives such as critical path length. Few approaches consider quantum circuits in terms of the set of vendor-calibrated operations (i.e., native gates) available on target hardware. This manuscript expands the analytical and numerical approaches for optimizing quantum circuits at this abstraction level. We present a procedure for combining the strengths of analytical native gate-level optimization with numerical optimization. Although we focus on optimizing Toffoli gates on the IBMQ native gate set, the methods presented are generalizable to any gate and superconducting qubit architecture. Our optimized Toffoli gate implementation demonstrates an 18% reduction in infidelity compared with the canonical implementation as benchmarked on IBM Jakarta with quantum process tomography. Assuming the inclusion of multi-qubit cross-resonance (MCR) gates in the IBMQ native gate set, we produce Toffoli implementations with only six multi-qubit gates, a 25% reduction from the canonical eight multi-qubit implementations for linearly connected qubits.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123628959","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}
Kaitlin N. Smith, Gokul Subramanian Ravi, Prakash Murali, Jonathan M. Baker, N. Earnest, Ali Javadi-Abhari, F. Chong
Quantum systems have the potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit [13, 20, 56], calibrating new gates [50], or extending a circuit’s runtime [32], this article’s primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in Noisy Intermediate Scale Quantum devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This article presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines. Unlike past approaches that apply reversibility properties to improve quantum circuit execution [35], TimeStitch amplifies fidelity without violating critical path frontiers in either the slack tuning procedures or the final rescheduled circuit. On average, compared to a state-of-the-art baseline, a practically constrained TimeStitch achieves a mean 38% relative improvement in success rates, with a maximum of 106%, while observing bounds on circuit depth. When unconstrained by depth criteria, TimeStitch produces a mean relative fidelity increase of 50% with a maximum of 256%. Finally, when TimeStitch intelligently leverages periodic dynamical decoupling within its scheduling framework, a mean 64% improvement is observed over the baseline, relatively outperforming stand-alone dynamical decoupling by 19%, with a maximum of 287%.
{"title":"TimeStitch: Exploiting Slack to Mitigate Decoherence in Quantum Circuits","authors":"Kaitlin N. Smith, Gokul Subramanian Ravi, Prakash Murali, Jonathan M. Baker, N. Earnest, Ali Javadi-Abhari, F. Chong","doi":"10.1145/3548778","DOIUrl":"https://doi.org/10.1145/3548778","url":null,"abstract":"Quantum systems have the potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit [13, 20, 56], calibrating new gates [50], or extending a circuit’s runtime [32], this article’s primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in Noisy Intermediate Scale Quantum devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This article presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines. Unlike past approaches that apply reversibility properties to improve quantum circuit execution [35], TimeStitch amplifies fidelity without violating critical path frontiers in either the slack tuning procedures or the final rescheduled circuit. On average, compared to a state-of-the-art baseline, a practically constrained TimeStitch achieves a mean 38% relative improvement in success rates, with a maximum of 106%, while observing bounds on circuit depth. When unconstrained by depth criteria, TimeStitch produces a mean relative fidelity increase of 50% with a maximum of 256%. Finally, when TimeStitch intelligently leverages periodic dynamical decoupling within its scheduling framework, a mean 64% improvement is observed over the baseline, relatively outperforming stand-alone dynamical decoupling by 19%, with a maximum of 287%.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"105 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120907561","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}
S. Hillmich, Alwin Zulehner, R. Kueng, I. Markov, R. Wille
Quantum computers promise to solve important problems faster than conventional computers ever could. Underneath is a fundamentally different computational primitive that introduces new challenges for the development of software tools that aid designers of corresponding quantum algorithms. The different computational primitives render classical simulation of quantum circuits particularly challenging. While the logic simulation of conventional circuits is comparatively simple with linear complexity with respect to the number of gates, quantum circuit simulation has to deal with the exponential memory requirements to represent quantum states on non-quantum hardware with respect to the number of qubits. Decision Diagrams (DDs) address this challenge through exploitation of redundancies in matrices and vectors to provide significantly more compact representations in many cases. Moreover, the probabilistic nature of quantum computations enables another angle to tackle the complexity: Quantum algorithms are resistant to some degree against small inaccuracies in the quantum state as these only lead to small changes in the outcome probabilities. We propose to exploit this resistance against (small) errors to gain even more compact decision diagrams. In this work, we investigate the potential of approximation in quantum circuit simulation in detail. To this end, we first present four dedicated schemes that exploit the error resistance and efficiently approximate quantum states represented by decision diagrams. Subsequently, we propose two simulation strategies that utilize those approximations schemes in order to improve the efficiency of DD-based quantum circuit simulation, while, at the same time, allowing the user to control the resulting degradation in accuracy. We empirically show that the proposed approximation schemes reduce the size of decision diagrams substantially and also analytically prove the effect of multiple approximations on the attained accuracy. Eventually, this enables speed-ups of the resulting approximate quantum circuit simulation of up to several orders of magnitudes—again, while controlling the fidelity of the result.
{"title":"Approximating Decision Diagrams for Quantum Circuit Simulation","authors":"S. Hillmich, Alwin Zulehner, R. Kueng, I. Markov, R. Wille","doi":"10.1145/3530776","DOIUrl":"https://doi.org/10.1145/3530776","url":null,"abstract":"Quantum computers promise to solve important problems faster than conventional computers ever could. Underneath is a fundamentally different computational primitive that introduces new challenges for the development of software tools that aid designers of corresponding quantum algorithms. The different computational primitives render classical simulation of quantum circuits particularly challenging. While the logic simulation of conventional circuits is comparatively simple with linear complexity with respect to the number of gates, quantum circuit simulation has to deal with the exponential memory requirements to represent quantum states on non-quantum hardware with respect to the number of qubits. Decision Diagrams (DDs) address this challenge through exploitation of redundancies in matrices and vectors to provide significantly more compact representations in many cases. Moreover, the probabilistic nature of quantum computations enables another angle to tackle the complexity: Quantum algorithms are resistant to some degree against small inaccuracies in the quantum state as these only lead to small changes in the outcome probabilities. We propose to exploit this resistance against (small) errors to gain even more compact decision diagrams. In this work, we investigate the potential of approximation in quantum circuit simulation in detail. To this end, we first present four dedicated schemes that exploit the error resistance and efficiently approximate quantum states represented by decision diagrams. Subsequently, we propose two simulation strategies that utilize those approximations schemes in order to improve the efficiency of DD-based quantum circuit simulation, while, at the same time, allowing the user to control the resulting degradation in accuracy. We empirically show that the proposed approximation schemes reduce the size of decision diagrams substantially and also analytically prove the effect of multiple approximations on the attained accuracy. Eventually, this enables speed-ups of the resulting approximate quantum circuit simulation of up to several orders of magnitudes—again, while controlling the fidelity of the result.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120947697","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}
Quantum data communications and networking involve classical hardware and software. Quantum storage is sensitive to environmental disturbances that may have malicious origins. Teleportation and entanglement swapping, two building blocks for the future quantum Internet, rely on secure classical bit communications. When lack of authenticity, integrity, and replay protection may have a high impact, quantum data communications are at risk and need to be protected. Building upon quantum cryptography and random generation of quantum operators, we propose a solution to protect the authenticity, integrity, and replay of quantum data communications. Our solution includes a classical data interface to quantum data cryptography. We describe how classical keying material can be mapped to quantum operators. This enables classical key management techniques for secure quantum data communications.
{"title":"Authenticity, Integrity, and Replay Protection in Quantum Data Communications and Networking","authors":"M. Barbeau, E. Kranakis, Nicolas Perez","doi":"10.1145/3517341","DOIUrl":"https://doi.org/10.1145/3517341","url":null,"abstract":"Quantum data communications and networking involve classical hardware and software. Quantum storage is sensitive to environmental disturbances that may have malicious origins. Teleportation and entanglement swapping, two building blocks for the future quantum Internet, rely on secure classical bit communications. When lack of authenticity, integrity, and replay protection may have a high impact, quantum data communications are at risk and need to be protected. Building upon quantum cryptography and random generation of quantum operators, we propose a solution to protect the authenticity, integrity, and replay of quantum data communications. Our solution includes a classical data interface to quantum data cryptography. We describe how classical keying material can be mapped to quantum operators. This enables classical key management techniques for secure quantum data communications.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389347","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}
The multiplicative depth of a logic network over the gate basis {∧ , ⊕ , ¬} is the largest number of ∧ gates on any path from a primary input to a primary output in the network. We describe a dynamic programming based logic synthesis algorithm to reduce the multiplicative depth of logic networks. It makes use of cut enumeration, tree balancing, and exclusive sum-of-products (ESOP) representations. Our algorithm has applications to cryptography and quantum computing, as a reduction in the multiplicative depth directly translates to a lower T-depth of the corresponding quantum circuit. Our experimental results show improvements in T-depth over state-of-the-art methods and over several hand-optimized quantum circuits, for instance, of AES, SHA, and floating-point arithmetic.
{"title":"Lowering the T-depth of Quantum Circuits via Logic Network Optimization","authors":"Thomas Häner, Mathias Soeken","doi":"10.1145/3501334","DOIUrl":"https://doi.org/10.1145/3501334","url":null,"abstract":"The multiplicative depth of a logic network over the gate basis {∧ , ⊕ , ¬} is the largest number of ∧ gates on any path from a primary input to a primary output in the network. We describe a dynamic programming based logic synthesis algorithm to reduce the multiplicative depth of logic networks. It makes use of cut enumeration, tree balancing, and exclusive sum-of-products (ESOP) representations. Our algorithm has applications to cryptography and quantum computing, as a reduction in the multiplicative depth directly translates to a lower T-depth of the corresponding quantum circuit. Our experimental results show improvements in T-depth over state-of-the-art methods and over several hand-optimized quantum circuits, for instance, of AES, SHA, and floating-point arithmetic.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108472","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}
O. D. Matteo, J. Izaac, T. Bromley, Anthony Joseph Hayes, Christina Lee, M. Schuld, A. Sz'ava, Chase Roberts, N. Killoran
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.
{"title":"Quantum Computing with Differentiable Quantum Transforms","authors":"O. D. Matteo, J. Izaac, T. Bromley, Anthony Joseph Hayes, Christina Lee, M. Schuld, A. Sz'ava, Chase Roberts, N. Killoran","doi":"10.1145/3592622","DOIUrl":"https://doi.org/10.1145/3592622","url":null,"abstract":"We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243979","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}
Jack Raymond, R. Stevanovic, William Bernoudy, K. Boothby, Catherine C. McGeoch, A. Berkley, Pau Farré, Joel Pasvolsky, Andrew D. King
Quantum processing units (QPUs) executing annealing algorithms have shown promise in optimization and simulation applications. Hybrid algorithms are a natural bridge to larger applications. We present a simple greedy method for solving larger-than-QPU lattice-structured Ising optimization problems. The method, implemented in the open source D-Wave Hybrid framework, uses a QPU coprocessor operating with generic parameters. Performance is evaluated for standard spin-glass problems on two lattice types with up to 11,616 spin variables, double the size that is directly programmable on any available QPU. The proposed method is shown to converge to low-energy solutions faster than an open source simulated annealing method that is either directly employed or substituted as a coprocessor in the hybrid method. Using newer Advantage QPUs in place of D-Wave 2000Q QPUs is shown to enhance convergence of the hybrid method to low energies and to achieve a lower final energy.
{"title":"Hybrid Quantum Annealing for Larger-than-QPU Lattice-structured Problems","authors":"Jack Raymond, R. Stevanovic, William Bernoudy, K. Boothby, Catherine C. McGeoch, A. Berkley, Pau Farré, Joel Pasvolsky, Andrew D. King","doi":"10.1145/3579368","DOIUrl":"https://doi.org/10.1145/3579368","url":null,"abstract":"Quantum processing units (QPUs) executing annealing algorithms have shown promise in optimization and simulation applications. Hybrid algorithms are a natural bridge to larger applications. We present a simple greedy method for solving larger-than-QPU lattice-structured Ising optimization problems. The method, implemented in the open source D-Wave Hybrid framework, uses a QPU coprocessor operating with generic parameters. Performance is evaluated for standard spin-glass problems on two lattice types with up to 11,616 spin variables, double the size that is directly programmable on any available QPU. The proposed method is shown to converge to low-energy solutions faster than an open source simulated annealing method that is either directly employed or substituted as a coprocessor in the hybrid method. Using newer Advantage QPUs in place of D-Wave 2000Q QPUs is shown to enhance convergence of the hybrid method to low energies and to achieve a lower final energy.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127614510","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}
Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson, James Ostrowski, T. Humble
Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unweighted MaxCut problems by leveraging similarities in the objective landscape among different problem instances. However, we show that the more general weighted MaxCut problem has significantly modified objective landscapes, with a proliferation of poor local optima. Our main contribution is a simple rescaling scheme that overcomes these deleterious effects of weights. We show that for a given QAOA depth, a single “typical” vector of QAOA parameters can be successfully transferred to weighted MaxCut instances. This transfer leads to a median decrease in the approximation ratio of only 2.0 percentage points relative to a considerably more expensive direct optimization on a dataset of 34,701 instances with up to 20 nodes and multiple weight distributions. This decrease can be reduced to 1.2 percentage points at the cost of only 10 additional QAOA circuit evaluations with parameters sampled from a pretrained metadistribution, or the transferred parameters can be used as a starting point for a single local optimization run to obtain approximation ratios equivalent to those achieved by exhaustive optimization in 96.35% of our cases.
{"title":"Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut","authors":"Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson, James Ostrowski, T. Humble","doi":"10.1145/3584706","DOIUrl":"https://doi.org/10.1145/3584706","url":null,"abstract":"Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unweighted MaxCut problems by leveraging similarities in the objective landscape among different problem instances. However, we show that the more general weighted MaxCut problem has significantly modified objective landscapes, with a proliferation of poor local optima. Our main contribution is a simple rescaling scheme that overcomes these deleterious effects of weights. We show that for a given QAOA depth, a single “typical” vector of QAOA parameters can be successfully transferred to weighted MaxCut instances. This transfer leads to a median decrease in the approximation ratio of only 2.0 percentage points relative to a considerably more expensive direct optimization on a dataset of 34,701 instances with up to 20 nodes and multiple weight distributions. This decrease can be reduced to 1.2 percentage points at the cost of only 10 additional QAOA circuit evaluations with parameters sampled from a pretrained metadistribution, or the transferred parameters can be used as a starting point for a single local optimization run to obtain approximation ratios equivalent to those achieved by exhaustive optimization in 96.35% of our cases.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130978698","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}
Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.
{"title":"Energy Cost of Quantum Circuit Optimisation: Predicting That Optimising Shor’s Algorithm Circuit Uses 1 GWh","authors":"A. Paler, Robert Basmadjian","doi":"10.1145/3490172","DOIUrl":"https://doi.org/10.1145/3490172","url":null,"abstract":"Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131056985","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}
{"title":"Editorial on Celebrating Quantum Computing with ACM","authors":"T. Humble, M. Ying","doi":"10.1145/3488391","DOIUrl":"https://doi.org/10.1145/3488391","url":null,"abstract":"","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133648942","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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