Quantum circuit synthesis with diffusion models

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.

Code available at: https://github.com/FlorianFuerrutter/genQC

References in corpus (9)

• Learning Transferable Visual Models From Natural Language Supervision
• Challenges and Opportunities in Quantum Machine Learning
• Prompt-to-Prompt Image Editing with Cross Attention Control
• Make-A-Video: Text-to-Video Generation without Text-Video Data
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
• The structure of multidimensional entanglement in multipartite systems
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Fermionic quantum processing with programmable neutral atom arrays
• Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning

Cited by in corpus (9)

• Artificial Intelligence for Quantum Computing
• Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks
• Machine-Learning Insights into the Entanglement-trainability Correlation of Parameterized Quantum Circuits
• Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
• Artificial intelligence for representing and characterizing quantum systems
• Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection
• Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders