Hybrid Quantum-Classical Algorithms and Quantum Error Mitigation

By Suguru Endo, Zhenyu Cai, Simon C. Benjamin, Xiao Yuan. Journal of the Physical Society of Japan, 2020

538 citations recorded by the source corpus.

Quantum computers can exploit a Hilbert space whose dimension increases exponentially with the number of qubits. In experiment, quantum supremacy has recently been achieved by the Google team by using a noisy intermediate-scale quantum (NISQ) device with over 50 qubits. However, the question of what can be implemented on NISQ devices is still not fully explored, and discovering useful tasks for such devices is a topic of considerable interest. Hybrid quantum-classical algorithms are regarded as well-suited for execution on NISQ devices by combining quantum computers with classical computers, and are expected to be the first useful applications for quantum computing. Meanwhile, mitigation of errors on quantum processors is also crucial to obtain reliable results. In this article, we review the basic results for hybrid quantum-classical algorithms and quantum error mitigation techniques. Since quantum computing with NISQ devices is an actively developing field, we expect this review to be a useful basis for future studies.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/11c1d92eb28c20e7e27275a4a94e8bc21accd900

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Citations in the corpus

Noise-induced barren plateaus in variational quantum algorithms (2020, background): “(3) cannot be implemented as a single gate on a physical device.”
Variational quantum algorithms (2020, background): “Quantum Error Mitigation (QEM) generally suppress physical errors to expectation values of observables via classical post-processing of measurement outcomes [204].”
Challenges and opportunities in quantum machine learning (2022, background): “[103] Suguru Endo, Zhenyu Cai, Simon C Benjamin, and Xiao Yuan, “Hybrid quantum-classical algorithms and quantum error mitigation,” Journal of the Physical Society of Japan 90, 032001 (2021).”
Error mitigation with Clifford quantum-circuit data (2020, methodology): “It also includes variational quantum algorithms [3, 10, 18, 34, 40], some of which can be used to remove the effects of incoherent noise [9, 29–31, 42].”
The future of quantum computing with superconducting qubits (2022, unknown): “82 .”