QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911265386921984 |
|---|---|
| author | Illésová, Silvie Rybotycki, Tomasz Beseda, Martin |
| author_facet | Illésová, Silvie Rybotycki, Tomasz Beseda, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23765 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions Illésová, Silvie Rybotycki, Tomasz Beseda, Martin Quantum Physics Computational Physics 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. |
| title | QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2506.23765 |