Classical Preoptimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to...

Area of Science:

• Quantum computing
• Computational chemistry
• Electronic structure theory

Background:

• The Adaptive Variational Quantum Eigensolver (ADAPT-VQE) is a key algorithm for quantum chemistry simulations.
• Accurate prediction of molecular electronic energies is crucial for chemical research.
• Current methods face challenges with computational cost and scalability.

Purpose of the Study:

• To implement and evaluate the performance of ADAPT-VQE using a novel sparse wave function circuit solver (SWCS).
• To assess the accuracy and efficiency of ADAPT-VQE-SWCS for molecular systems up to 52 spin orbitals.
• To explore an alternative optimization strategy for ADAPT-VQE to reduce computational demands.

Main Methods:

• Implementation of ADAPT-VQE algorithm.
• Development and application of a tunable sparse wave function circuit solver (SWCS).
• Testing on molecular systems with up to 52 spin orbitals.
• Proposal of a preoptimization procedure using classical high-performance computing.

Main Results:

• Demonstrated accurate predictions of electronic energies for molecular systems.
• Showcased enhanced efficiency and scalability of ADAPT-VQE with SWCS.
• Validated the tunable nature of SWCS for balancing cost and accuracy.
• Established the efficacy of the proposed optimization procedure.

Conclusions:

• ADAPT-VQE combined with SWCS offers a scalable and accurate method for molecular electronic structure calculations.
• The tunable SWCS extends ADAPT-VQE applicability to larger systems and basis sets.
• The proposed optimization strategy leverages classical computing to minimize quantum hardware workload, paving the way for quantum advantage in chemistry.