AI-Empowered Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects

Area of Science:

• Quantum Computing
• Artificial Intelligence

Background:

• Quantum computing offers a new paradigm to address computational bottlenecks.
• Advancements in quantum processors enable sophisticated quantum algorithm development and implementation.
• Logic circuit design and quantum compilation are critical for quantum algorithm execution.

Purpose of the Study:

• To systematically review literature on integrated quantum logic circuit design and compilation optimization.
• To explore the feasibility of combining algorithmic-level design with quantum hardware considerations.
• To investigate the role of artificial intelligence in enhancing quantum computing workflows.

Main Methods:

• Comprehensive literature review of quantum logic circuit synthesis, optimization, and compilation.
• Analysis of artificial intelligence integration in quantum algorithm design and execution.
• Exploration of AI-driven quantum architecture search for parameterized quantum circuits.

Main Results:

• Artificial intelligence methods are increasingly integrated into quantum algorithms, enhancing scale and precision.
• An integrated design and optimization scheme from algorithm to hardware is feasible.
• AI can significantly reduce manual design effort and improve execution efficiency.

Conclusions:

• Integrating AI into quantum logic circuit design and compilation optimizes quantum algorithm implementation.
• AI-driven approaches facilitate the efficient execution and validation of quantum algorithms on hardware.
• This integrated scheme promises to reduce costs and enhance the overall performance of quantum computing systems.