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Summary

This summary is machine-generated.

Classical computers can optimize quantum circuits for simulating physical systems, overcoming challenges with large-scale circuit optimization. This approach bridges high-performance computing with quantum advantage, enabling near-term quantum hardware exploration.

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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.