Improving Quantum Circuit Synthesis with Machine Learning
This work addresses the bottleneck of exponentially growing run times in quantum circuit synthesis for the NISQ era, offering incremental improvements in speed and generalization.
The paper tackles the problem of slow unitary synthesis algorithms for quantum circuits by introducing QSeed, a machine learning-based method that speeds up synthesis by 3.7x for a 64-qubit modular exponentiation circuit while maintaining low gate counts.
In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that minimize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of $3.7\times$ in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process.