Contextual Subspace Auxiliary-Field Quantum Monte Carlo: Improved Bias with Reduced Quantum Resources

Area of Science:

• Quantum Computing
• Computational Chemistry
• Many-Body Physics

Background:

• Auxiliary-field quantum Monte Carlo (AFQMC) is a powerful method for simulating strongly correlated systems.
• Reducing the computational cost and quantum resource requirements of AFQMC is crucial for its practical application.
• Hybrid quantum-classical approaches offer a promising avenue for enhancing quantum simulations.

Purpose of the Study:

• To develop a more resource-efficient hybrid quantum-classical algorithm for quantum simulations.
• To reduce the bias in auxiliary-field quantum Monte Carlo (QC-AFQMC) using trial wave functions.
• To enable accurate ground state energy computations with fewer qubits.

Main Methods:

• Decomposition of trial wave functions into classical and quantum components.
• Application of the contextual subspace projection formalism.
• Integration with the matchgate shadow protocol for efficient overlap calculation in QC-AFQMC.

Main Results:

• Demonstrated compatibility with the matchgate shadow protocol for overlap calculations.
• Achieved superior performance compared to established algorithms for ground state energy computations.
• Reached chemical precision using less than half the number of qubits compared to previous methods.

Conclusions:

• The proposed hybrid quantum-classical approach significantly reduces quantum resource requirements for QC-AFQMC.
• This method offers a more efficient pathway for accurate simulations of strongly correlated many-body systems.
• The algorithm shows promise for applications in computational chemistry and materials science.