July 19，2024 -- Research published earlier this month in the science journal Nature used NVIDIA-powered supercomputers to validate a pathway toward the commercialization of quantum computing. The research, led by Nobel laureate Giorgio Parisi and Massimo Bernaschi, director of technology at the National Research Council of Italy and a CUDA Fellow, focuses on quantum annealing, a method that may one day tackle complex optimization problems that are extraordinarily challenging to conventional computers.

To conduct their research, the team utilized 2 million GPU computing hours at the Leonardo facility (Cineca, in Bologna, Italy), nearly 160,000 GPU computing hours on the Meluxina-GPU cluster, in Luxembourg, and 10,000 GPU hours from the Spanish Supercomputing Network. Additionally, they accessed the Dariah cluster, in Lecce, Italy.

They used these state-of-the-art resources to simulate the behavior of a certain kind of quantum computing system known as a quantum annealer. Quantum computers fundamentally rethink how information is computed to enable entirely new solutions.

Unlike classical computers, which process information in binary — 0s and 1s — quantum computers use quantum bits or qubits that can allow information to be processed in entirely new ways. Quantum annealers are a special type of quantum computer that, though not universally useful, may have advantages for solving certain types of optimization problems.

The paper, “The Quantum Transition of the Two-Dimensional Ising Spin Glass,” represents a significant step in understanding the phase transition — a change in the properties of a quantum system — of Ising spin glass, a disordered magnetic material in a two-dimensional plane, a critical problem in computational physics.

The paper addresses the problem of how the properties of magnetic particles arranged in a two-dimensional plane can abruptly change their behavior. The study also shows how GPU-powered systems play a key role in developing approaches to quantum computing. GPU-accelerated simulations allow researchers to understand the complex systems’ behavior in developing quantum computers, illuminating the most promising paths forward.

Quantum annealers, like the systems developed by the pioneering quantum computing company D-Wave, operate by methodically decreasing a magnetic field that is applied to a set of magnetically susceptible particles. When strong enough, the applied field will act to align the magnetic orientation of the particles — similar to how iron filings will uniformly stand to attention near a bar magnet.If the strength of the field is varied slowly enough, the magnetic particles will arrange themselves to minimize the energy of the final arrangement.

Finding this stable, minimum-energy state is crucial in a particularly complex and disordered magnetic system known as a spin glass since quantum annealers can encode certain kinds of problems into the spin glass’s minimum-energy configuration. Finding the stable arrangement of the spin glass then solves the problem. Understanding these systems helps scientists develop better algorithms for solving difficult problems by mimicking how nature deals with complexity and disorder.

That’s crucial for advancing quantum annealing and its applications in solving extremely difficult computational problems that currently have no known efficient solution — problems that are pervasive in fields ranging from logistics to cryptography.

Unlike gate-model quantum computers, which operate by applying a sequence of quantum gates, quantum annealers allow a quantum system to evolve freely in time. This is not a universal computer — a device capable of performing any computation given sufficient time and resources — but may have advantages for solving particular sets of optimization problems in application areas such as vehicle routing, portfolio optimization and protein folding.

Through extensive simulations performed on NVIDIA GPUs, the researchers learned how key parameters of the spin glasses making up quantum annealers change during their operation, allowing a better understanding of how to use these systems to achieve a quantum speedup on important problems.