Learning Orthogonal Random Unitary Channels with Contracted Quantum Approaches and Simplex Optimization

TL;DR

This paper introduces a quantum learning method for random unitary channels using Pauli measurements and simplex optimization, enabling efficient characterization of noise models in quantum computing.

Contribution

It presents a novel quantum learning procedure for orthogonal random unitary channels employing contracted quantum approaches and multi-objective minimization.

Findings

Effective learning of random unitary channels demonstrated.

Method scales with noise levels and sparsity.

Applicable to noise modeling and quantum error mitigation.

Abstract

Random (mixed) unitary channels describe an important subset of quantum channels, which are commonly used in quantum information, noise modeling, and quantum error mitigation. Despite their usefulness, there is substantial complexity in characterizing or identifying generic random unitary channels. We present a procedure for learning a class of random unitary channels on orthogonal unitary bases on a quantum computer utilizing Pauli learning and a contracted quantum learning procedure. Our approach involves a multi-objective, Pauli- and unitary-based minimization, and allows for learning locally equivalent channels. We demonstrate our approach for varying degrees of noise and investigate the scalability of these approaches, particularly with sparse noise models.