Application of Quantum Computing to Biochemical Systems: A Look to the Future

Area of Science:

• Quantum computing applications in chemistry and materials science.
• Development of quantum algorithms and quantum advantage.
• Hybrid classical-quantum computational strategies.

Background:

• Near-term quantum computers have limitations requiring hybrid approaches.
• Computational chemistry and materials physics utilize multi-scale methods.
• Embedding, multi-scale, and fragment techniques split complex systems for feasible solutions.

Purpose of the Study:

• To review existing embedding, multi-scale, and fragment methods.
• To propose an embedding approach for complex biochemical systems.
• To enable the description of biochemical molecules with quantum computing.

Main Methods:

• Leveraging established computational chemistry and materials physics techniques.
• Adapting these methods for hybrid classical and quantum algorithms.
• Applying different levels of theory and computation to system parts.

Main Results:

• Demonstrated the feasibility of splitting complex systems between classical and quantum computers.
• Highlighted the potential of hybrid methods for quantum advantage in chemistry.
• Reviewed existing multi-scale approaches for complex system analysis.

Conclusions:

• Hybrid classical-quantum embedding strategies are crucial for future biochemical research.
• These methods are essential for studying active regions in molecules intractable for classical computers.
• The field is progressing towards tackling complex biochemical problems with quantum computing.