Quantum systems, from subatomic particles to complex molecules, hold the key to understanding the workings of the universe. However, modeling these systems quickly becomes overwhelming due to their immense complexity. It’s like trying to predict the behavior of a massive crowd where everyone constantly influences everyone else. When you replace the crowd with quantum particles, you encounter what’s known as the “quantum many-body problem.”
Quantum many-body problems involve predicting the behavior of numerous interacting quantum particles. Solving these problems could lead to major breakthroughs in fields like chemistry and materials science, and even accelerate the development of technologies like quantum computers.
However, as more particles are introduced, the modeling becomes increasingly difficult—especially when seeking the system’s ground state, or lowest energy state. Understanding the ground state is crucial because it reveals which materials are stable and can even uncover exotic phases like superconductivity.
For years, scientists have relied on a mix of methods like quantum Monte Carlo simulations and tensor networks (variational wave functions) to approximate solutions to these problems. Each method has its strengths and weaknesses, but it’s hard to know which one works best for which problem. And until now, there hasn’t been a universal way to compare their accuracy.
A large collaboration of scientists, led by Giuseppe Carleo at EPFL has now developed a new benchmark called the “V-score” to tackle this issue. The V-score (“V” for “Variational Accuracy”) offers a consistent way to compare how well different quantum methods perform on the same problem. The V-score can be used to identify the hardest-to-solve quantum systems, where current computational methods struggle, and where future methods —such as quantum computing — might offer an advantage.
The breakthrough method was published in the journal Science on October 17.
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