Quantum circuit synthesis with diffusion models

TL;DR

This paper introduces the use of denoising diffusion models to improve quantum circuit synthesis, enabling efficient generation and editing of quantum circuits without classical simulation bottlenecks.

Contribution

It pioneers the application of diffusion models in quantum circuit synthesis, allowing for flexible, conditioned generation and editing of quantum circuits.

Findings

Successfully generated quantum circuits for entanglement and unitary tasks

Supported circuit editing and device constraint alignment

Sidestepped exponential classical simulation overhead

Abstract

Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text-conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics -- a consistent bottleneck in preceding ML techniques. We demonstrate the model's capabilities across two tasks: entanglement generation and unitary compilation. The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device. Given their flexibility and generalization abilities, we envision DMs as pivotal in quantum circuit synthesis, enhancing both practical applications but also insights into theoretical quantum computation.