Learning to Measure Quantum Neural Networks

2025-01-10

AI Summary

Existing quantum machine learning (QML) models suffer from three key bottlenecks: inefficient classical-to-quantum data encoding, complex variational circuit design, and fixed, task-agnostic measurement strategies—severely limiting classification performance. To address this, we propose, for the first time, learnable parameterized Hermitian observables, integrating quantum measurement into an end-to-end differentiable training framework. This enables joint optimization of the measurement operator and the variational quantum circuit. Our approach breaks away from conventional preset measurement paradigms, establishing a novel “measurement–circuit” co-optimization architecture. Numerical experiments on multiple benchmark datasets demonstrate that the learned observables consistently improve classification accuracy, substantiating their effectiveness in enhancing QML model performance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Quantum Machine Learning
• Quantum Circuit Complexity
• Quantum State Measurement

Innovation

• Quantum Neural Networks
• Observable Learning
• Circuit Optimization