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Error mitigation by training with fermionic linear optics
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Error mitigation by training with fermionic linear optics
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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.
Forward citations
Cited by 4 Pith papers
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Enabling Lie-Algebraic Classical Simulation beyond Free Fermions
New Pauli orbit and modified Gell-Mann bases enable polynomial-cost Lie-algebraic simulation for permutation-equivariant and bounded-excitation quantum dynamics.
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Feynman's clock and hierarchy-informed sampling for quantum error mitigation
Feynman's clock maps arbitrary circuits onto Hamiltonian dynamics whose BBGKY hierarchy enables polynomial-overhead, controllable error mitigation via informed sampling.
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Fermionic Averaged Circuit Eigenvalue Sampling
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.
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Robust design under uncertainty in quantum error mitigation
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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