Learning a Quantum Computer's Capability
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 accurat...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10603420/ |
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| _version_ | 1832586891374886912 |
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| author | Daniel Hothem Kevin Young Tommie Catanach Timothy Proctor |
| author_facet | Daniel Hothem Kevin Young Tommie Catanach Timothy Proctor |
| author_sort | Daniel Hothem |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d5716fd9f2a246adaac7c29454ade838 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-d5716fd9f2a246adaac7c29454ade8382025-01-25T00:03:32ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01512610.1109/TQE.2024.343021510603420Learning a Quantum Computer's CapabilityDaniel Hothem0https://orcid.org/0000-0002-5628-9945Kevin Young1https://orcid.org/0000-0002-4679-4542Tommie Catanach2Timothy Proctor3https://orcid.org/0000-0003-0219-8930Quantum Performance Laboratory, Sandia National Laboratories, Livermore, CA, USAQuantum Performance Laboratory, Sandia National Laboratories, Livermore, CA, USASandia National Laboratories, Livermore, CA, USAQuantum Performance Laboratory, Sandia National Laboratories, Livermore, CA, USAAccurately 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.https://ieeexplore.ieee.org/document/10603420/Benchmarkingneural networksquantum characterizationvalidationverification |
| spellingShingle | Daniel Hothem Kevin Young Tommie Catanach Timothy Proctor Learning a Quantum Computer's Capability IEEE Transactions on Quantum Engineering Benchmarking neural networks quantum characterization validation verification |
| title | Learning a Quantum Computer's Capability |
| title_full | Learning a Quantum Computer's Capability |
| title_fullStr | Learning a Quantum Computer's Capability |
| title_full_unstemmed | Learning a Quantum Computer's Capability |
| title_short | Learning a Quantum Computer's Capability |
| title_sort | learning a quantum computer x0027 s capability |
| topic | Benchmarking neural networks quantum characterization validation verification |
| url | https://ieeexplore.ieee.org/document/10603420/ |
| work_keys_str_mv | AT danielhothem learningaquantumcomputerx0027scapability AT kevinyoung learningaquantumcomputerx0027scapability AT tommiecatanach learningaquantumcomputerx0027scapability AT timothyproctor learningaquantumcomputerx0027scapability |