Quantum computing for near-term applications in generative chemistry and drug discovery

Area of Science:

• Computational chemistry
• Artificial intelligence
• Quantum computing

Background:

• Machine learning and AI have transformed drug discovery and life sciences.
• Quantum computing is emerging as a transformative technology with early applications in quantum chemistry.

Purpose of the Study:

• To review near-term applications of quantum computing in generative chemistry.
• To highlight challenges addressable by noisy intermediate-scale quantum (NISQ) devices.
• To discuss integrating quantum generative systems with existing AI platforms.

Main Methods:

• Review of current literature on quantum computing applications in chemistry.
• Analysis of NISQ device capabilities for generative tasks.
• Exploration of hybrid quantum-classical AI models.

Main Results:

• Quantum computing presents significant advantages for generative chemistry simulations.
• NISQ devices can potentially address specific challenges in molecular design.
• Integration pathways exist for quantum generative systems within current AI frameworks.

Conclusions:

• Quantum computing is poised to significantly impact generative chemistry and drug discovery.
• Overcoming NISQ limitations is key to realizing quantum advantage.
• Hybrid approaches will likely bridge current quantum and AI capabilities.