Hybrid quantum systems consisting of a collection of $N$ spin-$1/2$ particles uniformly interacting with an electromagnetic field, such as one confined in a cavity, are important for the development of quantum information processors and will be useful for metrology, as well as tests of collective behavior. Such systems are often modeled by the Tavis-Cummings model, and having an accurate understanding of the thermal behaviors of this system is needed to understand their behavior in realistic environments. We quantitatively show in this work that the Dicke subspace approximation is at times invoked too readily. Specifically, we show that there is a temperature above which the degeneracies in the system become dominant and the Dicke subspace is minimally populated. This transition occurs at a lower temperature than previously considered. In such a temperature regime, the key constants of the motion are the total excitation count between the spin system and cavity and the collective angular momentum of the spin system. These enable perturbative expansions for thermal properties in terms of the energy shifts of dressed states, called Lamb shifts herein. They enable efficient numeric methods that scale in terms of the size of the spin system. Notably the runtime to obtain certain parameters of the system scales as $O\left(\sqrt{N}\right)$, and is thus highly efficient. These provide methods for approximating, and bounding, properties of these systems as well as characterizing the dominant population regions, including under perturbative noise. In the regime of stronger spin-spin coupling, the perturbations outweigh the expansion series terms, and inefficient methods must likely be employed, removing the computational efficiency of simulating such systems. The results in this work can also be used for related systems such as coupled-cavity arrays, cavity-mediated coupling of collective spin ensembles, and collective spin systems.

We propose a method to build an astronomical interferometer using continuous-variable quantum teleportation to overcome transmission loss between distant telescopes. The scheme relies on two-mode squeezed states shared by distant telescopes as entanglement resources, which are distributed using continuous-variable quantum repeaters. We find the optimal measurement on the teleported states, which uses beam splitters and photon-number-resolved detection. Compared to prior proposals relying on discrete states, our scheme has the advantages of using linear optics to implement it without wasting stellar photons, and making use of multiphoton events, which are regarded as noise in previous discrete schemes. We also outline the parameter regimes in which our scheme outperforms the direct detection method, schemes utilizing distributed discrete-variable entangled states, and local heterodyne techniques.

Arrays of superconducting qubits and cavities offer a promising route for realizing artificial materials. However, many analog simulations on superconducting circuit hardware have focused on bosonic systems. Fermionic simulations, in contrast, have largely relied on digital approaches. Here, we propose and study an alternative approach for analog fermionic quantum simulation based on arrays of coherently coupled mesoscopic Josephson junctions. These Josephson junction arrays implement an effective superlattice of Andreev bound state atoms that can trap individual fermionic quasiparticles and, due to their wave-function overlap, mediate quasiparticles hoppings. By developing a Wannier function approach, we show that these Andreev bound state arrays form an all-superconducting platform for emulating lattice models of fermionic quasiparticles that are phase and gate programmable. Interestingly, the junction lattices can also undergo a topological transition and host fermionic boundary modes that can be probed by conductance measurements. We hope our results will inspire the realization of artificial and topological materials on Andreev bound state quantum simulators.

We study quantum-fluctuation-driven fractionalized phases in the vicinity of altermagnetic order. First, the long-range magnetic orders in the vicinity of collinear altermagnetism are identified; these feature a noncoplanar orbital altermagnet, which has altermagnetic symmetries in spin-rotation-invariant observables. We then describe neighboring fractionalized phases with topological order reached when quantum fluctuations destroy long-range spin order, within Schwinger boson theory and an SU(2) gauge theory of fluctuating magnetism. Discrete symmetries remain broken in some of the fractionalized phases, with the orbital altermagnet becoming an altermagnetic spin liquid. We compute the electronic spectral function in the doped system, which is characterized by split Fermi surfaces with preserved spin-rotation symmetry.

Variational quantum algorithms (VQAs) and their applications in the field of quantum machine learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices. However, differences in the performance of certain VQA architectures are often unclear since established best practices, as well as detailed studies, are missing. In this paper, we build upon the work by Schuld et al. [Phys. Rev. A 103, 032430 (2021)] and Vidal et al. [Front. Phys. 8, 297 (2020)] by comparing how well popular ansatz for PQCs learn different one-dimensional truncated Fourier series. We also examine dissipative quantum neural networks (dQNN) as introduced by Beer et al. [Nat. Commun. 11, 808 (2020)] and propose a data reupload structure for dQNNs to increase their capability for this regression task. By comparing the results for different PQC architectures, we can provide guidelines for designing efficient PQCs.

The integration of machine-learning techniques into inertial confinement fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal-component spaces compared to conventional transformations.

We study thermalization slowing down of a quantum many-body spin system upon approach to two distinct integrability limits. Motivated by previous studies of classical systems, we identify two thermalization timescales: one quantum Lyapunov timescale is extracted by quantifying operator growth in time on an appropriately defined basis, while another ergodization timescale is related to the statistics of fluctuations of the time-evolved operator around its mean value based on the eigenstate thermalization hypothesis. Using a paradigmatic quantum Ising chain, we find that both timescales diverge upon approach to integrability. We investigate the relative strength of the divergence in the two limits and find that, despite significant qualitative differences in the mechanism of integrability breaking, the timescales diverge in a similar fashion. This allows us to establish a universality of integrability breaking in quantum spin dynamics.

We explore the mechanism underlying the anomalous rheology of puller-type microswimmer suspensions through direct hydrodynamic simulations. Puller-type swimmers generate contractile flow fields along their swimming direction, leading to hydrodynamic interactions that promote a tendency for head-to-tail alignment among the swimmers. Our simulations reveal that this alignment effect, along with the resultant orientational order of swimming motion, becomes particularly pronounced near boundary walls, where local swimmer density is amplified, predominantly controlling the overall swimming dynamics and rheological properties of the suspension. These findings contrast with our previous simulations of pusher-type swimmers, which hydrodynamically interact through extensile flow fields, whereby they exhibit weak orientational order in the bulk region, which primarily determines their steady-state properties. Furthermore, we demonstrate that the steady-state behavior near the walls is strongly influenced by the aspect ratio of the microswimmers and the degree of confinement between the walls. Our results highlight the crucial role of microswimmer characteristics, such as shape and swimming mechanisms, in determining the rheological properties of active suspensions.

We report on the discovery of superconductivity in the previously unknown compound ${\mathrm{Ta}}_4{\mathrm{Rh}}_2{\mathrm{C}}_{1-\delta }$. ${\mathrm{Ta}}_4{\mathrm{Rh}}_2{\mathrm{C}}_{1-\delta }$ crystallizes in the $\eta$-carbide structure type, in the cubic space group $Fd\overline{3}m$ (No. 227) with a unit cell parameter of $a=11.7947$ Å. Temperature-dependent magnetic susceptibility, resistivity, and specific-heat capacity measurements reveal that ${\mathrm{Ta}}_4{\mathrm{Rh}}_2{\mathrm{C}}_{1-\delta }$ is a type-II bulk superconductor with a critical temperature of $T_{\mathrm{c}}=6.4\mathrm{K}$ and a normalized specific-heat jump $\mathrm{\Delta }C/\gamma T_{\mathrm{c}}=1.56$. Notably, we find that ${\mathrm{Ta}}_4{\mathrm{Rh}}_2{\mathrm{C}}_{1-\delta }$ has a high upper critical field of ${\mu }_0{H}_{\mathrm{c}2}\left(0\right)=17.4\mathrm{T}$, which exceeds the BCS weak-coupling Pauli limit of ${\mu }_0{H}_{\mathrm{Pauli}}=11.9\mathrm{T}$.

We provide a simple framework for the study of parametric (multiplicative) noise, making use of scale parameters. We focus on a large class of one-dimensional stochastic differential equations in which the deterministic drift pushes trajectories toward infinity. We show that increasing the multiplicative noise intensity surprisingly causes the mass of the stationary probability distribution to become increasingly concentrated around the points of minimal multiplicative noise strength. Under quite general conditions the trajectory exhibits intermittent burstlike jumps away from these minima. Our framework relies on first-term expansions, which become more accurate for larger noise intensities. In this work we show that the full width at half maximum in addition to the maximum is appropriate for quantifying the stationary probability distribution (instead of the mean and variance, which are often undefined). We define a corresponding kind of weak-sense stationarity. We end by applying these results to the problem of a double-well potential with multiplicative noise, where noise stabilizes unstable fixed points.

Biological cells exhibit a hierarchical spatial organization, where various compartments harbor condensates that form by phase separation. Cells can control the emergence of these condensates by affecting compartment size, the amount of the involved molecules, and their physical interactions. While physical interactions directly affect compartment binding and phase separation, they can also cause oligomerization, which has been suggested as a control mechanism. Analyzing an equilibrium model, we illustrate that dimerization amplifies compartment binding and phase separation, which reinforce each other. This nonlinear interplay can also induce multistability, which provides additional potential for control. Our work forms the basis for deriving thermodynamically consistent kinetic models to understand how biological cells can regulate phase separation in their compartments.

Collective motion in living systems is observed across many scales, from bacterial colonies to insect swarms, fish schools, and bird flocks. In all cases the motility of individual cells or organisms both is influenced by and, in turn, influences the motion of others. While the mechanisms by which individuals sense and respond to others differ greatly among species, simplified models that capture general principles of social interactions, such as the Vicsek model, have proved insightful. Here we introduce anisotropic sensory perception (a feature common to many animal species) into the Vicsek model in order to evaluate how this feature impacts collective motion. We find that, when the anisotropy $\alpha >0$, meaning individuals are more influenced by neighbors ahead of them than those behind them, as the anisotropy becomes stronger, the swarming transition changes from the weak first-order transition observed for populations with isotropic perception to a stronger (i.e., more abrupt) first-order transition. Moreover, the critical noise threshold to achieve ordered motion decreases if $\alpha$ increases from zero to positive values. By contrast, when the anisotropy $\alpha <0$, indicating individuals pay more attention to rearward neighbors, as the anisotropy becomes stronger, the order-disorder transition remains first order (at the thermodynamic limit) but becomes increasingly weak (less abrupt), and the critical noise threshold required to reach ordered motion increases slightly. An even simpler front-back semicircle model and an anisotropic model with vectorial noise are employed to further validate the respective roles those ahead and behind play in the emergence of collective motion. Taken together, our results demonstrate that the contribution of neighboring individuals to the collective motion in such particle systems significantly depends on their relative position to the focal particle. Frontal neighbors primarily enhance the strength of the first-order transition, whereas rearward neighbors progressively weaken it.

We propose a realization of the one-dimensional Kitaev topological superconductor in classical mechanical metamaterials. By designing appropriate braiding protocols, we demonstrate that the system's midgap vibrational modes, termed classical Majorana zero modes (MZMs), accurately reproduce the braiding statistics predicted by quantum theory. Encoding four MZMs as a classical analog of a qubit, we implement all single-qubit Clifford gates through braiding, enabling the simulation of topological quantum computation in a classical system. Furthermore, we establish the system's topological protection by demonstrating its robustness against mechanical defects. This work provides a different framework for exploring topological quantum computation using classical metamaterials and offers a pathway to realizing stable vibrational systems protected by topology.

Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with constraints. The pipeline decomposes the optimization problem into constrained subproblems, which are then solved separately and aggregated to give a final result. Our pipeline includes three main components: preprocessing of correlation matrices based on random matrix theory, modified spectral clustering based on Newman's algorithm, and risk rebalancing. Our empirical results show that our pipeline consistently decomposes real-world portfolio optimization problems into subproblems with a size reduction of approximately 80%. Since subproblems are then solved independently, our pipeline drastically reduces the total computation time for state-of-the-art solvers. Moreover, by decomposing large problems into several smaller subproblems, the pipeline enables the use of near-term quantum devices as solvers, providing a path toward practical utility of quantum computers in portfolio optimization.

Solving combinatorial optimization problems on near-term quantum devices has gained a lot of attention in recent years. Currently, most studies have focused on single-objective problems, whereas many real-world applications need to consider multiple, mostly conflicting objectives, such as cost and quality. We present a variational quantum optimization algorithm to solve discrete multiobjective optimization problems on quantum computers. The proposed quantum multiobjective optimization (QMOO) algorithm incorporates all cost Hamiltonians representing the classical objective functions in the quantum circuit and produces a quantum state consisting of Pareto-optimal solutions in superposition. From this state, we retrieve a set of solutions and utilize the widely applied hypervolume indicator to determine its quality as an approximation to the Pareto front. The variational parameters of the QMOO circuit are tuned by maximizing the hypervolume indicator in a quantum-classical hybrid fashion. We show the effectiveness of the proposed algorithm on several benchmark problems with up to five objectives. We investigate the influence of the classical optimizer and the circuit depth, and compare them to results from classical optimization algorithms. We find that the algorithm is robust to shot noise and produces good results with as few as 128 measurement shots in each iteration. These promising results open the possibility to run the algorithm on near-term quantum hardware.

Prethermalization occurs as an important phase in the dynamics of isolated many-body systems when coupled degrees of freedom relax in a subspace separated from a state of complete thermodynamic equilibrium by a gap in energy or other conserved quantity. Slow equilibration from a prethermal phase can localize the dynamics of natural and model systems despite high dimensionality and limited disorder. Here, we report the signature of an enduring prethermal regime of arrested relaxation in the molecular ultracold plasma that forms following the avalanche of a state-selected Rydberg gas of nitric oxide. For a wide range of initial conditions, this system enters a critical phase in which a density of ${\mathrm{NO}}^{+}$ and electrons balances a population of Rydberg molecules. Electron collisions mix orbital angular momentum, scattering Rydberg electrons to states of very high $\ell$. The rapid predissociation of molecules in states of low-$\ell$ purifies this high-$\ell$ ensemble. The angular momentum barrier that separates ${\mathrm{NO}}^{+}$ ions and electrons creates an extraordinary gap between the plasma states of $n\approx \ell$, with measured $n>200$ and penetrating states of $\ell =0,1$, and 2. Evolution to a statistically equilibrated state of N and O atoms cannot occur without Rydberg electron penetration, and this gap blocks relaxation for a millisecond or more. A weak radiofrequency (RF) field drives $\ell$-mixing electron collisions that erase this gap, causing wholesale dissipation. Remarkably, a local quantum-state transition promoting an exceedingly small fraction of the molecules in the prethermalized ensemble to a predissociative state also acts with a global effect, driving the entire system to a dissipative equilibrium. Using the Lindblad master equation, we illustrate qualitatively similar dynamics for a toy model of an open quantum system that consists of a localized set of spins on which dissipation acts at a single site as a gateway to equilibrium.

Optimization of circuits is an essential task for both quantum and classical computers to improve their efficiency. In contrast, classical logic optimization is known to be difficult, and a lot of heuristic approaches have been developed so far. In this paper, we define and construct a quantum algorithmic primitive called quantum circuit unoptimization, which makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence, i.e., the inverse operation of circuit optimization. Using quantum circuit unoptimization, we propose the quantum circuit equivalence test, a decision problem contained both in NP and BQP classes, but is not trivially included in the P class. Furthermore, as a practical application, we construct concrete unoptimization recipes to generate compiler benchmarks and evaluate circuit optimization performance using Qiskit and Pytket. Our numerical simulations demonstrate that the quantum circuit unoptimizer systematically generates redundant circuits that are challenging for compilers to optimize, which can be used to compare the performance of different compilers and improve them. We also offer potential applications of quantum circuit unoptimization, such as generating quantum advantageous machine learning data sets and quantum computer fidelity benchmarks.

The assertion of intrinsic material properties based on measured experimental data is being challenged by emerging sample synthesis protocols, which opens new avenues for discovering novel functionalities. In this study, we revisit one of the most widely studied strongly correlated materials of the early 2000s, ${\mathrm{Na}}_x{\mathrm{CoO}}_2$ (NCO). Leveraging the sensitivity of muon spin rotation and relaxation (${\mu }^{+}\mathrm{SR}$) measurements, we discern significant differences between NCO samples synthesized via conventional solid-state reaction (SSR) and our electrochemical reaction (ECR) approach. Contrary to SSR-synthesized ${\mathrm{Na}}_{0.7}{\mathrm{CoO}}_2$, which exhibits a nonmagnetic ground state, our ECR-derived sample showcases an antiferromagnetic (AF) order from $x\ge 0.7$, challenging established phase boundaries. We attribute the observed magnetic phenomena in ECR-NCO to long-range order of Na-ions and/or vacancies, as well as the inherent flexibility of the crystal framework. Our study holds implications for tailoring and optimization of next-generation devices based on layered materials.

We present a laser-based neutron source that produces $1.8\times {10}^5\text{neutrons/s}$ with a conversion rate of $7.8\times {10}^5\text{neutrons/J}$. Laser pulses of 12 fs and 23 mJ were focused onto a $430\text{-nm}$-thick heavy water liquid sheet at a 10 Hz repetition rate. The resulting peak intensity of $4\times {10}^{18}{\text{W/cm}}^2$ accelerated deuterium ions from the target rear side to a kinetic energy of 1 MeV. This deuteron beam induced ${}^2\mathrm{H}{(d,n)}^3\text{He}$ fusion reactions in a deuterated polyethylene target, producing fast neutrons. The neutron yield was measured using two independent detection systems: the LILITH time-of-flight spectrometer, consisting of eight plastic scintillators covering nearly ${180}^{\circ }$, and a calibrated bubble detector spectrometer. The neutron yield per laser shot is 35 times higher than that recently achieved by lasers with comparable pulse energies, while the conversion rate is the highest ever achieved by continuously operating, sub-100 fs lasers. The generated neutrons are emitted from an area of $0.65{\text{cm}}^2$ corresponding to the deuteron beam spot on the catcher. Their angular distribution is peaked in forward and backward directions in agreement with the literature data on the angular distribution of ${}^2\mathrm{H}{(d,n)}^3\text{He}$ reaction. The system operated continuously for several hours per day with an unprecedented stability of $5\%$.