Nic Shannon

Machine-learning algorithms thrive on large data sets of good quality. Here we show that they can also excel in a typical research setting with little data of limited quality, through an interplay of insights coming from machine and human researchers. The question we address is the unsolved problem of ordering out of a spin-liquid phase described by an emergent rank-2 U ( 1 ) gauge theory, as described by H. Yan []. Published Monte Carlo simulations for this problem are consistent with a strong first-order phase transition, but were too noisy for the form of low-temperature order to be identified. Using a highly interpretable machine-learning approach based on a support vector machine with a tensorial kernel, we reanalyze these Monte Carlo data, gaining new information about the form of order that could in turn be interpreted by traditionally trained physicists. We find that the low-temperature ordered phase is a form of magnetic order analogous to a smectic liquid crystal. This arises due to a subtle thermal order-by-disorder mechanism, that can be understood from the fluctuations of the tensor electric field of the parent rank-2 gauge theory. These results were obtained by a back-and-forth process which closely resembles a collaboration between human researchers and machines. We argue that this “collaborative” approach may provide a blueprint for solving other problems that have not yielded to human insights alone.

There are no clean samples in nature. Therefore, when we come to discuss the entanglement properties of quantum materials, the effects of disorder must be taken into account. This question is of particular interest for highly entangled phases, such as quantum spin liquids, which lie outside the Landau paradigm for classifying phases of matter. In this work, we explore what experimentally accessible measures, in the form of concurrence, residual tangle, and quantum Fisher information can teach us about the entanglement in the presence of disorder. As a representative example, we consider the Tomonaga-Luttinger liquids (TLL) and disorder-driven random singlet (RS) phases found in antiferromagnetic quantum spin chains. Using quantum Fisher information and residual tangle, we demonstrate that both TLL and RS phases exhibit multipartite entanglement. In the case of the RS phase, we attribute this to entanglement localized below a crossover length scale. We further show that the order of disorder average matters in calculating measures like concurrence and that this can lead to false inferences when interpreting experiments. Nonetheless, correctly interpreted, these witnesses provide useful information about the effects of disorder. We explore how information about the central charge of the TLL can be extracted from the low-temperature behavior of concurrence and conjecture that this analysis can be extended to the effective central charge of the RS phase. Finally, we establish how RS and TLL phases can be distinguished through the growth of multipartite entanglement, as witnessed by the equal-time structure factor. These results establish that, used carefully, experiments based on entanglement witnesses can provide important information about quantum spin systems in the presence of disorder. Published by the American Physical Society 2025

We have measured the critical current density, superconducting coherence length, and superconducting transition temperature of single-domain, epitaxially grown Nb ( 110 ) / Au ( 111 ) / Nb ( 110 ) trilayers, all of which show a nonmonotonic dependence on the thickness of the Au layer. These results are compared with the predictions of a relativistic, theory, which incorporates superconducting correlations. We find good agreement with experiment, coming from a rich interplay between superconducting proximity and quantum size effects, mediated by Andreev bound states. These results suggest that quantum size effects could provide a systematic method of controlling the transport properties of superconducting multilayers. Published by the American Physical Society 2025

The usual starting point for understanding magnons in cuprate antiferromagnets such as La 2 CuO 4 is a spin model incorporating cyclic exchange, which descends from a one-band Hubbard model, and has parameters taken from fits based on non-interacting spin wave theory. Here we explore whether this provides a reliable description of experiment, using matrix product states (MPS) to calculate magnon spectra beyond spin wave theory. We find that analysis based on low orders of spin wave theory leads to systematic overestimates of exchange parameters, with corresponding errors in estimates of Hubbard t / U . Once these are corrected, the “standard” model provides a good account of magnon dispersion and lineshape in La 2 CuO 4 , but fails to fully capture the continuum observed at high energies. The extension of this analysis to CaCuO 2 and Sr 2 IrO 4 is also discussed.