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

• URL: http://arxiv.org/abs/2506.23765v2
• Date: Wed, 16 Jul 2025 12:10:12 GMT
• Title: QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions
• Authors: Silvie Illésová, Tomasz Rybotycki, Martin Beseda,
• Abstract summary: We present QMetric, a Python package offering a suite of interpretable metrics to evaluate quantum circuit expressibility.<n>QMetric quantifies key aspects such as circuit fidelity, entanglement entropy, barren plateau risk, and training stability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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