Quantum error mitigation

Cai, Zhenyu; Babbush, Ryan; Benjamin, Simon C.; Endo, Suguru; Huggins, William J.; Li, Ying; McClean, Jarrod R.; O’Brien, Thomas E.

doi:10.1103/revmodphys.95.045005

Cited by 138 publications

(11 citation statements)

References 236 publications

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“…To assess the feasibility of our method on noisy devices, we evaluated its noise robustness by including noise models in our simulations and using zero noise extrapolation (ZNE) and state preparation and measurement (SPAM) error mitigation techniques. 36 5.1. Noise Model.…”

Section: Noise Robustness and Error Mitigationmentioning

confidence: 99%

“…The presented algorithm is based on the VQE, which is designed to be amenable to near-term quantum devices. To assess the feasibility of our method on noisy devices, we evaluated its noise robustness by including noise models in our simulations and using zero noise extrapolation (ZNE) and state preparation and measurement (SPAM) error mitigation techniques …”

Section: Noise Robustness and Error Mitigationmentioning

confidence: 99%

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Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States

Cadi Tazi,

Thom

2024

J. Chem. Theory Comput.

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The recent developments of quantum computing present novel potential pathways for quantum chemistry as the scaling of the computational power of quantum computers could be harnessed to naturally encode and solve electronic structure problems. Theoretically exact quantum algorithms for chemistry have been proposed (e.g., quantum phase estimation), but the limited capabilities of current noisy intermediate-scale quantum devices motivated the development of less demanding hybrid algorithms. In this context, the variational quantum eigensolver (VQE) algorithm was successfully introduced as an effective method to compute the ground-state energies of small molecules. This study investigates the folded spectrum (FS) method as an extension of the VQE algorithm for the computation of molecular excited states. It provides the possibility of directly computing excited states around a selected target energy using the same or quantum circuit as for the ground-state calculation. Inspired by the variance-based methods from the quantum Monte Carlo literature, the FS method minimizes the energy variance, thus, in principle, requiring a computationally expensive squared Hamiltonian to be applied. We alleviate this potentially poor scaling by employing a Pauli grouping procedure to identify sets of commuting Pauli strings that can be evaluated simultaneously. This allows for a significant reduction in the computational cost. We applied the FS-VQE method to small molecules (H 2 , LiH), obtaining all electronic excited states with chemical accuracy on ideal quantum simulators. Furthermore, we explore the application of quantum error mitigation techniques, demonstrating improved energy accuracy on noisy simulators compared with simulations without mitigation.

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Section: Noise Robustness and Error Mitigationmentioning

confidence: 99%

Section: Noise Robustness and Error Mitigationmentioning

confidence: 99%

Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States

Cadi Tazi,

Thom

2024

J. Chem. Theory Comput.

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“…As the number of qubits and operators in the circuit increases, the likelihood of errors in the quantum computer increases. Although considerable effort has been dedicated to error mitigation, a definitive solution remains elusive . In this study, we limited the number of qubits used in the model to avoid the error issue that occurs as the number of qubits increases and introduced a hybrid QNN, an innovative fusion of a QNN and an ANN, tailored to harness the power of a limited number of qubits in the catalysis field.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Roh,

Oh,

Lee

et al. 2024

ACS Sustainable Chem. Eng.

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Machine learning (ML), which has been increasingly applied to complex problems such as catalyst development, encounters challenges in data collection and structuring. Quantum neural networks (QNNs) outperform classical ML models, such as artificial neural networks (ANNs), in prediction accuracy, even with limited data. However, QNNs have limited available qubits. To address this issue, we introduce a hybrid QNN model, combining a parametrized quantum circuit with an ANN structure. We used the catalyst data sets of the dry reforming of methane reaction from the literature and in-house experimental results to compare the hybrid QNN and the ANN models. The hybrid QNN exhibited superior prediction accuracy and a faster convergence rate, achieving an R 2 of 0.942 at 2478 epochs, whereas the ANN achieved an R 2 of 0.935 at 3175 epochs. For the 224 in-house experimental data points previously unreported in the literature, the hybrid QNN exhibited an enhanced generalization performance. It showed a mean absolute error (MAE) of 13.42, compared with an MAE of 27.40 for the ANN under similar training conditions. This study highlights the potential of the hybrid QNN as a powerful tool for solving complex problems in catalysis and chemistry, demonstrating its advantages over classical ML models.

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“…This implies that we are not fully utilizing the capacity of quantum subspace methods [36][37][38][39][40][41][42] or quantum algorithms that utilize error mitigation techniques such as the symmetry expansion [43][44][45], and furthermore early fault-tolerant quantum computers with quantum error detection methods [46][47][48][49]. Considering that such post-processing techniques and error mitigation methods are envisioned to remain crucial for both near-term and fault-tolerant quantum devices [50], it is critical to overcome the above issue to fully harness their computational powers.…”

Section: Introductionmentioning

confidence: 99%

Adaptive measurement strategy for quantum subspace methods

Nakamura,

Yano,

Yoshioka

2024

New J. Phys.

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Estimation of physical observables for unknown quantum states is an important problem that underlies a wide range of fields, including quantum information processing, quantum physics, and quantum chemistry. In the context of quantum computation, in particular, existing studies have mainly focused on holistic state tomography or estimation on specific observables with known classical descriptions, while this lacks the important class of problems where the estimation target itself relies on the measurement outcome. In this work, we propose an adaptive measurement optimization method that is useful for the quantum subspace methods, namely the variational simulation methods that utilize classical postprocessing on measurement outcomes. The proposed method first determines the measurement protocol for classically simulatable states, and then adaptively updates the protocol of quantum subspace expansion (QSE) according to the quantum measurement result. As a numerical demonstration, we have shown for excited-state simulation of molecules that (i) we are able to reduce the number of measurements by an order of magnitude by constructing an appropriate measurement strategy (ii) the adaptive iteration converges successfully even for a strongly correlated molecule of H4. Our work reveals that the potential of the QSE method can be empowered by elaborated measurement protocols, and opens a path to further pursue efficient quantum measurement techniques in practical computations.

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Quantum error mitigation

Cited by 138 publications

References 236 publications

Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States

Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Adaptive measurement strategy for quantum subspace methods

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