Phys.org August 3, 2022
Predicting the properties of a molecule or material requires calculating the collective behavior of its electrons because the electrons can become “quantum mechanically” entangled with one another. The entangled web of connections becomes tricky for even the most powerful computers to unravel directly for any system with more than a handful of particles. An international team of researchers (USA – Res. org., Switzerland) created a way to simulate entanglement by adding to their computations extra “ghost” electrons that interact with the system’s actual electrons. The behavior of the added electrons is controlled by neural network. The network makes tweaks until it finds an accurate solution that can be projected back into the real world, thereby recreating the effects of entanglement without the accompanying computational hurdles. They demonstrated that their approach matches or outclasses competing methods in simple quantum systems. Now they are taking this to the next step and trying this on molecules and other, more realistic problems. Their long-term goal is to enable researchers to computationally predict the properties of a material or molecule without having to synthesize and test it in a lab…read more. Open Access TECHNICAL ARTICLE
Hidden-fermion determinant-state amplitudes with a neural-network parameterized constraint function… Credit: PNAS, Vol. 119 | No. 32, August 3, 2022