Twisted van der Waals heterostructures have recently been proposed as a condensed-matter platform for realizing controllable quantum models due to the low-energy moiré bands with specific charge distributions moiré superlattices. Here, combining angle-resolved photoemission spectroscopy with submicron spatial resolution ($\mu$-ARPES) and scanning tunneling microscopy (STM), we performed a systematic investigation on the electronic structure of 5.1° twisted bilayer ${\mathrm{WSe}}_2$ that hosts correlated insulating and zero-resistance states. Interestingly, contrary to one’s expectation, moiré bands were observed only at $\mathrm{\Gamma }$ valley but not $K$ valley in $\mu$-ARPES measurements, and correspondingly, our STM measurements clearly identified the real-space honeycomb- and kagome-shaped charge distributions at the moiré length scale associated with the $\mathrm{\Gamma }$-valley moiré bands. These results not only reveal the unusual valley-dependent moiré-modified electronic structure in twisted transition metal dichalcogenides, but also highlight the $\mathrm{\Gamma }$-valley moiré bands as a promising platform for exploring strongly correlated physics in emergent honeycomb and kagome lattices at different energy scales.

We present a comprehensive dynamical mean field study of the triangular lattice moiré Hubbard model, which is believed to represent the physics of moiré bilayer transition metal dichalcogenides. In these materials, important aspects of the band structure including the bandwidth and the order and location of van Hove singularities can be tuned by varying the interlayer potential. We present a magnetic and metal-insulator phase diagram and a detailed study of the dependence of the resistivity on temperature, band filling, and interlayer potential. We find that transport displays Fermi liquid, strange metal, and quantum critical behaviors in distinct regions of the phase diagram. Specifically, we find that the cube-root van Hove singularity [$\rho (\epsilon )\sim |\epsilon {|}^{-1/3}$] gives a strange metal behavior with a $T$-linear scattering rate and $\omega /T$ scaling. We show how magnetic order affects the resistivity. Our results elucidate the physics of the correlated states and the metal-insulator continuous transition recently observed in twisted homobilayer ${\mathrm{WSe}}_2$ and heterobilayer ${\mathrm{MoTe}}_2/{\text{WSe}}_2$ experiments.

Storing memory for molecular recognition is an efficient strategy for responding to external stimuli. Biological processes use different strategies to store memory. In the olfactory cortex, synaptic connections form when stimulated by an odor and establish an associative distributed memory that can be retrieved upon reexposure to the same odors. In contrast, the immune system encodes specialized memory by diverse receptors that can recognize a multitude of evolving pathogens. Despite the mechanistic differences between memory storage in the olfactory system and the immune system, these processes can still be viewed as different information encoding strategies. Here, we develop analytical and numerical techniques for a generalized Hopfield network to probe the utility of distinct memory strategies against both static and dynamic (evolving) patterns. We find that while classical Hopfield networks with distributed memory can efficiently encode a memory of static patterns, they are inadequate against evolving patterns. To follow an evolving pattern, we show that a Hopfield network should use a higher learning rate, which can in turn distort the energy landscape associated with the stored memory attractors. Specifically, we observe the emergence of narrow connecting paths between memory attractors that lead to misclassification of evolving patterns. We demonstrate that compartmentalized networks with specialized subnetworks are the optimal solutions to memory storage for evolving patterns. We postulate that evolution of pathogens may be the reason for the immune system to be encoded in a focused memory, in contrast to the distributed memory used in the olfactory cortex that interacts with mixtures of static odors. Our approach offers a principled framework to study learning and memory retrieval in out-of-equilibrium dynamical systems.

The demonstration of a quantum link between microwave and optical frequencies would be an important step toward the realization of a quantum network of superconducting processors. A major impediment to quantum electro-optic transduction in all platforms explored to date is noise added by thermal occupation of modes involved in the transduction process, and it has proved difficult to realize low thermal occupancy concurrently with other desirable features like high duty cycle and high efficiency. In this work, we present an efficient and continuously operating electro-optomechanical transducer whose mechanical mode has been optically sideband cooled to its quantum ground state. The transducer achieves a maximum efficiency of 47% and minimum input-referred added noise of 3.2 photons in upconversion. Moreover, the thermal occupancy of the transducer’s microwave mode is minimally affected by continuous laser illumination with power more than 2 orders of magnitude greater than that required for optomechanical ground-state cooling.

Quantum sensors such as spin defects in diamond have achieved excellent performance by combining high sensitivity with spatial resolution. Unfortunately, these sensors can only detect signal fields with frequency in a few accessible ranges, typically low frequencies up to the experimentally achievable control field amplitudes and a narrow window around the sensors’ resonance frequency. Here, we develop and demonstrate a technique for sensing arbitrary-frequency signals by using the sensor qubit as a quantum frequency mixer, enabling a variety of sensing applications. The technique leverages nonlinear effects in periodically driven (Floquet) quantum systems to achieve quantum frequency mixing of the signal and an applied bias ac field. The frequency-mixed field can be detected using well-developed sensing techniques such as Rabi and CPMG with the only additional requirement of the bias field. We further show that the frequency mixing can distinguish vectorial components of an oscillating signal field, thus enabling arbitrary-frequency vector magnetometry. We experimentally demonstrate this protocol with nitrogen-vacancy centers in diamond to sense a 150-MHz signal field, proving the versatility of the quantum mixer sensing technique.

At high magnetic fields, monolayer graphene hosts competing phases distinguished by their breaking of the approximate SU(4) isospin symmetry. Recent experiments have observed an even denominator fractional quantum Hall state thought to be associated with a transition in the underlying isospin order from a spin-singlet charge density wave at low magnetic fields to an antiferromagnet at high magnetic fields, implying that a similar transition must occur at charge neutrality. However, this transition does not generate contrast in typical electrical transport or thermodynamic measurements and no direct evidence for it has been reported, despite theoretical interest arising from its potentially unconventional nature. Here, we measure the transmission of ferromagnetic magnons through the two-dimensional bulk of clean monolayer graphene. Using spin polarized fractional quantum Hall states as a benchmark, we find that magnon transmission is controlled by the detailed properties of the low-momentum spin waves in the intervening Hall fluid, which is highly density dependent. Remarkably, as the system is driven into the antiferromagnetic regime, robust magnon transmission is restored across a wide range of filling factors consistent with Pauli blocking of fractional quantum Hall spin-wave excitations and their replacement by conventional ferromagnetic magnons confined to the minority graphene sublattice. Finally, using devices in which spin waves are launched directly into the insulating charge-neutral bulk, we directly detect the hidden phase transition between bulk insulating charge density wave and a canted antiferromagnetic phase at charge neutrality, completing the experimental map of broken-symmetry phases in monolayer graphene.

The sliding motion of objects is typically governed by their friction with the underlying surface. Compared to translational friction, however, rotational friction has received much less attention. Here, we experimentally and theoretically study the rotational depinning and orientational dynamics of two-dimensional colloidal crystalline clusters on periodically corrugated surfaces in the presence of magnetically exerted torques. We demonstrate that the traversing of locally commensurate areas of the moiré pattern through the edges of clusters, which is hindered by potential barriers during cluster rotation, controls its rotational depinning. The experimentally measured depinning thresholds as a function of cluster size strikingly collapse onto a universal theoretical curve which predicts the possibility of a superlow-static-torque state for large clusters. We further reveal a cluster-size-independent rotation-translation depinning transition when lattice-matched clusters are driven jointly by a torque and a force. Our work provides guidelines to the design of nanomechanical devices that involve rotational motions on atomic surfaces.

The classic combinatorial construct of maximum matchings probes the random geometry of regions with local sublattice imbalance in a site-diluted bipartite lattice. We demonstrate that these regions, which host the monomers of any maximum matching of the lattice, control the localization properties of a zero-energy quantum particle hopping on this lattice. The structure theory of Dulmage and Mendelsohn provides us with a way of identifying a complete and nonoverlapping set of such regions. This motivates our large-scale computational study of the Dulmage-Mendelsohn decomposition of site-diluted bipartite lattices in two and three dimensions. Our computations uncover an interesting universality class of percolation associated with the end-to-end connectivity of such monomer-carrying regions with local sublattice imbalance, which we dub Dulmage-Mendelsohn percolation. Our results imply the existence of a monomer percolation transition in the classical statistical mechanics of the associated maximally packed dimer model and the existence of a phase with area-law entanglement entropy of arbitrary many-body eigenstates of the corresponding quantum dimer model. They also have striking implications for the nature of collective zero-energy Majorana fermion excitations of bipartite networks of Majorana modes localized on sites of diluted lattices, for the character of topologically protected zero-energy wavefunctions of the bipartite random hopping problem on such lattices, and thence for the corresponding quantum percolation problem, and for the nature of low-energy magnetic excitations in bipartite quantum antiferromagnets diluted by a small density of nonmagnetic impurities.

We construct high-quality graphene-based van der Waals devices with narrow superconducting niobium nitride (NbN) electrodes, in which superconductivity and a robust fractional quantum Hall (FQH) state coexist. We find a possible signature for crossed Andreev reflection (CAR) across the superconductor separating two FQH edges. Our observed CAR probabilities in the particlelike fractional fillings are markedly higher than those in the integer and hole-conjugate fractional fillings and depend strongly on temperature and magnetic field unlike the other fillings. Further, we find a filling-independent CAR probability in integer fillings, which we attribute to spin-orbit coupling in NbN allowing for Andreev reflection between spin-polarized edges. These results provide a route to realize novel topological superconducting phases in FQH-superconductor hybrid devices based on graphene and NbN.

Power-law distributions are widely used in computational and statistical investigations of extreme events and complex systems. The usual technique to generate power-law distributed data is to first infer the scale exponent $\alpha$ using the observed data of interest and then sample from the associated distribution. This approach has important limitations because it relies on a fixed $\alpha$ (e.g., it has limited applicability in testing the family of power-law distributions) and on the hypothesis of independent observations (e.g., it ignores temporal correlations and other constraints typically present in complex systems data). Here we propose a constrained surrogate method that overcomes these limitations by choosing uniformly at random from a set of sequences exactly as likely to be observed under a discrete power law as the original sequence (i.e., regardless of $\alpha$) and by showing how additional constraints can be imposed in the sequence (e.g., the Markov transition probability between states). This nonparametric approach involves redistributing observed prime factors to randomize values in accordance with a power-law model but without restricting ourselves to independent observations or to a particular $\alpha$. We test our results in simulated and real data, ranging from the intensity of earthquakes to the number of fatalities in disasters.

Understanding how symmetries encode optical polarization information into selection rules in molecules and materials is important for their optoelectronic applications including spectroscopic analysis, display technology, and quantum computation. Here, we extend polarization-dependent selection rules from atoms to solid-state systems with various point groups with the help of the rotational operator for circular polarization and the twofold rotational operator (or reflection operator) for linear polarization. We use these new selection rules to study the optical properties of twisted bilayer graphene quantum dots (TBGQDs), which inherit advantages of graphene quantum dot including its ultrathin thickness, excellent biocompatibility, and shape- and size-tunable optical absorption or emission. We study how the electronic structures and optical properties of TBGQDs rely on size, shape, twist angle, and correlation effects for TBGQDs with 10 different point groups for which we obtain an optical selection rule database. We show how current operator matrix elements identify the generalized polarization-dependent selection rules. Our results show that both the electronic and optical band gaps follow power-law size scalings with a dominant role of the twist angle. We derive an atlas of optical conductivity spectra for both size and twist angle in TBGQDs. As a result of quantum confinement effects, in the atlas a new type of optical conductivity features emerges with multiple discrete absorption frequencies ranging from infrared to ultraviolet energy, allowing for applications in photovoltaic devices and photodetectors. The atlas and size scaling provide a full structure–symmetry-function interrelation and hence offer an excellent basis for the geometrical manipulation of optical properties of TBGQDs as building blocks for novel integrated carbon optoelectronics.

The threat of global warming and the demand for reliable climate predictions pose a formidable challenge because the climate system is multiscale, high-dimensional and nonlinear. Spatiotemporal recurrences of the system hint to the presence of a low-dimensional manifold containing the high-dimensional climate trajectory that could make the problem more tractable. Here we argue that reproducing the geometrical and topological properties of the low-dimensional attractor should be a key target for models used in climate projections. In doing so, we propose a general data-driven framework to characterize the climate attractor and showcase it in the tropical Pacific Ocean using a reanalysis as observational proxy and two state-of-the-art models. The analysis spans four variables simultaneously over the periods 1979–2019 and 2060–2100. At each time $t$, the system can be uniquely described by a state space vector parametrized by $N$ variables and their spatial variability. The dynamics is confined on a manifold with dimension lower than the full state space that we characterize through manifold learning algorithms, both linear and nonlinear. Nonlinear algorithms describe the attractor through fewer components than linear ones by considering its curved geometry, allowing for visualizing the high-dimensional dynamics through low-dimensional projections. The local geometry and local stability of the high-dimensional, multivariable climate attractor are quantified through the local dimension and persistence metrics. Model biases that hamper climate predictability are identified and found to be similar in the multivariate attractor of the two models during the historical period while diverging under the warming scenario considered. Finally, the relationships between different subspaces (univariate fields), and therefore among climate variables, are evaluated. The proposed framework provides a comprehensive, physically based, test for assessing climate feedbacks and opens new avenues for improving their model representation.

At cell-matrix contacts, an elastocapillary effect arises in addition to active cell traction. The coexistence of active extracellular traction and other interfacial forces at the contacts blurs their distinctive roles in tissue morphogenesis, wound healing, and cancer metastasis. Here, we first observe that cell colonies can indent soft hydrogels, forming dimples. At inhibited cell contractility, the dimples still occur but with a reduced depth. To exclude active cell traction at the cell-matrix contact, we inhibit cell contractility and measure the interfacial stresses at the colony-hydrogel-medium three-phase interfaces using the Neumann triangle method. The critical elastocapillary length scale of the colony-hydrogel contacts is found to be comparable to the cell colony size, at which the classical Johnson-Kendall-Roberts contact theory fails. Theoretical and computational models suitable for different critical elastocapillary length scales are developed to predict the elastocapillarity-induced indentation depth. A two-step traction force microscopy is, therefore, proposed to separate active cell traction from the interfacial stresses. By quantifying the elastocapillary effect separated from active cell traction, our study lays down a foundation for understanding a variety of elastocapillarity-induced mechanobiological processes in cells and tissues.

Ventricular fibrillation (VF) is a life-threatening electromechanical dysfunction of the heart associated with complex spatiotemporal dynamics of electrical excitation and mechanical contraction of the heart muscle. It has been hypothesized that VF is driven by three-dimensional rotating electrical scroll waves, which can be characterized by filamentlike electrical phase singularities or vortex filaments, but visualizing their dynamics has been a long-standing challenge. Recently, it was shown that rotating excitation waves during VF are associated with rotating waves of mechanical deformation. Three-dimensional mechanical scroll waves and mechanical filaments describing their rotational core regions were observed in the ventricles by using high-resolution ultrasound. The findings suggest that the spatiotemporal organization of cardiac fibrillation may be assessed from waves of mechanical deformation. However, the complex relationship between excitation and mechanical waves during VF is currently not understood. Here, we study the fundamental nature of mechanical phase singularities, their spatiotemporal organization, and their relation with electrical phase singularities. We demonstrate the existence of two fundamental types of mechanical phase singularities: “paired singularities,” which are colocalized with electrical phase singularities, and “unpaired singularities,” which can form independently. We show that the unpaired singularities emerge due to the anisotropy of the active force field, generated by fiber anisotropy in cardiac tissue, and the nonlocality of elastic interactions, which jointly induce strong spatiotemporal inhomogeneities in the strain fields. The inhomogeneities lead to the breakup of deformation waves and create mechanical phase singularities, even in the absence of electrical singularities, which are typically associated with excitation wave break. We exploit these insights to develop an approach to discriminate paired and unpaired mechanical phase singularities, which could potentially be used to locate electrical rotor cores from a mechanical measurement. Our findings provide a fundamental understanding of the complex spatiotemporal organization of electromechanical waves in the heart and a theoretical basis for the analysis of high-resolution ultrasound data for the three-dimensional mapping of heart rhythm disorders.

The mapping of the wiring diagrams of neural circuits promises to allow us to link the structure and function of neural networks. Current approaches to analyzing such connectomes rely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph-theory metrics convey little information about the similarity of networks’ responses. Instead, we learn a functional metric between networks based on their synaptic differences and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features—the sum of synaptic inputs that each neuron receives and the sum of each neuron’s synaptic outputs—predicts the functional similarity of networks of up to 1000 neurons, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks. These architectural features conform with experimental evidence of homeostatic synaptic mechanisms.

In correlated electronic systems, strong interactions and the interplay between different degrees of freedom may give rise to anomalous charge-transport properties, which can be tuned by external parameters like temperature and magnetic field. Recently, magnetic quantum oscillations and metallic low-temperature thermal conductivity have been observed in the Kondo insulator ${\mathrm{YbB}}_{12}$, whose resistivity is a few orders of magnitude higher than those of conventional metals. As yet, these unusual observations are not fully understood. Here we present a detailed investigation of the behavior of ${\mathrm{YbB}}_{12}$ under intense magnetic fields using both transport and torque magnetometry measurements. The Hall resistivity displays a strongly nonlinear field dependence which cannot be described using a standard two-band Drude model. A low-field Hall anomaly, reminiscent of the Hall response associated with “strange-metal” physics, develops at $T<1.5\mathrm{K}$. At two characteristic magnetic fields (${\mu }_0{H}_1=19.6\mathrm{T}$ and ${\mu }_0{H}_2\sim 31\mathrm{T}$), signatures appear in the Hall coefficient, magnetic torque, and magnetoresistance; the latter characteristic field coincides with the occurrence of a change in quantum-oscillation frequency. We suggest that they are likely to be field-induced Lifshitz transitions. Moreover, above 35 T, where the most pronounced quantum oscillations are detected, the background resistivity displays an unusual, nonmetallic $T^{\alpha }$ behavior, with $\alpha$ being field dependent and varying between $-1.5$ and $-2$ By normalizing the Shubnikov–de Haas oscillation amplitude to this $T^{\alpha }$ dependence, the calculated cyclotron mass becomes more consistent with that deduced from de Haas–van Alphen oscillations. Our results support a novel two-fluid scenario in ${\mathrm{YbB}}_{12}$: A Fermi-liquid-like fluid of charge-neutral quasiparticles coexists with charge carriers that remain in a nonmetallic state. The former experience successive Lifshitz transitions and develop Landau quantization in applied magnetic fields, while scattering between both fluids allows the Shubnikov–de Haas effect to be observed in the electrical transport. The verification of this two-fluid scenario by the data in the current work strongly suggests that ${\mathrm{YbB}}_{12}$ represents a new phase of matter.

Neutral-atom arrays have recently emerged as a promising platform for quantum information processing. One important remaining roadblock for the large-scale application of these systems is the ability to perform error-corrected quantum operations. To entangle the qubits in these systems, atoms are typically excited to Rydberg states, which could decay or give rise to various correlated errors that cannot be addressed directly through traditional methods of fault-tolerant quantum computation. In this work, we provide the first complete characterization of these sources of error in a neutral-atom quantum computer and propose hardware-efficient, fault-tolerant quantum computation schemes that mitigate them. Notably, we develop a novel and distinctly efficient method to address the most important errors associated with the decay of atomic qubits to states outside of the computational subspace. These advances allow us to significantly reduce the resource cost for fault-tolerant quantum computation compared to existing, general-purpose schemes. Our protocols can be implemented in the near term using state-of-the-art neutral-atom platforms with qubits encoded in both alkali and alkaline-earth atoms.

The biochemical reaction networks that regulate living systems are all stochastic to varying degrees. The resulting randomness affects biological outcomes at multiple scales, from the functional states of single proteins in a cell to the evolutionary trajectory of whole populations. Controlling how the distribution of these outcomes changes over time—via external interventions like time-varying concentrations of chemical species—is a complex challenge. In this work, we show how counterdiabatic (CD) driving, first developed to control quantum systems, provides a versatile tool for steering biological processes. We develop a practical graph-theoretic framework for CD driving in discrete-state continuous-time Markov networks. Though CD driving is limited to target trajectories that are instantaneous stationary states, we show how to generalize the approach to allow for nonstationary targets and local control—where only a subset of system states is targeted. The latter is particularly useful for biological implementations where there may be only a small number of available external control knobs, insufficient for global control. We derive simple graphical criteria for when local versus global control is possible. Finally, we illustrate the formalism with global control of a genetic regulatory switch and local control in chaperone-assisted protein folding. The derived control protocols in the chaperone system closely resemble natural control strategies seen in experimental measurements of heat shock response in yeast and E. coli.

The glass transition is a long-standing unsolved problem in materials science. For polymers, our understanding of glass formation is particularly poor because of the added complexity of chain connectivity and flexibility; structural relaxation of polymers thus involves a complex interplay between intramolecular and intermolecular cooperativity. Here, we study how the glass-transition temperature $T_g$ varies with molecular weight $M$ for different polymer chemistries and chain flexibilities. We find that $T_g(M)$ is controlled by the average mass (or volume) per conformational degree of freedom and that a “local” molecular relaxation (involving a few conformers) controls the larger-scale cooperative $\alpha$ relaxation responsible for $T_g$. We propose that dynamic facilitation where a local relaxation facilitates adjacent relaxations, leading to hierarchical dynamics, can explain our observations, including logarithmic $T_g(M)$ dependences. Our study provides a new understanding of molecular relaxations and the glass transition in polymers, which paves the way for predictive design of polymers based on monomer-scale metrics.