A new mathematical tool to simulate quantum material’s properties more quickly

This could accelerate the development of materials for energy-efficient IT technologies of the future.

Quantum Monte Carlo (QMC) methods are the gold standard for studying equilibrium properties of quantum many-body systems. However, in many unusual situations, QMC methods are faced with a significant problem, causing the severe limitation of an exponential increase in the runtime of the QMC algorithm.

The calculation of quantum material characteristics costs about one million hours of CPU on mainframe computers every day.

In a new study, a joint research group at Freie Universität Berlin and the Helmholtz-Zentrum Berlin (HZB, Germany) has demonstrated a systematic, generally applicable, and practically feasible methodology for easing the sign problem by efficiently computable basis changes and use it to assess the sign problem rigorously.

Dominik Hangleiter, the first author of the study, said, “We show that solid-state systems can be viewed from very different perspectives. The sign problem plays a different role in these different perspectives. It is then a matter of dealing with the solid-state system in such a way that the sign problem is minimized.”

For basic strong state systems with spins, which structure what are known as Heisenberg ladders, this methodology has empowered the group to decrease the sign problem’s computational time significantly. Be that as it may, the numerical tool can likewise be applied to more complex spin systems and promises quicker computation of their properties.

Prof. Jens Eisert, who heads the joint research group at Freie Universität Berlin and the HZB, said“This provides us with a new method for accelerated development of materials with special spin properties. These materials could find application in future IT technologies for which data must be processed and stored with considerably less energy expenditure.”

Journal Reference:
  1. Dominik Hangleiter et al. Easing the Monte Carlo sign problem, Science Advances (2020). DOI: 10.1126/sciadv.abb8341

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