Noise injection into each parameterized Pauli gate exponentially suppresses high-frequency Fourier components of a quantum loss function, smoothing the landscape and improving optimization quality in numerical tests.
Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. Practical implementations of these algorithms, despite offering certain levels of robustness against systematic errors, show a decline in performance due to the presence of stochastic errors and limited coherence time. In this work, we develop a recipe for mitigating quantum gate errors for variational algorithms using zero-noise extrapolation. We introduce an experimentally amenable method to control error strength in the circuit. We utilize the fact that gate errors in a physical quantum device are distributed inhomogeneously over different qubits and qubit pairs. As a result, one can achieve different circuit error sums based on the manner in which abstract qubits in the circuit are mapped to a physical device. We find that the estimated energy in the variational approach is approximately linear with respect to the circuit error sum (CES). Consequently, a linear fit through the energy-CES data, when extrapolated to zero CES, can approximate the energy estimated by a noiseless variational algorithm. We demonstrate this numerically and investigate the applicability range of the technique.
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quant-ph 1years
2025 1verdicts
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Regularizing quantum loss landscapes by noise injection
Noise injection into each parameterized Pauli gate exponentially suppresses high-frequency Fourier components of a quantum loss function, smoothing the landscape and improving optimization quality in numerical tests.