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Control of noisy quantum systems: Field theory approach to error mitigation (1512.05477v2)

Published 17 Dec 2015 in quant-ph, cond-mat.mes-hall, and cond-mat.quant-gas

Abstract: We consider the quantum-control task of obtaining a target unitary operation via control fields that couple to the quantum system and are chosen to best mitigate errors resulting from time-dependent noise. We allow for two sources of noise: fluctuations in the control fields and those arising from the environment. We address the issue of error mitigation by means of a formulation rooted in the Martin-Siggia-Rose (MSR) approach to noisy, classical statistical-mechanical systems. We express the noisy control problem in terms of a path integral, and integrate out the noise to arrive at an effective, noise-free description. We characterize the degree of success in error mitigation via a fidelity, which characterizes the proximity of the sought-after evolution to ones achievable in the presence of noise. Error mitigation is then accomplished by applying the optimal control fields, i.e., those that maximize the fidelity subject to any constraints obeyed by the control fields. To make connection with MSR, we reformulate the fidelity in terms of a Schwinger-Keldysh (SK) path integral, with the added twist that the forward' andbackward' branches of the time-contour are inequivalent with respect to the noise. The present approach naturally allows the incorporation of constraints on the control fields; a useful feature in practice, given that they feature in real experiments. We illustrate this MSR-SK approach by considering a system consisting of a single spin $s$ freedom (with $s$ arbitrary), focusing on the case of $1/f$ noise. We discover that optimal error-mitigation is accomplished via a universal control field protocol that is valid for all $s$, from the qubit (i.e., $s=1/2$) case to the classical (i.e., $s \to \infty$) limit. In principle, this MSR-SK approach provides a framework for addressing quantum control in the presence of noise for systems of arbitrary complexity.

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