# Changelog

All notable changes to [qb-compiler](https://qubitboost.io/compiler), the open-source quantum circuit compiler by [QubitBoost](https://qubitboost.io), will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

## [0.1.0] - 2026-03-13

### Added
- Core IR: QBCircuit, QBDag, QBGate, QBMeasure, QBBarrier
- Qiskit and OpenQASM 2.0 converters
- CalibrationMapper: VF2-based calibration-weighted qubit placement
- NoiseAwareRouter: Dijkstra shortest-error-path SWAP routing
- NoiseAwareScheduler: ALAP scheduling with T1/T2 urgency scoring
- GateDecomposition: Native basis decomposition (IBM ECR/RZ/SX/X, Rigetti CZ/RX/RZ, IonQ MS/GPI/GPI2, IQM CZ/PRX)
- ErrorBudgetEstimator: Pre-execution fidelity prediction
- T1 asymmetry awareness: readout-scaled penalty for qubits with high |1> decay
- Temporal correlation detection: Pearson correlation across calibration snapshots
- Calibration subsystem: StaticCalibrationProvider, CachedCalibrationProvider, BackendProperties
- Noise modelling: EmpiricalNoiseModel, FidelityEstimator
- Backend support: IBM Heron, Rigetti Ankaa, IonQ Aria/Forte, IQM Garnet/Emerald
- Cost estimation with vendor pricing
- Qiskit transpiler plugin: QBCalibrationLayout, qb_transpile(), QBPassManager
- CLI: `qbc compile`, `qbc info`, `qbc calibration show`
- Gate cancellation and commutation analysis optimisation passes
- Depth and gate count analysis passes
- ML Phase 2: XGBoost layout predictor (AUC=0.94, 454KB, +5.4% fidelity on GHZ-8)
- ML Phase 3: GNN layout predictor (dual-graph GCN, 42KB, +6.5% fidelity on QAOA-8)
- ML Phase 4: RL SWAP router (PPO actor-critic, 190KB, calibration-aware routing)
- ML training infrastructure: data generator, feature extraction, model training scripts
- 461 tests covering all passes, IR, calibration, backends, ML pipeline
- CI/CD: GitHub Actions for lint, typecheck, test matrix (Python 3.10-3.12)
- 10 example scripts demonstrating key features
- Comprehensive benchmark suite comparing all ML phases
