Learning to Measure Quantum Neural Networks

TL;DR

This paper presents a novel end-to-end differentiable framework for learning the measurement observables in quantum neural networks, enhancing their performance and accuracy.

Contribution

It introduces a learnable observable approach that jointly trains measurement operators with quantum circuit parameters, addressing a key overlooked aspect in QML.

Findings

Improved classification accuracy in numerical simulations.

Learnable observables outperform fixed measurement protocols.

Enhanced QML model performance through adaptive measurement learning.

Abstract

The rapid progress in quantum computing (QC) and machine learning (ML) has attracted growing attention, prompting extensive research into quantum machine learning (QML) algorithms to solve diverse and complex problems. Designing high-performance QML models demands expert-level proficiency, which remains a significant obstacle to the broader adoption of QML. A few major hurdles include crafting effective data encoding techniques and parameterized quantum circuits, both of which are crucial to the performance of QML models. Additionally, the measurement phase is frequently overlooked-most current QML models rely on pre-defined measurement protocols that often fail to account for the specific problem being addressed. We introduce a novel approach that makes the observable of the quantum system-specifically, the Hermitian matrix-learnable. Our method features an end-to-end differentiable learning framework, where the parameterized observable is trained alongside the ordinary quantum circuit parameters simultaneously. Using numerical simulations, we show that the proposed method can identify observables for variational quantum circuits that lead to improved outcomes, such as higher classification accuracy, thereby boosting the overall performance of QML models.