A semi-agnostic ansatz with variable structure for quantum machine learning

TL;DR

This paper introduces VAns, a variable-structure ansatz for VQAs that adaptively grows and prunes quantum circuits to improve trainability and noise resilience in quantum machine learning applications.

Contribution

The paper proposes VAns, a novel method for dynamically adjusting quantum circuit structures during training to enhance performance and mitigate noise in variational quantum algorithms.

Findings

VAns improves trainability in VQAs.

VAns reduces circuit depth, mitigating noise effects.

Successful applications in quantum chemistry and data compression.

Abstract

Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.