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Abstract Quantum computing holds transformative promise, but its realization is hindered by the inherent susceptibility of quantum computers to errors. Quantum error mitigation has proved to be an enabling way to reduce computational error in present noisy intermediate scale quantum computers. This research introduces an innovative approach to quantum error mitigation by leveraging machine learning, specifically employing adaptive neural networks. With experiment and simulations done on 127-qubit IBM superconducting quantum computer, we were able to develop and train a neural network architecture to dynamically adjust output expectation values based on error characteristics. The model leverages a prior classifier module outcome on simulated quantum circuits with errors, and the antecedent neural network regression module adapts its parameters and response to each error characteristics. Results demonstrate the adaptive neural network’s efficacy in mitigating errors across diverse quantum circuits and noise models, showcasing its potential to surpass traditional error mitigation techniques with an accuracy of 99% using the fully adaptive neural network for quantum error mitigation. This work presents a significant application of classical machine learning methods towards enhancing the robustness and reliability of quantum computations, providing a pathway for the practical realization of quantum computing technologies.
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Relaxation of stationary states on a quantum computer yields a unique spectroscopic fingerprint of the computer’s noise
Abstract Quantum computing has the potential to revolutionize computing, but its significant sensitivity to noise requires sophisticated error correction and mitigation. Traditionally, noise on the quantum device is characterized directly through qubit and gate measurements, but this approach has drawbacks in that it does not adequately capture the effect of noise on realistic multi-qubit applications. In this paper, we simulate the relaxation of stationary quantum states on a quantum computer to obtain a unique spectroscopic fingerprint of the computer’s noise. In contrast to traditional approaches, we obtain the frequency profile of the noise as it is experienced by the simulated stationary quantum states. Data from multiple superconducting-qubit IBM processors show that noise generates a bath within the simulation that exhibits both colored noise and non-Markovian behavior. Our results provide a direction for noise mitigation but also suggest how to use noise for quantum simulations of open systems.
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- Award ID(s):
- 1955907
- PAR ID:
- 10363044
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Physics
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2399-3650
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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