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Two-dimensional quantum spin liquid states, which retain spin disorder down to low temperatures, have never been realized experimentally. Here, the authors use quantum Monte Carlo methods to predict a new route to this state in rare-earth pyrochlore quantum spin ices under an applied (111) magnetic field.

Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.

Quantum simulation of non-Hermitian systems is non-trivial, as the use of Trotterization would incur into a huge sampling overhead. Here, the authors use a variational approach to simulate a quench in a 18-site fermionic chain on a trapped-ion quantum processor, seeing the signature of a supersonic mode in the connected density-density correlation function and providing insights on when quantum computers fail to simulate non-Hermitian dynamics.

The success of machine learning techniques in handling big data sets proves ideal for classifying condensed-matter phases and phase transitions. The technique is even amenable to detecting non-trivial states lacking in conventional order.

An experiment reveals the dynamics of singly and doubly occupied sites in an atomic Bose gas in a one-dimensional optical lattice, which may provide a better understanding of thermalization and quantum correlations in many-body systems.

In quantum technologies, scalable ways to characterise errors in quantum hardware are highly needed. Here, the authors propose an approximate version of quantum process tomography based on tensor network representations of the processes and data-driven optimisation.

A kagome lattice spin-ice system is created with the superconducting qubits of a quantum annealer, and shown to exhibit a field-induced kinetic crossover between spin-liquid phases. Specifically, kinetics within both the Ice-I phase and the unconventional field-induced Ice-II phase are  presented.

So-called noisy intermediate-scale quantum devices will be capable of a range of quantum simulation tasks, provided that the effects of noise can be sufficiently reduced. A neural error mitigation approach is developed that uses neural networks to improve the estimates of ground states and ground-state observables of molecules and quantum systems obtained using quantum simulations on near-term devices.

Optimization problems can be described in terms of a statistical physics framework. This offers the possibility to make use of ‘simulated annealing’, which is a procedure to search for a target solution similar to the gradual cooling of a condensed matter system to its ground state. The approach can now be sped up significantly by implementing a model of recurrent neural networks, in a new strategy called variational neural annealing.

A draft sequence of the staple crop kabuli chickpea, together with resequencing and analysis of 90 additional lines from 10 countries, provides a resource for breeders.

A large-scale programmable quantum simulation is described, using a D-Wave quantum processor to simulate a two-dimensional magnetic lattice in the vicinity of a topological phase transition.

Unsupervised machine learning techniques can efficiently perform quantum state tomography of large, highly entangled states with high accuracy, and allow the reconstruction of many-body quantities from simple experimentally accessible measurements.

Frustrated geometries in Rydberg atom arrays present challenges for conventional simulations, particularly in exploring exotic many-body states like spin liquids and glasses. The authors employ 2D recurrent neural network wave functions to study ground states on the Kagome lattice, revealing no evidence of exotic phases and highlighting the potential of autoregressive models in overcoming simulation limitations.

Language models offer promises in encoding quantum correlations and learning complex quantum states. This Perspective discusses the advantages of employing language models in quantum simulation, explores recent model developments, and offers insights into opportunities for realizing scalable and accurate quantum simulation.

A type of stochastic neural network called a restricted Boltzmann machine has been widely used in artificial intelligence applications for decades. They are now finding new life in the simulation of complex wavefunctions in quantum many-body physics.

Neural network quantum states (NQS) are a promising method to simulate large fermionic systems. This work reports on accurate simulations of the t-J model in 1D and 2D lattices by means of NQS based on a recurrent neural network (RNN) architecture focusing on the calculation of dispersion relations, for which a general method is introduced, and on the performance of the RNN ansatz upon doping.

Generative modeling has become a widespread method in many areas of science. This work provides a comprehensive comparison between quantum and classical generative modeling techniques, with promising prospects for quantum generative modeling towards reaching practical quantum advantage.