Routing quantum circuits with AlphaZero deep exploration (Part 2/2)

Compiling a quantum circuit for specific quantum hardware is a challenging problem, since current quantum processing units (QPUs) generally have low connectivity between physical qubits and limited coherence time. To make optimal use of these constrained resources and to ensure that the quantum circuit is executable on the target QPU, a circuit-transformation process with low depth overhead is essential. Due to the large search space for such circuit transformations, coupled with a high branching factor, the majority of existing algorithms tend to conduct only superficial searches, often resulting in solutions that are at best locally optimal. We propose an AlphaZero-inspired algorithm for systematically averting this limitation.

In this second part of the talk, we describe how our method employs a transformer neural network in conjunction with deep lookahead in a guided tree search, which allows for searching deeper and attempting to find better solutions than existing algorithms do. Using the transformer architecture in combination with graph neural networks, our algorithm is hardware-agnostic. We present benchmarks of our method compared to other algorithms.