Simulations of frustrated Ising Hamiltonians using quantum approximate optimization.

Area of Science:

• Condensed Matter Physics
• Quantum Computing
• Materials Science

Background:

• Understanding magnetic materials is crucial for technological advancement.
• Computational complexity limits traditional methods for calculating ground-state properties.
• Near-term quantum computers offer a potential solution for these complex calculations.

Purpose of the Study:

• To investigate the use of the quantum approximate optimization algorithm (QAOA) for preparing ground states of magnetic materials.
• To explore the feasibility of QAOA on near-term quantum hardware for materials science applications.

Main Methods:

• Studied classical Ising spin models on various lattice structures (square, Shastry-Sutherland, triangular).
• Employed the quantum approximate optimization algorithm (QAOA) on simulated and trapped-ion quantum computers.
• Analyzed the relationship between QAOA success probability and ground-state structure.

Main Results:

• Established a correlation between QAOA success probability and ground-state properties.
• Demonstrated that a modest number of measurements are sufficient to determine ground states, even with magnetic frustration.
• Successfully recovered ground states of a Shastry-Sutherland unit cell on a trapped-ion quantum computer with high accuracy.

Conclusions:

• QAOA is a viable method for preparing ground states of magnetic materials, particularly in the frustrated Ising limit.
• This work represents a significant step towards using quantum computation for systematic materials understanding.
• Quantum advantage may be essential for tackling larger and more complex material Hamiltonians in the future.