Q-Fusion: Diffusing Quantum Circuits
This addresses the need for automated quantum circuit design to reduce manual effort in quantum computing, though it appears incremental as it builds on existing Quantum Architecture Search techniques.
The paper tackles the laborious design of quantum algorithms for NISQ devices by proposing a diffusion-based method to automatically generate quantum circuits, achieving 100% valid circuit outputs.
Quantum computing holds great potential for solving socially relevant and computationally complex problems. Furthermore, quantum machine learning (QML) promises to rapidly improve our current machine learning capabilities. However, current noisy intermediate-scale quantum (NISQ) devices are constrained by limitations in the number of qubits and gate counts, which hinder their full capabilities. Furthermore, the design of quantum algorithms remains a laborious task, requiring significant domain expertise and time. Quantum Architecture Search (QAS) aims to streamline this process by automatically generating novel quantum circuits, reducing the need for manual intervention. In this paper, we propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits. This method contrasts with other approaches that utilize large language models (LLMs), reinforcement learning (RL), variational autoencoders (VAE), and similar techniques. Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.