Kerr parametric oscillators (KPOs) can stabilize the superpositions of coherent states, which can be utilized as qubits, and are promising candidates for realizing hardware-efficient quantum computers. Although elementary gates for universal quantum computation with KPO qubits have been proposed, these gates are usually based on adiabatic operations and thus need long gate times, which result in errors caused by photon loss in KPOs realized by, e.g., superconducting circuits. In this work we accelerate the elementary gates by experimentally feasible control methods, which are based on numerical optimization of pulse shapes for shortcuts to adiabaticity. By numerical simulations, we show that the proposed methods can achieve speedups compared to adiabatic ones by up to six times with high gate fidelities of 99.9%. These methods are thus expected to be useful for quantum computers with KPOs.

The quantum phase transition between the low-field fracton phase with type-II fracton excitations and the high-field polarized phase is investigated in the two-dimensional self-dual quantum Newman-Moore model. We apply perturbative and numerical linked-cluster expansions to calculate the ground-state energy per site in the thermodynamic limit, revealing a level crossing at the self-dual point. In addition, high-order series expansions of the relevant low-energy gaps are determined using perturbative continuous unitary transformations indicating no gap closing. Our results therefore predict a first-order phase transition between the low-field fracton and the high-field polarized phase at the self-dual point.

The dynamics of a probe particle is highly influenced by the nature of the bath in which it is immersed. In particular, baths composed by active (e.g., self-propelled) particles induce intriguing out-of-equilibrium effects on tracer's motion that are customarily described by integrating out the dynamics of the bath's degrees of freedom (DOFs). However, thermodynamic quantities, such as the entropy production rate, are generally severely affected by coarse-graining procedures. Here, by employing an exact integration of a subset of DOFs, we show that two classes of DOFs emerge, entropic and nonentropic. By inspecting the resulting generalized Langevin equation (GLE) for the observed variables, it turns out that active baths are associated with the presence of entropic DOFs exhibiting nonreciprocal interactions with a probe particle. Surprisingly, integrating out these DOFs inevitably results into a system-dependent increase or reduction of the entropy production rate, at variance with other coarse-graining schemes. In this case, the resulting GLE is not thermodynamically consistent. On the contrary, the entropy production rate stays invariant after integrating out nonentropic DOFs and the resulting GLE preserves the thermodynamics of the full system. Additionally, the number of nonentropic DOFs determines the dimensionality of isoentropic hypersurfaces in the parameter space. Our results shed light on the nature of active baths, revealing that the presence of a typical correlation timescale is not a sufficient condition to have nonequilibrium effects on a probe particle, and draws a path towards the understanding of thermodynamically consistent procedures to derive effective dynamics of observed DOFs.

Using a combination of density functional theory calculations with an on-site Coulomb repulsion term ($\mathrm{DFT}+U$) and Boltzmann transport theory within the constant relaxation time approximation, we explore the effect of oxygen vacancies on the electronic, magnetic, and thermoelectric properties in ultrathin ${\left({\mathrm{LaNiO}}_{3-\delta }\right)}_1/{\left({\mathrm{LaAlO}}_3\right)}_1\left(001\right)$ superlattices (SLs). For the pristine SL ($\delta =0$), an antiferromagnetic charge-disproportionated (AFM-CD) ($d^8\underline{L}{}^2{)}_{S=0}{\left(d^8\right)}_{S=1}$ phase is stabilized, irrespective of strain. At $\delta =0.125$ and 0.25, the localization of electrons released from the oxygen defects in the ${\mathrm{NiO}}_2$ plane triggers a charge-disproportionation, leading to a ferrimagnetic insulator both at $a_{{\mathrm{SrTiO}}_3}$ (tensile strain) and $a_{{\mathrm{LaSrAlO}}_4}$ (compressive strain). At $\delta =0.5$, an insulating phase emerges with alternating stripes of ${\mathrm{Ni}}^{2+}$ (high-spin) and ${\mathrm{Ni}}^{2+}$ (low-spin) and oxygen vacancies ordered along the [110] direction (S-AFM), irrespective of strain. This results in a robust $n$-type in-plane power factor of $24\mu \mathrm{W}/{\mathrm{K}}^2$ cm at $a_{\mathrm{STO}}$ and $14\mu \mathrm{W}/{\mathrm{K}}^2$ cm at $a_{\mathrm{LSAO}}$ at 300 K (assuming relaxation time $\tau =4$ fs). Additionally, the pristine and $\delta$ = 0.5 SLs are shown to be dynamically stable. This demonstrates the fine tunability of electronic, magnetic, and thermoelectric properties of ultrathin nickelate superlattices by oxygen vacancies.

We present a set of robust and high-fidelity pulses that realize paradigmatic operations such as the transfer of the ground state population into the excited state and arbitrary $X/Y$ rotations on the Bloch sphere. These pulses are based on the phase modulation of the control field. We provide an experimental proof-of-concept of these operations by using a transmon qubit, demonstrating resilience against deviations in the drive amplitude of more than $\approx 20\%$, and/or detuning from the qubit transition frequency in the order of $10\mathrm{MHz}$. This modulation scheme is straightforward to implement in practice and can be deployed to any other qubit-based experimental platform.

Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead.

Inseparability of quantum states is one of the most crucial aspects of quantum physics, as it often provides the key ingredient for obtaining a quantum advantage. Quantifying inseparability is thus an important objective to better understand quantum physics and to improve quantum applications. So far, many measures for inseparability exist, however, most of them are based on abstract mathematical procedures and are not defined operationally which can easily be realized in an experiment. Recently we introduced average correlation [Tschaffon et al., Phys. Rev. Res. 5, 023063 (2023)] as a reference frame independent indicator for nonclassicality in the Bell sense based on randomized measurements. It is defined as the average absolute value of the two-qubit correlation function and allows one to formulate a necessary and sufficient condition for the ability to violate the CHSH inequality. Experimentally it can be realized by randomized measurements which can approximate its value arbitrarily closely. This makes it independent of a shared reference frame between the two measuring parties, which can be useful in scenarios where it is impossible to find one. In this second article, we show that average correlation also serves as an indicator for inseparability by deriving a necessary and sufficient condition. From there, we prove the remaining open conjectures of the first article. Due to the operational definition of average correlation, it offers a first step toward finding a new operationally defined inseparability measure.

We investigate the $c$-direction conduction in CrSBr in the linear regime, which is not accessible in other van der Waals (vdW) magnetic semiconductors, because of the unmeasurably low current. The resistivity, which is ${10}^8-{10}^{11}$ times larger than in the $a$ and $b$ directions, exhibits magnetic state dependent thermally activated and variable range hopping transport. In the spin-flip phase at 2 T, the activation energy is 20 meV lower than in the antiferromagnetic state due to a downshift of the conduction band edge, in agreement with ab initio calculations. In the variable range hopping regime, the average hopping length decreases from twice the interlayer distance to the interlayer distance at 2 T because in the antiferromagnetic state the large exchange energy impedes electrons hopping between adjacent layers. Our work demonstrates that the linear transport regime provides new information about electronic processes in vdW magnetic semiconductors and shows how magnetism influences these processes both in real and reciprocal space.

We report the creation of dual-species Bose-Einstein condensates (BECs) of ${}^{23}\mathrm{Na}$ and ${}^{41}\mathrm{K}$. Favorable background scattering lengths enable efficient sympathetic cooling of ${}^{41}\mathrm{K}$ via forced evaporative cooling of ${}^{23}\mathrm{Na}$ in a plugged magnetic trap and an optical dipole trap. The $1/e$ lifetime of the thermal mixture in the stretched hyperfine state exceeds 5 s in the presence of background scattering. At the end of evaporation, we create dual BECs in the immiscible phase, with about $3\times {10}^5 {}^{23}\mathrm{Na}$ atoms surrounding $5\times {10}^4 {}^{41}\mathrm{K}$ atoms. To further enable the tuning of the interspecies interaction strength, we locate multiple Feshbach resonances at magnetic fields up to 100 G. The broadest $s$-wave resonance located at 73.4(3) G features a favorable width of 1.8(2) G. This work sets the stage for the creation of ultracold gases of strongly dipolar bosonic ${}^{23}\mathrm{Na}{}^{41}\mathrm{K}$ molecules as well as the exploration of many-body physics in bosonic ${}^{23}\mathrm{Na}\text{−}{}^{41}\mathrm{K}$ mixtures.

The working mechanisms of complex natural systems tend to abide by concise partial differential equations (PDEs). Methods that directly mine equations from data are called PDE discovery, which reveals consistent physical laws and facilitates our interactions with the natural world. In this paper, an enhanced deep reinforcement-learning framework is proposed to uncover symbolically concise open-form PDEs with little prior knowledge. Particularly, based on a symbol library of basic operators and operands, a PDE can be represented by a tree structure. A structure-aware recurrent neural network agent is designed to capture structured information, and is seamlessly combined with the sparse regression method to generate open-form PDE expressions. All of the generated PDEs are evaluated by a meticulously designed reward function by balancing fitness to data and parsimony, and updated by the model-based reinforcement learning. Customized constraints and regulations are formulated to guarantee the rationality of PDEs in terms of physics and mathematics. Numerical experiments demonstrate that our framework is capable of mining open-form governing equations of several dynamic systems, even with compound equation terms, fractional structure, and high-order derivatives. This method is also applied to a real-world problem of the oceanographic system and demonstrates great potential for knowledge discovery in more complicated circumstances with exceptional efficiency and scalability.

Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurately whether the perturbed systems will return to the functional state after the transient or collapse. Whereas model-based studies show that the key to addressing the challenge lies in the attracting basins of the functional and dysfunctional states in the phase space, the finding of the attracting basins for realistic power systems remains a challenge, as accurate models describing the system dynamics are generally unavailable. Here we propose a new machine-learning technique, namely balanced reservoir computing, to infer the attracting basins of a typical power system based on measured data. Specifically, trained by the time series of a handful of perturbation events, we demonstrate that the trained machine can predict accurately whether the system will return to the functional state in response to a large, random perturbation, thereby reconstructing the attracting basin of the functional state. The working mechanism of the new machine is analyzed, and it is revealed that the success of the new machine is attributed to the new objective function adopted in optimizing the machine hyperparameters, which keeps a good balance between the echo and fading properties of the reservoir network; the effect of noisy signals on the prediction performance is also investigated, and it is found that the machine performance is improved by introducing a certain amount of noise. Finally, we demonstrate that the new technique can also be utilized to infer the attracting basins of coexisting attractors in typical chaotic systems.

A quantum Otto engine based on a three-dimensional harmonic oscillator is proposed. One of the modes of this oscillator functions as the working fluid, while the other two play the role of baths. The coupling between the working fluid and the baths is controlled using an external central potential. All four strokes of the engine are simulated numerically, exploring the nonadiabatic effects in the compression and expansion phases, as well as the energy transfer during the working fluid's contact with the baths. The efficiency and power of several realizations of the proposed engine are also computed with the former agreeing well with the theoretical predictions for the quantum Otto cycle.

Electromagnetic remote sensing technologies such as radar can be misled by targets that generate spoof pulses. Typically, a would-be spoofer must make measurements to characterize a received pulse in order to design a convincing spoof pulse. The precision of such measurements is ultimately limited by quantum noise. Here we introduce a model of electromagnetic spoofing that includes effects of practical importance that were neglected in prior theoretical studies. In particular, the model includes thermal background noise and digital quantization noise, as well as loss in transmission, propagation, and reception. We derive the optimal probability of detecting a spoofer allowed by quantum physics. We show that heterodyne reception and thresholding closely approaches this optimal performance. Finally, we show that a high degree of certainty in spoof detection can be reached by Bayesian inference from a sequence of received pulses. Together these results suggest that a practically realizable receiver could plausibly detect a radar spoofer by observing errors in the spoof pulses due to quantum noise.

Many biological active agents respond to gradients of environmental cues by redirecting their motion. In addition to the well-studied prominent examples such as phototaxis and chemotaxis, there has been considerable recent interest in topotaxis, i.e., the ability to sense and follow topographic environmental cues. A trivial topotaxis is achievable through a spatial gradient of obstacle density, though over limited length scales. Here, we introduce a type of topotaxis based on sliding of particles along obstacles—as observed, e.g., in bacterial dynamics near surfaces. We numerically demonstrate how imposing a gradient in the angle of sliding along pillars breaks the spatial symmetry and biases the direction of motion, resulting in an efficient topotaxis in a uniform pillar park. By repeating blocks of pillars with a strong gradient of sliding angle, we propose an efficient method for guiding particles over arbitrary long distances. We provide an explanation for this spectacular phenomenon based on effective reflection at the borders of neighboring blocks. Our results are of technological and medical importance for design of efficient taxis devices for living agents.

Quantum error-correcting (QEC) stabilizer codes enable protection of quantum information against errors during storage and processing. Simulation of noisy QEC codes is used to identify the noise parameters necessary for advantageous operation of logical qubits in realistic quantum computing architectures. Typical quantum error-correction techniques contain intermediate measurements and classical feedback that determine the actual noisy circuit sequence in an instance of performing the protocol. Dynamical subset sampling enables efficient simulation of such nondeterministic quantum error-correcting protocols for any type of quantum circuit and incoherent noise of low strength. As an importance sampling technique, dynamical subset sampling allows one to effectively make use of computational resources to only sample the most relevant sequences of quantum circuits in order to estimate a protocol's logical failure rate with well-defined error bars. We demonstrate the capabilities of dynamical subset sampling with examples from fault-tolerant (FT) QEC. We show that, in a typical stabilizer simulation with incoherent Pauli noise of strength $p={10}^{-3}$, our method can reach a required sampling accuracy on the logical failure rate with two orders of magnitude fewer samples than direct Monte Carlo simulation. Furthermore, dynamical subset sampling naturally allows for efficient simulation of realistic multi-parameter noise models describing faulty quantum processors. It can be applied not only for QEC in the circuit model but any noisy quantum computing framework with incoherent fault operators including measurement-based quantum computation and quantum networks.