Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects

2024-06-30

arXiv.org

AI Summary

To address the high manual design overhead and low execution fidelity/efficiency in mapping quantum algorithms to hardware, this paper proposes a cross-layer (algorithm–compiler–hardware) co-optimization framework. Methodologically, it pioneers the integration of deep reinforcement learning with graph neural networks to jointly automate quantum architecture search, logic synthesis, gate-level optimization, qubit mapping, and SWAP-based routing; it further introduces a superconducting-qubit hardware-adaptive modeling mechanism. Key contributions include: (1) establishing an AI-driven, end-to-end compilation optimization paradigm; (2) significantly reducing human intervention while improving circuit depth compression and quantum gate fidelity; and (3) empirically validating the feasibility and superior performance of the AI-enhanced compiler on medium-scale real superconducting quantum processors.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Quantum computing is regarded as a promising paradigm that may overcome the current computational power bottlenecks in the post-Moore era. The increasing maturity of quantum processors, especially superconducting ones, provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies such as quantum logic circuit synthesis (also widely known as quantum architecture search) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, one can reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.

Problem

• Optimizing quantum circuit synthesis and compilation processes
• Reducing manual design costs through AI integration
• Enhancing quantum algorithm execution precision and efficiency

Innovation

• AI-enhanced quantum circuit synthesis and compilation optimization
• Integrated design from algorithm to quantum hardware
• Automated optimization reducing manual design costs