Model-Based Reinforcement Learning for Quantum Circuit Synthesis

Quantum circuit synthesis is a compilation primitive that constructs implementations of algorithms based on functional descriptions. These tools can discover new implementations of algorithms and are powerful circuit optimizers. The run time required to synthesize quantum circuits grows exponentially with the number of qubits. Much of this run time is used to search for parameterized circuit templates or ansatzes that define the structure of the synthesized circuit. Past work has shown how Machine Learning (ML) can be used to accelerate this process by providing seed circuits from which to start synthesis. This work addresses scalability issues present in this previous work. Instead of enumerating possible output circuits, this work demonstrates how techniques in generative ML can be used to produce seed circuit templates.