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Quantum simulations of the phase diagram of quantum chromodynamics faces hard challenges, such as having to prepare mixed states and enforcing the non-Abelian gauge symmetry constraints. Here, the authors show how to solve the two above problems in a trapped-ion device using motional ancillae and charge-singlet measurements.

This study of magic-angle twisted trilayer graphene moiré superconductors using scanning tunnelling microscopy and spectroscopy identifies two energy gaps that develop from many-body resonance in this highly tunable class of materials.

Using intracranial electroencephalography from patients with epilepsy during spatial attention tasks, this study shows that high-frequency bursts facilitate fast communications in brain networks and support attentional information routing.

There are many quantum systems that act as high-quality quantum harmonic oscillators, and they can be used to store quantum information using the Gottesman–Kitaev–Preskill code. Entangling gates have now been demonstrated between two of these qubits.

The estimation of low energies of many-body systems is a cornerstone of the computational quantum sciences. This paper demonstrates on a superconducting quantum processor that the Krylov quantum diagonalization algorithm is poised to complement its classical counterparts at the foundation of computational methods for quantum systems.

Neural network representations of quantum states are hoped to provide an efficient basis for numerical methods without the need for case-by-case trial wave functions. Here the authors show that limited generalization capacity of such representations is responsible for convergence problems for frustrated systems.

The spectral gap problem—whether the Hamiltonian of a quantum many-body problem is gapped or gapless—is rigorously proved to be undecidable; there exists no algorithm to determine whether an arbitrary quantum many-body model is gapped or gapless, and there exist models for which the presence or absence of a spectral gap is independent of the axioms of mathematics.

A terahertz (THz) optical single-sideband modulator (OSSB) for direct conversion of free-space THz electromagnetic radiation to THz optical modulations is realized. The THz OSSB operates in the 0.3–1.0 THz range without any frequency-dependent tuning.

The brain and body are necessarily connected. Here the authors show that brain blood flow and electrical activity are coupled with systemic physiological changes in the body.

How are memories transferred from short-term to long-term storage? Here, the authors show that during deep (NREM) sleep, the prefrontal cortex initiates rapid, bidirectional interactions to trigger information transfer from the hippocampus to the neocortex.

Numerical simulation of light propagation through 1D atomic systems in the many-body limit rapidly saturates hardware capabilities. Here, the authors tackle the problem by mapping the dynamics to an open 1D interacting spin system and solving it using the matrix product state ansatz.

This study demonstrates that resting-state fMRI networks can rapidly reconfigure in response to a single, 10-ms thalamic neural population activity input, revealing how brain networks mediate fast neural information flow and processing.

To design and manipulate qubits, it is necessary to engineer multidimensional non-equilibrium steady states immune to decoherence in an open system. Here the authors devise a symmetry-based framework to create such non-equilibrium steady states showing characteristics of degenerate vacua of a unitary topological system.

In solid metals, electron orbitals form broad bands and their binding of adsorbates depends on the bandwidth. Now, it is shown that a weak solute–matrix interaction in dilute alloys results in extremely narrow electronic bands on the solute, similar to a free-atom electronic structure. This structure affords unique adsorption properties important for catalysis.

A superconducting quantum computer is used to realize an out-of-equilibrium topologically ordered state, which hosts anyonic excitations.

Phase diagrams describe how a system changes phenomenologically as an external parameter, such as a magnetic field strength, is varied. Here, the authors prove that in general such a phase diagram is uncomputable, by explicitly constructing a one-parameter Hamiltonian for which this is the case.

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.

Complex-valued neural networks can recognize phase-sensitive data in wave-related phenomena. Here, authors report a complex-valued optical convolution accelerator operating at over 2 TOPS for recognition of radar images, represents advances towards real-time analysis of complex satellite data.

Implementations of shallow quantum machine learning models are a promising application of near-term quantum computers, but rigorous results on their trainability are sparse. Here, the authors demonstrate settings where such models are untrainable.

Quantum compressed sensing can provide a scalable way to characterize quantum states and devices, but has been so far limited to states with quickly decaying eigenvalues. Here the authors show that it can be appropriate even in the general case, demonstrating reconstruction the state of a seven-qubit system.

A cold-atom simulator has realized a popular many-body model of quantum magnetism in regimes that cannot be easily studied theoretically, achieving the record-coldest fermions ever seen.

Inherent limitations on continuously measured quantum systems calls into question whether they could even in principle be used for online learning. Here, the authors experimentally demonstrate a quantum machine learning framework for inference on streaming data of arbitrary length, and provide a theory with criteria for the utility of their algorithm for inference on streaming data.

The approach to stabilizing a quantum state by coupling to engineered reservoirs is limited by a trade-off between state fidelity and stabilization rate. Here the authors implement a protocol based on parametric system-bath coupling to achieve fast and high-fidelity Bell state stabilization in a qutrit-qubit system.

Experimental measurements of high-order out-of-time-order correlators on a superconducting quantum processor show that these correlators remain highly sensitive to the quantum many-body dynamics in quantum computers at long timescales.

The whole-brain organization of functional MRI signals has been studied in myriad ways. An in-depth study of these signals suggests a parsimonious description with a small number of spatiotemporal patterns.

Though the amplitude and frequency of neural oscillations in the alpha band are related to dissociable visual processes, they are not independent mathematically. Here, the authors show that fluctuations in instantaneous frequency predict alpha amplitude during visual discrimination tasks.

Full tomography of the quantum state of a many-body system becomes harder as more and more atoms are included. Here the authors borrow a concept from condensed-matter physics, continuous matrix-product states, and present an efficient approach for experimental quantum-field tomography.

By implementing random circuit sampling, experimental and theoretical results establish the existence of transitions to a stable, computationally complex phase that is reachable with current quantum processors.

Bosonic qubits can be engineered to feature intrinsic protection against certain kinds of errors, which makes quantum error correction across many bosonic qubits possible with less overhead.

Understanding surface carrier dynamics enables the design of optimal optoelectronic devices. Yang et al. find that surface recombination limits the total carrier lifetime in polycrystalline lead iodide perovskite films, meaning recombination at surfaces is more important than within and between grains.

Many quantum machine learning algorithms have been proposed, but it is typically unknown whether they would outperform classical methods on practical devices. A specially constructed algorithm shows that a formal quantum advantage is possible.

The authors present an approach to collect and process solution NMR spectra based on steady-state free-precession acquisitions, which can provide higher sensitivity for small organic molecules than currently used Fourier-based counterparts.

Implementations of quantum walks on ion trap quantum computers have been so far limited to the analogue simulation approach. Here, the authors implement a quantum-circuit-based discrete quantum walk in one-dimensional position space, realizing a Dirac cellular automaton with tunable mass parameter.

Experiments with a trapped-ion quantum simulator observe Stark many-body localization, in which the quantum system evades thermalization despite having no disorder.

The use of time-bin entangled qudits is hindered by the phase instability, timing inaccuracy and low scalability of current interferometric schemes. Here, the authors show a fiber-pigtailed photonic chip for generating and processing picosecond-spaced time-bin entangled qudits and utilize the system to implement a quantum key distribution protocol.

After short-term Physical activity (PA), working memory (WM) performance improved. Ripple activity increased with memory load, and ripple to awake-spindle coupling strengthened, suggesting PA boosts WM via hippocampal-thalamocortical dynamics.

The electrodynamics of superconducting devices make them suitable for applications as detectors, amplifiers, and qubits. Here the authors show that resonators made from granular aluminum, which naturally realizes a network of Josephson junctions, have practically useful impedances and nonlinearities.

Generalization - that is, the ability to extrapolate from training data to unseen data - is fundamental in machine learning, and thus also for quantum ML. Here, the authors show that QML algorithms are able to generalise the training they had on a specific distribution and learn over different distributions.

A violation of Bell's inequality, which is a direct proof of entanglement, can be observed in the solid state using the electron and nuclear spins of a single phosphorus atom in silicon.

The study of complexity in quantum systems is a fascinating topic, which however is still in its infancy, especially at the experimental level. Here, the authors report on the observation of “small-world” characteristics in the network of quantum correlations within chains of up to 23 superconducting qubits long.