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
Posts
We Got AI Agents to Train RL Models for Quantum Transpilation
A new MCP server that enables AI agents to autonomously train reinforcement learning models for quantum circuit synthesis - including permutation, linear function, and Clifford circuits.
qiskit-ibm-transpiler Ranks #3 in Unitary Foundation 2025 Survey
The qiskit-ibm-transpiler library advanced to
#3 in the Unitary Foundation 2025 Survey for full-stack development platforms, surpassing 929K downloads in under two years. AI transpiler passes now run in local mode without requiring an IBM Quantum premium plan.
qiskit-ibm-transpiler Ranks #4 in Unitary Fund 2024 Survey
Celebrating qiskit-ibm-transpiler recognition as the 4th most used quantum computing development tool globally in the 2024 Unitary Fund survey. A remarkable achievement for a project with just 1 year of public existence, combining Qiskit heuristic algorithms with novel AI transpiler passes.
Publications
Intelligent unitary synthesis for quantum computing
Systems and techniques that facilitate intelligent unitary synthesis for quantum computing are provided. For example, a system can access a …
AI Methods for Permutation Circuit Synthesis Across Generic Topologies
This paper investigates artificial intelligence (AI) methodologies for the synthesis and transpilation of permutation circuits across …
Reinforcement learning based transpilation of quantum circuits
Systems and techniques that facilitate quantum circuit transpiling are provided. For example, one or more embodiments described herein can …
Pauli Network Circuit Synthesis with Reinforcement Learning
We introduce a Reinforcement Learning (RL)-based method for re-synthesis of quantum circuits containing arbitrary Pauli rotations alongside …
Reinforcement Learning based Clifford Circuit Synthesis
Systems and techniques that facilitate Clifford circuit synthesis are provided. For example, one or more embodiments described herein can …
Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning
This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the …