Scientists are developing innovative ways to benchmark the potential of quantum computing in solving complex scientific problems, particularly in understanding material systems. The research, led by physicist Giuseppe Carleo at the Swiss Federal Institute of Technology, introduces a novel approach to comparing classical and quantum computational methods for tackling challenging physics problems.
At the heart of this research is the “many-body problem,” a fundamental challenge in physics where predicting the behavior of multiple interacting particles becomes extraordinarily complex. While quantum mechanics theoretically provides the tools to predict particle interactions, practical calculations become nearly impossible when dealing with multiple particles in complex molecular or crystal structures.
The researchers developed a metric called the V-score, which evaluates computational algorithms’ effectiveness in calculating a material’s ground state – its lowest possible energy level. This metric combines two critical measurements: the calculated ground state energy and its fluctuations. By creating a comprehensive library of ground-state simulations using different techniques, the team could rigorously assess various computational approaches.
Their findings reveal fascinating insights into computational complexity across different material structures. One-dimensional materials, like carbon nanotubes, are relatively straightforward to solve using existing classical methods. Many two-dimensional and three-dimensional materials are also manageable. However, certain complex systems pose significant challenges, particularly three-dimensional crystal structures with intricate atomic arrangements and “frustrated” geometries.
The Hubbard model, which simulates electron interactions, emerged as especially challenging in two-dimensional systems where electron mobility and interactions strongly compete. These complex scenarios represent potential breakthrough opportunities for quantum computing algorithms.
Crucially, the researchers’ approach is not about ranking existing techniques but creating an open, dynamic framework for assessing computational methods. By making their database and codes fully accessible, they invite ongoing contributions and innovations from the scientific community.
Carleo emphasizes the interdisciplinary nature of this work, where theoretical computer science and physics converge. Unlike previous “quantum supremacy” claims that solve abstract computational puzzles, this research focuses on problems with genuine scientific significance, particularly in material science.
The study represents an important step in understanding when and how quantum computing might provide meaningful advantages over classical computational methods, offering a rigorous methodology for evaluating emerging technologies.
Reference: “Variational benchmarks for quantum many-body problems” by Dian Wu, Riccardo Rossi, Filippo Vicentini, Nikita Astrakhantsev, Federico Becca, Xiaodong Cao, Juan Carrasquilla, Francesco Ferrari, Antoine Georges, Mohamed Hibat-Allah, Masatoshi Imada, Andreas M. Läuchli, Guglielmo Mazzola, Antonio Mezzacapo, Andrew Millis, Javier Robledo Moreno, Titus Neupert, Yusuke Nomura, Jannes Nys, Olivier Parcollet, Rico Pohle, Imelda Romero, Michael Schmid, J. Maxwell Silvester, Sandro Sorella, Luca F. Tocchio, Lei Wang, Steven R. White, Alexander Wietek, Qi Yang, Yiqi Yang, Shiwei Zhang and Giuseppe Carleo, 17 October 2024, Science.
DOI: 10.1126/science.adg9774
