The idea of simulating quantum physics with controllable quantum devices had been proposed several decades ago. With the extensive development of quantum technology, large-scale simulation, such as the analog quantum simulation tailoring an artificial Hamiltonian mimicking the system of interest, has been implemented on elaborate quantum experimental platforms. However, due to the limitations caused by the significant noises and the connectivity, analog simulation is generically infeasible on near-term quantum computing platforms. Here we propose an alternative analog simulation approach on near-term quantum devices. Our approach circumvents the limitations by adaptively partitioning the bath into several groups based on the performance of the quantum devices. We apply our approach to simulate the free induction decay of the electron spin in a diamond $\mathrm{N}{\mathrm{V}}^{-}$ center coupled to a huge number of nuclei and investigate the nonclassicality induced by the nuclear spin polarization. The simulation is implemented collaboratively with authentic devices and simulators on IBM quantum computers. We have also applied our approach to address the nonclassical noise caused by the crosstalk between qubits. This work sheds light on a flexible approach to simulate large-scale materials on noisy near-term quantum computers.

A high harmonic generation (HHG) scheme, which does not rely on tunnel ionization as the ionization mechanism but rather on single-photon ionization, is theoretically proposed and numerically demonstrated. The scheme uses two driver fields: an extreme-ultraviolet driver which induces the ionization, and a circularly polarized, corotating, two-color infrared driver carried at a fundamental frequency and its second harmonic which induces the recollision. Using classical and time-dependent Schrödinger equation simulations of a model argon atom, we show that in this scheme ionization is essentially decoupled from recollision. Releasing the HHG mechanism from being tunneling dependent reduces its degree of nonlinearity, which offers interesting capabilities in attosecond science, such as generation of high harmonics from highly charged ions, or from specific deep core electronic levels. It is shown that the emitted high harmonics involve the absorption of photons of one color of the infrared driver, and the emission of photons of the second color. This calls for future examination of the possible correlations between the emitted high harmonics.

Generation of logical zero states encoded with a quantum error-correcting code is the first step for fault-tolerant quantum computation but requires considerably large resource overheads in general. To reduce such overheads, we propose an efficient encoding method for the distance-three, nine-qubit surface code and show its fault tolerance. This method needs no measurement, unlike other fault-tolerant encoding methods. Moreover, this is applicable to a one-dimensional qubit array. Observing these facts, we experimentally demonstrate the logical-zero-state encoding of the surface code using a superconducting quantum computer on the cloud. We also experimentally demonstrate the suppression of fast dephasing due to intrinsic residual interactions in this machine by a dynamical decoupling technique dedicated to the qubit array. To extend this method to larger codes, we also investigate the concatenation of the surface code with itself, resulting in a distance-nine, 81-qubit code. We numerically show that fault-tolerant encoding of this large code can be achieved by appropriate error detection.

The variational quantum eigensolver (VQE) is a hybrid algorithm to find the minimum eigenvalue/vector of the given Hamiltonian by optimizing a parameterized quantum circuit (PQC) using a classical computer. Sequential optimization methods, which are often used in quantum circuit tensor networks, are popular for optimizing the parameterized gates of PQCs. In this paper, we focus on the case where the components to be optimized are single-qubit gates, in which the analytic optimization of a single-qubit gate is sequentially performed. The analytical solution is given by diagonalization of a matrix whose elements are computed from the expectation values of observables specified by a set of predetermined parameters, which we refer to as the parameter configurations. In this paper, we first show that the optimization accuracy significantly depends on the choice of parameter configurations owing to the statistical errors in the expectation values. We then identify a metric that quantifies the optimization accuracy of a parameter configuration for all possible statistical errors, named configuration overhead/cost or C-cost. We theoretically provide the lower bound of C-cost and show that, for the minimum size of parameter configurations, the lower bound is achieved if and only if the parameter configuration satisfies the so-called equiangular line condition. Finally, we provide numerical experiments demonstrating that the optimal parameter configuration exhibits the best result in several VQE problems. We hope that this general statistical methodology will enhance the efficacy of sequential optimization of PQCs for solving practical problems with near-term quantum devices.

Giant atoms are attracting interest as an emerging paradigm in the quantum optics of engineered waveguides. At variance with the well-known artificial giant atoms for microwave photonics, here we propose the archetype of a giant atom working in the optical regime by considering a pair of interacting Rydberg atoms coupled to a photonic crystal waveguide (PCW) and also driven by a coherent field. Giant-atom effects are observed as a phase-dependent decay of the double Rydberg excitation during the initial evolution stage while a nontrivial internal entanglement is exhibited at later times. Such an entanglement onset occurs in the presence of intrinsic atomic decay toward nonguided vacuum modes and is accompanied by antibunching in the emitted photons. Our predictions should be observable in current Rydberg-PCW experiments and may open the way toward giant-atom optical photonics for quantum information processing.

We here reply to a recent comment by Vaidman [Phys. Rev. Res. 5, 048001 (2023)] on our paper [Phys. Rev. Res. 5, 023048 (2023)]. In his Comment, Vaidman first admits that he is just defining (assuming) the weak trace gives the presence of a particle—however, in this case, he should use a term other than presence, as this already has a separate, intuitive meaning other than “where a weak trace is.” Despite this admission, Vaidman then goes on to argue for this definition by appeal to ideas around an objectively existing idea of presence. We show these appeals rely on their own conclusion—that there is always a matter of fact about the location of a quantum particle.

In a recent paper, Hance et al. [Phys. Rev. Res. 5, 023048 (2023)] criticized recent proposals connecting weak values and the past of a quantum particle. I argue that their conclusion follows from a conceptual error in understanding the approach to the past of the particle they discuss.

We demonstrate that an interferometer based on modulated magnetic field pulses enables precise characterization of the energies and lifetimes of Efimov trimers irrespective of the magnitude and sign of the interactions in ${}^{85}\mathrm{Rb}$ thermal gases. Despite thermal effects, interference fringes develop when the dark time between the pulses is varied. This enables the selective excitation of coherent superpositions of trimer, dimer, and free-atom states. The interference patterns possess two distinct damping timescales at short and long dark times that are either equal to or twice as long as the lifetime of Efimov trimers, respectively. Specifically, this behavior at long dark times provides an interpretation of the unusually large damping timescales reported in a recent experiment with ${}^7\mathrm{Li}$ thermal gases [Yudkin et al., Phys. Rev. Lett. 122, 200402 (2019)]. Apart from that, our results constitute a stepping stone towards a high precision few-body state interferometry for dense quantum gases.

Measurements of physical properties, including transport and magnetic properties and thermal conductivity, have been performed on single crystal MnRuP with helical magnetism structure. A field-induced first-order metamagnetism transition with a magnetization jump is discovered when the magnetic field is applied perpendicular to the $c$ axis. The arising transition is a result of the competition between magnetic energy and elastic energy with a complex noncollinear character in the vicinity of the metastable state in MnRuP, which is due to the transition from the incommensurate helical phase to the commensurate Yafet-Kittel ferrimagnetic phase or from the helical phase to the conical phase. It is worthwhile to note that both negative and positive magnetoresistance (MR) are observed when the field is parallel or perpendicular to the electric current, respectively, which is rarely found in the other system simultaneously. Such positive MR is unexpected on the basis that the application of magnetic field should favor a low-resistive state due to alignment of spins. The ability to manipulate magnetic properties with current directions may pave the way to a new method of realizing magnetic memories based on the spin internal degrees of freedom.

This paper examines the relationship between sparse random network architectures and neural network stability by examining the eigenvalue spectral distribution. Specifically, we generalize classical eigenspectral results to sparse (not fully connected) connectivity matrices obeying Dale's law: neurons function as either excitatory (E) or inhibitory (I). By defining $\alpha$ as the probability that a neuron is connected to another neuron, we give explicit formulas that show how sparsity interacts with the E-I population statistics to scale key features of the eigenspectrum in both the balanced and unbalanced cases. Our results show that the eigenspectral outlier is linearly scaled by $\alpha$, but the eigenspectral radius and density now depend on a nonlinear interaction between $\alpha$ and the E-I population means and variances. Contrary to previous results, we demonstrate that a nonuniform eigenspectral density results if any of the E-I population statistics differ, not just the variances. We also find that local eigenvalue outliers are present for sparse random matrices obeying Dale's law, and demonstrate that these eigenvalues can be controlled by a modified zero row-sum constraint for the balanced case, however, they persist in the unbalanced case. We examine all levels of connection sparsity $0\le \alpha \le 1$ and distributed E-I population weights to describe a general class of sparse connectivity structures which unifies all the previous results as special cases of our framework. Sparsity and Dale's law are both fundamental anatomical properties of biological neural networks. We generalize their combined effects on the eigenspectrum of random neural networks, thereby gaining insight into network stability, state transitions, and the structure-function relationship.

High harmonic generation (HHG) in solids reflects the underlying nonperturbative nonlinear dynamics of electrons in a strong light field and is a powerful tool for ultrafast spectroscopy of electronic structures. Photocarrier doping allows us to understand the carrier dynamics and the correlations between the carriers in the HHG process. Here, we study the effect of incoherent electron-hole pairs on HHG in an atomically thin semiconductor. The experimentally observed response to photocarrier doping is successfully reproduced in numerical simulations incorporating the photoexcited carrier distribution, excitonic Coulomb interaction, and electron-electron scattering effects. The simulation results reveal that the presence of photocarriers enhances the intraband current that contributes to high harmonics below the absorption edge. We also clarify that the excitation-induced dephasing process rather than the phase-space filling effect is the dominant mechanism reducing the higher-order harmonics above the absorption edge. Our work provides a deeper understanding of high harmonic spectroscopy and the optimum conditions for generating extreme ultraviolet light from solids.

How predictable is the next move of an animal? Specifically, which factors govern the short- and long-term motion patterns and the overall dynamics of land-bound, plant-eating animals in general and ruminants in particular? To answer this question, we here study the movement dynamics of springbok antelopes Antidorcas marsupialis. We propose several complementary statistical-analysis techniques combined with machine-learning approaches to analyze—across multiple time scales—the springbok motion recorded in long-term GPS tracking of collared springboks at a private wildlife reserve in Namibia. As a result, we are able to predict the springbok movement within the next hour with a certainty of about 20%. The remaining about 80% are stochastic in nature and are induced by unaccounted factors in the modeling algorithm and by individual behavioral features of springboks. We find that directedness of motion contributes approximately 17% to this predicted fraction. We find that the measure for directedeness is strongly dependent on the daily cycle of springbok activity. The previously known daily affinity of springboks to their water points, as predicted from our machine-learning algorithm, overall accounts for only about 3% of this predicted deterministic component of springbok motion. Moreover, the resting points are found to affect the motion of springboks at least as much as the formally studied effects of water points. The generality of these statements for the motion patterns and their underlying behavioral reasons for other ruminants can be examined on the basis of our statistical-analysis tools in the future.

Lattice gauge theories coupled to fermionic matter account for many interesting phenomena in both high-energy physics and condensed-matter physics. Certain regimes, e.g., at finite fermion density, are difficult to simulate with traditional Monte Carlo algorithms due to the so-called sign problem. We present a variational, sign-problem-free Monte Carlo method for lattice gauge theories with continuous gauge groups and apply it to $(2+1)$-dimensional compact QED with dynamical fermions at finite density. The variational ansatz is formulated in the full gauge-field basis, i.e., without having to resort to truncation schemes for the $\text{U}\left(1\right)$ gauge-field Hilbert space. The ansatz consists of two parts: first, a pure gauge part based on Jastrow-type ansatz states (which can be connected to certain neural-network ansatz states) and, second, a fermionic part based on gauge-field-dependent fermionic Gaussian states. These are designed in such a way that the gauge-field integral over all fermionic Gaussian states is gauge-invariant and at the same time still efficiently tractable. To ensure the validity of the method we benchmark the pure gauge part of the ansatz against another variational method and the full ansatz against an existing Monte Carlo simulation where the sign problem is absent. Moreover, in limiting cases where the exact ground state is known we show that our ansatz is able to capture this behavior. Finally, we study a sign-problem affected regime by probing density-induced phase transitions.

Current quantum technologies bring a new integration of quantum data with classical data for hybrid processing. However, the frameworks of these technologies are restricted to a single classical or quantum task, which limits their flexibility in near-term applications. We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs. This analog processor comprises a quantum network in which quantum data are incident to the network and classical data are encoded via a coherent field exciting the network. We perform a multitasking application of quantum tomography and nonlinear equalization of classical channels. Interestingly, the tomography can be performed in a closed-loop manner via the feedback control of classical data. Therefore, if the classical input comes from a dynamical system, embedding this system in a closed loop enables hybrid processing even if access to the external classical input is interrupted. Finally, we demonstrate preparing quantum depolarizing channels as a quantum machine learning technique for quantum data processing.

Compact inductor-capacitor (LC) resonators, in contrast to coplanar waveguide (CPW) resonators, have a simple lumped-element circuit representation but usually call for sophisticated finite-element method (FEM) simulations for an accurate modeling. Here we present a simple analytical model for a family of coplanar LC resonators where the electrical properties are directly obtained from the circuit geometry with a satisfying accuracy. Our experimental results on ten high-internal-quality-factor resonators $(Q_{\mathrm{i}}\gtrsim 2\times {10}^5)$, with frequencies ranging from $300\mathrm{MHz}$ to $1\mathrm{GHz}$, show an excellent consistency with both the derived analytical model and detailed FEM simulations. These results showcase the ability to design sub-gigahertz resonators with less than $2\%$ deviation in the resonance frequency, which has immediate applications, for example, in the implementation of ultrasensitive cryogenic detectors. The achieved compact resonator size of the order of a square millimeter indicates a feasible way to integrate hundreds of microwave resonators on a single chip for realizing photonic lattices.