Harel Kol-Namer | Searcharxiv

arXiv2026

Stoquastic simulations of non-stoquastic superconducting flux circuits

There is a tremendous interest in fabricating superconducting flux circuits that are nonstoquastic -- i.e., have positive off-diagonal matrix elements -- in their qubit representation, as these circuits are thought to be unsimulable by classical approaches due to the presence of a sign problem and thus could play a key role in the demonstration of speedups in quantum annealing protocols. We show, however, that the elimination of the sign problem in these systems is possible by the direct simulation of the flux circuits. Our approach not only obviates the reduction of flux circuits to their qubit representation but also produces results that are more in the spirit of the experimental setup. We discuss the implications of our work, arguing that our findings cast doubt on the conception that superconducting flux circuits represent the correct avenue for universal adiabatic quantum computers.

quant-ph↗

arXiv2024

Neural Network Ground State from the Neural Tangent Kernel Perspective: The Sign Bias

Neural networks has recently attracted much interest as useful representations of quantum many body ground states, which might help address the infamous sign problem. Most attention was directed at their representability properties, while possible limitations on finding the desired optimal state have not been suitably explored. By leveraging well-established results applicable in the context of infinite width, specifically regarding the renowned neural tangent kernel and conjugate kernel, a comprehensive analysis of the convergence and initialization characteristics of the method is conducted. We reveal the dependence of these characteristics on the interplay among these kernels, the Hamiltonian, and the basis used for its representation. We introduce and motivate novel performance metrics and explore the condition for their optimization. By leveraging these findings, we elucidate a substantial dependence of the effectiveness of this approach on the selected basis, demonstrating that so-called stoquastic Hamiltonians are more amenable to solution through neural networks than those suffering from a sign problem.

quant-ph↗