v1.0.0 — PIC1 submission

Initial release of the PQC-HEP binary classifier, submitted as part of PIC1.

What's included

• Four complete implementations of a 4-qubit parametrized quantum circuit (PQC) binary classifier for HEP signal/background separation, across Qiskit and PyQuil (Gram-Schmidt, QR, and Pytket embedding variants).
• 19 variational circuit templates following Figure 2 of Sim et al. (2019), with 4–28 trainable parameters per layer.
• Exact parameter-shift gradients for all gate types: two-term rule for Rx/Ry/Rz and four-term rule (Anselmetti et al. 2021) for CRx/CRz, automatically selected per parameter.
• Real hardware support: IBM Quantum via qiskit-ibm-runtime SamplerV2 (Qiskit), Rigetti QPU via native-gate rebase (Pytket).
• Mini-batch gradient descent with Adam optimiser, weighted binary cross-entropy loss, and physics event weight normalisation.
• Standalone classifier.py output with trained parameters baked in.

Requirements

See README for full installation instructions.