AI-powered quantum circuit optimization that outperforms traditional heuristics. Uses reinforcement learning to achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits—orders of magnitude faster than SAT solvers. Our Pauli Network synthesis delivers over 2× reduction in two-qubit gate count, with average improvements of 20% and up to 60% on the Benchpress benchmark.
Supports hardware-aware routing up to 133 qubits and works as a drop-in replacement for standard Qiskit transpilation. Available as both local execution (with our open-source RL models) and cloud-based services.
Achievements:
- #3 in Unitary Foundation 2025 Survey (full-stack platforms)
- #4 in Unitary Fund 2024 Survey (after just 1 year public)
- 929K+ downloads • 56 releases • Apache 2.0