Sprecher
Beschreibung
We propose using quantum computers in conjunction with classical machine learning to discover instances of interesting quantum many-body dynamics. Concretely, an “interest function” is defined for a given circuit (family) instance that can be evaluated on a quantum computer. The circuit is then adapted by a classical learning agent to maximize interest. We illustrate this approach using two examples and show numerically that, within a sufficiently general circuit family, two simple interest functions based on (i) classifiability of evolved states and (ii) spectral properties of the unitary circuit, are maximized by discrete time crystals (DTCs) and dual-unitary circuits, respectively. For (i), we simulate the adaptive optimization and show that it indeed finds DTCs with high probability, and we evaluate the interest function on a quantum device beyond the (easily) classically simulable regime. Our results show substantial gradients even at large system size and hence provide evidence showing that the interest function is indeed amenable to numerical optimization. This suggests that learning agents with access to quantum-computing resources can discover new phenomena in many-body quantum dynamics, and establishes the design of good interest functions as a future research paradigm for quantum many-body physics.