Error mitigation with Clifford quantum-circuit data
Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{nois...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2021-11-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2021-11-26-592/pdf/ |
| _version_ | 1818341693928243200 |
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| author | Piotr Czarnik Andrew Arrasmith Patrick J. Coles Lukasz Cincio |
| author_facet | Piotr Czarnik Andrew Arrasmith Patrick J. Coles Lukasz Cincio |
| author_sort | Piotr Czarnik |
| collection | DOAJ |
| description | Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator. |
| first_indexed | 2024-12-13T16:02:51Z |
| format | Article |
| id | doaj.art-d13fd1910b05422d996c43b26d729e77 |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-12-13T16:02:51Z |
| publishDate | 2021-11-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-d13fd1910b05422d996c43b26d729e772022-12-21T23:39:08ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2021-11-01559210.22331/q-2021-11-26-59210.22331/q-2021-11-26-592Error mitigation with Clifford quantum-circuit dataPiotr CzarnikAndrew ArrasmithPatrick J. ColesLukasz CincioAchieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.https://quantum-journal.org/papers/q-2021-11-26-592/pdf/ |
| spellingShingle | Piotr Czarnik Andrew Arrasmith Patrick J. Coles Lukasz Cincio Error mitigation with Clifford quantum-circuit data Quantum |
| title | Error mitigation with Clifford quantum-circuit data |
| title_full | Error mitigation with Clifford quantum-circuit data |
| title_fullStr | Error mitigation with Clifford quantum-circuit data |
| title_full_unstemmed | Error mitigation with Clifford quantum-circuit data |
| title_short | Error mitigation with Clifford quantum-circuit data |
| title_sort | error mitigation with clifford quantum circuit data |
| url | https://quantum-journal.org/papers/q-2021-11-26-592/pdf/ |
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