Quantum Computing Advances: Breakthroughs in Quantum Annealing

Quantum computing is a multidisciplinary field leveraging quantum mechanics to process information more efficiently than classical computers. A promising application of this technology is quantum annealing, an optimization technique that utilizes quantum computing to solve complex problems by finding the minimum energy of a function.

Spin glasses, complex disordered systems influenced by quantum effects, are instrumental in this research.

Understanding the critical phase of a quantum spin glass—when its behaviour drastically changes with varying magnetic fields—is of significant theoretical and practical interest. For over twenty years, contrasting theories have debated this phenomenon, which is crucial for assessing whether quantum annealing can effectively solve combinatorial optimization problems like the travelling salesman problem, which consists of finding the shortest route to visit a series of cities once each until returning to the starting point.

To address these questions, Nobel Prize winner Giorgio Parisi from Sapienza University of Rome, Massimo Bernaschi of the National Research Council (CNR-IAC), Isidoro González-Adalid, and Víctor Martín-Mayor of the Complutense University of Madrid developed advanced simulation techniques. Their results, published in Nature, indicate that quantum annealing can find optimal solutions under specific conditions.

Their novel approach allowed for unprecedented precision in measuring system characteristics such as the energy gap, the difference between the minimum energy state and the first excited state, that  controls the complexity of solving a particular problem instance in a quantum annealer. Mutually contradicting predictions exist about how this gap will behave. Unfortunately, which of these alternatives, provides the correct picture is a very hard computational task.

Achieving these results required approximately seven million computing hours provided by the EuroHPC initiative, utilizing two of Europe’s largest computing facilities: Leonardo at CINECA in Italy and MeluXina in Luxembourg.

The main objective of the project was to employ custom CUDA codes. These codes need to be run on a large High Performance Computing facility such as Leonardo where a large number of high-end GPUs are available to the European Scientific Community.

Thanks to this combination of highly optimized codes and enormous computational power, the team reached unprecedented accuracy and reliable statistics that will deliver a clearer perspective than previously thought possible.

The complete Press Release on the CNR Web Page