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Error mitigation by training with fermionic linear optics

4 Pith papers cite this work. Polarity classification is still indexing.

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abstract

Noisy intermediate-scale quantum (NISQ) computers could solve quantum-mechanical simulation problems that are beyond the capabilities of classical computers. However, NISQ devices experience significant errors which, if not corrected, can render physical quantities measured in these simulations inaccurate or meaningless. Here we describe a method of reducing these errors which is tailored to quantum algorithms for simulating fermionic systems. The method is based on executing quantum circuits in the model of fermionic linear optics, which are known to be efficiently simulable classically, to infer the relationship between exact and noisy measurement outcomes, and hence undo the effect of noise. We validated our method by applying it to the VQE algorithm for estimating ground state energies of instances of the Fermi-Hubbard model. In classical numerical simulations of 12-qubit examples with physically realistic levels of depolarising noise, errors were reduced by a factor of around 34 compared with the uncorrected case. Smaller experiments on quantum hardware demonstrate an average reduction in errors by a factor of 10 or more.

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Fermionic Averaged Circuit Eigenvalue Sampling

quant-ph · 2025-04-02 · unverdicted · novelty 6.0

FACES is a new protocol for simultaneous self-consistent learning of averaged error rates across many FLO gates with rigorously shown efficient sampling complexity via Kravchuk transformations.

Robust design under uncertainty in quantum error mitigation

quant-ph · 2023-07-11 · unverdicted · novelty 6.0

Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.

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