Large-scale sparse wave function circuit simulator for applications with the variational quantum eigensolver

Area of Science:

• Quantum Computing
• Computational Physics
• Computational Chemistry

Background:

• Parameterized quantum circuits are standard for near-term quantum simulations.
• Optimizing large quantum circuits is computationally challenging.
• The utility of large-scale circuit optimization remains largely unknown.

Purpose of the Study:

• To demonstrate classical optimization of quantum circuits for physical system simulations.
• To explore the potential of bridging classical high-performance computing with quantum advantage.
• To investigate the benefits of variational optimization on near-term quantum hardware.

Main Methods:

• Development and application of sparse wave function circuit solvers.
• Utilizing purely classical resources for approximate yet robust quantum circuit optimization.
• Testing with a unitary coupled cluster ansatz on molecules up to 64 qubits.

Main Results:

• Demonstrated a region of efficient classical simulation for quantum circuits.
• Showcased a method to avoid optimization problems in circuits with hundreds of qubits.
• Successfully applied the method to molecules with up to 64 qubits and tens of thousands of parameters.

Conclusions:

• Classical resources can effectively optimize quantum circuits, enabling exploration of near-term quantum hardware.
• Sparse wave function circuit solvers provide a pathway to quantum advantage.
• This work clarifies the benefits of variational optimization for physical system simulations on quantum computers.