Artificial intelligence for quantum computing

Area of Science:

• Computer Science
• Quantum Information Science

Background:

• Artificial intelligence (AI) has significantly impacted various applications.
• Quantum computing (QC) presents complex challenges due to its counterintuitive nature and high-dimensional mathematics.
• AI's data-driven learning capabilities are well-suited to address QC's complexities.

Purpose of the Study:

• To review the application of state-of-the-art AI techniques in advancing quantum computing (QC).
• To encourage cross-pollination between AI and QC expertise.
• To examine future opportunities and obstacles for AI in QC.

Main Methods:

• Review of current AI techniques applied to QC challenges.
• Analysis of AI's impact across the QC hardware and software stack.
• Exploration of AI's role in QC device design and applications.

Main Results:

• AI is actively addressing significant challenges in quantum computing (QC) development.
• AI techniques are enhancing both the hardware and software aspects of QC.
• AI contributes to areas from quantum device design to the development of QC applications.

Conclusions:

• The integration of AI is crucial for overcoming major scaling challenges in quantum computing (QC).
• Continued advancements in AI offer substantial opportunities for the future of QC.
• Addressing interdisciplinary expertise gaps is key for realizing AI's full potential in QC.