Optimal and robust error filtration for quantum information processing

TL;DR

This paper develops an optimal error filtration scheme for quantum information that uses entangling gates and auxiliary qubits to reduce noise, outperforming some existing methods even under imperfections.

Contribution

It introduces a method to optimize entangling unitaries for effective noise mitigation in quantum systems, including analytical solutions and robustness analysis.

Findings

Optimized entangling gates significantly reduce noise effects.

Adding more ancillary qubits enhances protection against noise.

Our scheme outperforms SQEM in various noise regimes.

Abstract

Error filtration is a hardware scheme that mitigates noise by exploiting auxiliary qubits and entangling gates. Although both signal and ancillas are subject to local noise, constructive interference(and in some cases post-selection) allows us to reduce the noise level in the signal qubit. Here we determine the optimal entangling unitary gates that make the qubits interfere most effectively,starting from a set of universal gates and proceeding by optimizing suitable functionals by gradient-descent or stochastic approximation. We examine how our optimized scheme behaves under imperfect implementation, where ancillary qubits may be noisy or subject to cross-talk. Even with these imperfections, we find that adding more ancillary qubits helps in protecting quantum information . We benchmark our approach against figures of merit that correspond to different applications, including entanglement fidelity, quantum Fisher information (for applications in quantum sensing),and CHSH value (for cryptographic applications), with one, two, and three ancillary qubits. With one and two ancillas we also provide analytical explicit expressions from an ansatz for the optimal unitary. We also compare our method with the recently introduced Superposed Quantum Error Mitigation (SQEM) scheme based on superposition of causal orders, and show that, for a wide range of noise strengths, our approach may outperform SQEM in terms of effectiveness and robustness.