The ability to transfer quantum information between systems is a fundamental component of quantum technologies and leads to correlations within the global quantum process. However, correlation structures in quantum channels are less studied than those in quantum states. Motivated by recent techniques in randomized benchmarking, we develop a range of results for efficient estimation of correlations within a bipartite quantum channel. We introduce subunitarity measures that are invariant under local changes of basis, generalize the unitarity of a channel, and allow for the analysis of quantum information exchange within channels. Using these, we show that unitarity is monogamous, and we provide an information-disturbance relation. We then define a notion of correlated unitarity that quantifies the correlations within a given channel. Crucially, we show that this measure is strictly bounded on the set of separable channels and therefore provides a witness of nonseparability. Finally, we describe how such measures for effective noise channels can be efficiently estimated within different randomized benchmarking protocols. We find that the correlated unitarity can be estimated in a SPAM-robust manner for any separable quantum channel, and we show that a benchmarking/tomography protocol with mid-circuit resets can reliably witness nonseparability for sufficiently small reset errors. The tools we develop provide information beyond that obtained via simultaneous randomized benchmarking and so could find application in the analysis of cross-talk errors in quantum devices.

Large scale quantum computing motivates the invention of two-qubit gate schemes that not only maximize the gate fidelity but also draw minimal resources. In the case of superconducting qubits, the weak anharmonicity of transmons imposes profound constraints on the gate design, leading to increased complexity of devices and control protocols. Here we demonstrate a resource-efficient control over the interaction of strongly-anharmonic fluxonium qubits. Namely, applying an off-resonant drive to noncomputational transitions in a pair of capacitively-coupled fluxoniums induces a $\text{ZZ}$ interaction due to unequal ac Stark shifts of the computational levels. With a continuous choice of frequency and amplitude, the drive can either cancel the static $\text{ZZ}$ term or increase it by an order of magnitude to enable a controlled-phase (CP) gate with an arbitrary programmed phase shift. The cross-entropy benchmarking of these non-Clifford operations yields a sub $1\%$ error, limited solely by incoherent processes. Our result demonstrates the advantages of strongly-anharmonic circuits over transmons in designing the next generation of quantum processors.

Complex dynamical systems have tipping points and exhibit nonlinear dynamics. It is difficult to predict and prevent the onset and progression of the tumors, mainly due to the complexity of interactions between tumor growth and tumor-immune cells involved. Moreover, previous models were based on the influence of the zero-dimensional systems and did not consider the spatiotemporal fluctuation in the tumor microenvironment. We here extend the previous model to a two-dimensional system and employ spatial early warning signals to study the spatially extended tumor-immune system with stochasticity. On the one hand, we obtain the stationary probability density of the system under the mean-field approximation assumption. It is found that the health state gets more and more stable than the disease state as the noise level increases when the system has a bistable state, and the system goes from health to disease state through a bistable region as the growth rate increases. On the other hand, we present a spatiotemporal diffusion coefficient indicator to predict upcoming critical transitions. It is shown that a rising spatiotemporal diffusion coefficient obtained from the spatial snapshot data can be an effective indicator for predicting upcoming critical transitions. Anticipating critical transitions in the spatial tumor-immune system with stochasticity can be greatly helpful to prevent disease onset and progression, which may intercept abrupt shifts from health to disease state.

Domain walls between different topological phases are one of the most interesting phenomena that reveal the nontrivial bulk properties of topological phases. Very recently, gapped domain walls between different topological phases have been intensively studied. In this paper, we systematically construct a large class of lattice models for gapless domain walls between twisted and untwisted gauge theories with arbitrary finite group $G$. As simple examples, we numerically study several finite groups (including both Abelian and non-Abelian finite group such as $S_3$) in $2\mathrm{D}$ using the state-of-the-art loop optimization of tensor network renormalization algorithm. We also propose a physical mechanism for understanding the gapless nature of these particular domain wall models. Finally, by taking advantage of the classification and construction of twisted gauge theories using group cohomology theory, we generalize such constructions into arbitrary dimensions, which might provide us a systematical way to understand gapless domain walls and topological quantum phase transitions.

We study the coordination in a group of humans by means of experiments and simulations. Experiments with human participants were implemented in a multiclient game setting, where players move on a virtual hexagonal lattice, can observe their and other players' positions on a screen, and receive a payoff for reaching specific goals on the playing field. Flocking behavior was incentivized by larger payoffs if multiple players reached the same goal field. We choose two complementary simulation methods to explain the experimental data: a minimal cognitive force approach, based on the maximization of future movement options in the agents' local environment, and multiagent reinforcement learning (RL), which learns behavioral policies to maximize reward based on past observations. Comparison between experimental and computer simulation data suggests that group coordination in humans can be achieved through nonspecific, information-based strategies. We also find that although the RL approach can capture some key aspects of the experimental results, it achieves lower performance compared to both the cognitive force simulation and the experiment, and matches the observed human behavior less closely.

We discuss topology in dissipative quantum systems from the perspective of quantum trajectories. The latter emerge in the unraveling of Markovian quantum master equations and/or in continuous quantum measurements. Ensemble-averaging quantum trajectories at the occurrence of quantum jumps, i.e., the jump times, gives rise to a discrete, deterministic evolution which is highly sensitive to the presence of dark states [Gneiting et al., Phys. Rev. A 104, 062212 (2021)]. We show for a broad family of translation-invariant collapse models that the set of dark-state-inducing Hamiltonians imposes a nontrivial topological structure on the space of Hamiltonians, which is also reflected by the corresponding jump-time dynamics. The topological character of the latter can then be observed, for instance, in the transport behavior, exposing an infinite hierarchy of topological phase transitions. We develop our theory for one- and two-dimensional two-band Hamiltonians and show that the topological behavior is directly manifest for chiral, $\mathcal{PT}$, or time-reversal-symmetric Hamiltonians.

Complete characterization of a noisy multipartite quantum state in terms of entanglement requires full knowledge of how the entanglement content in the state is affected by the spatial distribution of noise in the state. Specifically, we find that if the measurement basis in the protocol of computing localizable entanglement and the basis of the Kraus operator representing the local noisy channel do not commute, the information regarding the noise is retained in the system even after the qubit is traced out after measurement. Using this result and the basic properties of entanglement under noise, we present a set of hierarchies that localizable entanglement over a specific subsystem in a multiqubit state can obey when local noise acts on the subparts or on all the qubits of the whole system. In particular, we propose two types of hierarchies—one tailored according to the number of noisy unmeasured qubits, and the other one that depends additionally on the cardinality of the set of noisy measured qubits, leading to the classification of quantum states. We report the percentage of states satisfying the proposed hierarchies in the case of random three- and four-qubit systems and show, using both analytical methods and numerical simulations, that in almost all the cases, anticipated hierarchies tend to hold with the variation of the strength of noise.

With the intense interest in small, noisy quantum computing devices comes the push for larger, more accurate—and hence more useful—quantum computers. While fully fault-tolerant quantum computers are, in principle, capable of achieving arbitrarily accurate calculations using devices subjected to general noise, they require immense resources far beyond our current reach. An intermediate step would be to construct quantum computers of limited accuracy enhanced by lower-level, and hence lower-cost, noise-removal techniques. This is the motivation for our paper, which looks into fault-tolerant encoded quantum computation targeted at the dominant noise afflicting the quantum device. Specifically, we develop a protocol for fault-tolerant encoded quantum computing components in the presence of amplitude-damping noise, using a 4-qubit code and a recovery procedure tailored to such noise. We describe a universal set of fault-tolerant encoded gadgets and compute the pseudothreshold for the noise, below which our scheme leads to more accurate computation. Our paper demonstrates the possibility of applying the ideas of quantum fault tolerance to targeted noise models, generalizing the recent pursuit of biased-noise fault tolerance beyond the usual Pauli noise models. We also illustrate how certain aspects of the standard fault tolerance intuition, largely acquired through Pauli-noise considerations, can fail in the face of more general noise.

We develop a field-theoretic approach for the treatment of both the nonlocal and the nonlinear response of structured liquid dielectrics. Our systems of interest are composed of dipolar solvent molecules and simple salt cations and anions. We describe them by two independent order parameters, the polarization field for the solvent and the charge-density field for the ions, including and treating the nonelectrostatic part of the interactions explicitly and consistently. We show how to derive functionals for the polarization and the electrostatic field of increasingly finer scales and solve the resulting mean-field saddle-point equations in the linear regime. We derive criteria for the character of their solutions that depend on the structural lengths and the polarity of the solvent. Our approach provides a systematic way to derive generalized polarization theories.

The progress of food in the gastrointestinal (GI) tract is driven by a peristaltic motion generated by the muscle belt surrounding the GI tract. In turn, the response of the intestinal epithelial cells to the peristaltic stresses affects the dynamics of the epithelial structure. In this work, we study the effect of cyclic stretching (0.125 Hz, 10% strain) on the spatial organization of the intestinal epithelium using intestinal cells deposited on a flat elastomeric substrate to mimic the peristaltic motion in vitro. A confluent monolayer of Caco-2 cells is grown on a PDMS chip to probe the morphological and orientational response of the tissue to cyclic stretching. The PDMS chips are either covalently or noncovalently coated with laminin to recapitulate the basement membrane. We observe a significant orientational response where the cells rearrange their long axes perpendicular to the stretching direction for both coating conditions. The experiment is modeled by a vertex model where the cells store elastic energy with varying strain and effectively have a rotational diffusive motion of their long axes through rearrangements of their shapes. The model also predicts a transition between the perpendicular orientation and the orientation at an oblique angle determined by the level of the cell elastic anisotropy. It provides a general framework to study cell response and relaxation dynamics under cyclic stretching across different cell types. We also discuss potential relevance of peristalsis in determining planar cell polarity in three-dimensional architectures.

We show that the notion of “Hookean law” $F=kx$, suitably defined in asymptotically flat singly spinning Myers-Perry black hole space-times in dimensions $d⩾5$, is related to the Emparan-Myers fragmentation (splitting of a black hole into two becomes thermodynamically preferable). Specifically, the values of black hole parameters when fragmentation occurs correspond to the maximal value of $F$. Furthermore this always happens before $F$ reaches $1/4$ in Planck units. These results suggest that a version of “maximum force conjecture” may be relevant for black hole thermodynamics. We also relate these findings to the Ruppeiner thermodynamic geometry of these black holes and speculate on the implications for the underlying microstructures of black hole horizons.

The ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$ family has attracted significant attention due to its rich topological states such as the quantum anomalous Hall (QAH) insulator state, the axion insulator state, and the magnetic Weyl semimetal state. Nevertheless, the intrinsic antiferromagnetic (AFM) interlayer coupling in ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$ partly hinders the realization of the “high-temperature” QAH effect. Here, by using first-principles electronic structure calculations, we design a new class of materials $\mathrm{Mn}{X}_2{B}_2{T}_6$ ($X=\text{Ge}$, Sn, or Pb; $B=\text{Sb}$ or Bi; $T=\text{Se}$ or Te) based on the $X_2{B}_2{T}_5$ structures rather than the ${\mathrm{Bi}}_2{\mathrm{Te}}_3$ family. We find that each septuple-layer $\mathrm{Mn}{B}_2{T}_4$ is sandwiched by two $\left[XT\right]$ layers, which may turn the AFM interlayer coupling into a ferromagnetic (FM) coupling. The calculations specifically demonstrate that ${\text{MnGe}}_2{\text{Sb}}_2{\text{Te}}_6$, ${\text{MnGe}}_2{\text{Bi}}_2{\text{Te}}_6$, and ${\text{MnPb}}_2{\text{Bi}}_2{\text{Te}}_6$ are FM axion insulators, while ${\mathrm{MnGe}}_2{\mathrm{Sb}}_2{\mathrm{Se}}_6$, ${\mathrm{MnGe}}_2{\mathrm{Bi}}_2{\mathrm{Se}}_6$, ${\mathrm{MnSn}}_2{\mathrm{Sb}}_2{\mathrm{Te}}_6$, and ${\mathrm{MnSn}}_2{\mathrm{Bi}}_2{\mathrm{Te}}_6$ are A-type AFM axion insulators. These seven materials all have an out-of-plane easy axis of magnetization. The $\mathrm{Mn}{X}_2{B}_2{T}_6$ family thus offers a promising platform beyond the ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$ family for the realization of the quantized magnetoelectric effect and “high-temperature” QAH effect in future experiments.

Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely on featurization of materials composition, however, whether the exclusive use of structural knowledge in such models has the capacity to make comparable predictions remains unknown. Here we employ a deep neural network model to decode structure-property relationships in crystalline materials without explicitly considering chemical compositions. The focus is on classification of crystal systems, mechanical elasticity, electronic band gap, and phase stability. Our model utilizes a three-dimensional (3D) momentum space representation of structure from elastic x-ray scattering theory that exhibits rotation and permutation invariance. We perform novel ablation studies to help interpret the model performance by perturbing the physically meaningful input features (i.e., the diffraction patterns) instead of tuning the architecture of the learning model as in conventional ablation methods. We find that the spatial symmetry of the 3D diffraction patterns, which reflects crystalline symmetry operations, is more important than the diffraction intensities contained within for the model to make a successful classification. Our work showcases the potential of using statistical learning models to help understand materials physics, rather than performing predictive and generative tasks as in most materials informatics research. We also argue that learning the crystal structure genome in a chemistry-agnostic manner demonstrates that some crystal structures inherently host high propensities for optimal materials properties, which enables the decoupling of structure and composition for future codesign of multifunctionality.

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.