QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions

TL;DR

QMetric is a Python toolkit that provides interpretable metrics to evaluate quantum neural networks' expressibility, training dynamics, and feature representations, aiding in benchmarking their effectiveness beyond accuracy.

Contribution

It introduces a comprehensive, easy-to-use benchmarking package for quantum neural networks, integrating multiple metrics and supporting popular quantum and machine learning frameworks.

Findings

QMetric effectively quantifies circuit fidelity and entanglement entropy.

It assesses barren plateau risk and training stability.

Demonstrated on quantum-enhanced MNIST classification.

Abstract

As hybrid quantum-classical models gain traction in machine learning, there is a growing need for tools that assess their effectiveness beyond raw accuracy. We present QMetric, a Python package offering a suite of interpretable metrics to evaluate quantum circuit expressibility, feature representations, and training dynamics. QMetric quantifies key aspects such as circuit fidelity, entanglement entropy, barren plateau risk, and training stability. The package integrates with Qiskit and PyTorch, and is demonstrated via a case study on binary MNIST classification comparing classical and quantum-enhanced models. Code, plots, and a reproducible environment are available on GitLab.