Quantum Resource Analysis and Benchmarking

Frameworks like PennyLane and Qiskit handle circuit construction and simulation well but provide almost no tools for measuring the quantum resources a circuit uses. No unified library exists to assess profiling entanglement, magic, or Bell nonlocality. I propose a PennyLane-based library computing four resource classes through a single resource_profile() API: entanglement (Meyer–Wallach, Von Neumann entropy, concurrence), magic (Stabilizer Rényi Entropy), mana (discrete Wigner negativity for qudits), and Bell nonlocality (CHSH values). Since PennyLane QNodes are differentiable, the measures inherit gradient access for tracking resource shifts during training. I also propose benchmarking across QAOA for MaxCut, Quantum Phase Estimation, VQE for molecular ground states (H₂, LiH), and quantum autoencoders for BSM event detection following Duffy et al. (arXiv:2407.07961). The study would record resource trajectories per epoch and test whether the resource decrease Duffy et al. observed generalizes, via Spearman correlations and Mann–Kendall trend tests. This project grows out of my prior work. My Quantum Encoding Atlas library (DOI: 10.5281/zenodo.18780936) benchmarks 16 encodings across PennyLane, Qiskit, and Cirq, and my 12 QMLHEP15 evaluation tasks span quantum circuits, GNNs, equivariant QNNs, RL for PQC optimization, and contrastive learning. Running those benchmarks I identified SRE as a missing diagnostic, which inspired me towards this project. Deliverables: 1. Python package on PyPI with entanglement, magic, mana, and nonlocality modules. 2. Circuit profiler for epoch-level resource tracking. 3. Benchmarking dataset for all four algorithms. 4. Resource–performance correlation analysis. 5. API docs and Jupyter notebook tutorials. 6. Two blog posts per ML4SCI requirements. Stretch: tensor network SRE approximations beyond 10 qubits and integration with Knipfer et al.'s agent-based VQC design (arXiv:2602.19387).