Local Gradient Optimization of Modular Entangling Sequences

TL;DR

This paper introduces a gradient-based optimization method to improve the fidelity of entangling gates in quantum computing by suppressing errors through interleaved local rotations, applicable to various noise types.

Contribution

The authors develop a modular, gradient-based optimization technique for local rotations that enhances entangling gate fidelity under different noise conditions, adaptable to any two-qubit system.

Findings

Effective suppression of logical errors with local rotations.

Applicable to static and dynamic noise environments.

Fidelity depends on local rotation accuracy and noise level.

Abstract

Implementation of logical entangling gates is an important step towards realizing a quantum computer. We use a gradient-based optimization approach to find single-qubit rotations which can be interleaved between applications of a noisy nonlocal gate to dramatically suppress arbitrary logical errors, while steering the evolution operator towards the perfectly entangling subset of SU(4) gates. The modularity of the approach allows for application to any two-qubit system, regardless of the Hamiltonian or details of the experimental implementation. This approach is effective for both quasi-static and time-dependent $1/f^{\alpha}$ noise. We also show how the fidelity of the final operation depends on both the fidelity of the local rotations and the noise strength.