The presence of spin-orbit coupling or noncollinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop noncollinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our noncollinear Koopmans-compliant theory involves functionals of four-component orbital densities that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic nonmagnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide ${\mathrm{WSe}}_2$, the cubic perovskite ${\mathrm{CsPbBr}}_3$, and the ferromagnetic semiconductor ${\mathrm{CrI}}_3$. The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory and include spin-dependent interactions and screening effects that are missing in standard diagrammatic approaches based on the random phase approximation. While the inclusion of orbital- and spin-dependent interactions in many-body perturbation theory requires self-screening or vertex corrections, they emerge naturally in the Koopmans-functionals framework.

We introduce an adiabatic state preparation protocol which implements quantum imaginary time evolution under the Hamiltonian of the system. Unlike the original quantum imaginary time evolution algorithm, adiabatic quantum imaginary time evolution does not require quantum state tomography during its runtime and, unlike standard adiabatic state preparation, the final Hamiltonian is not the system Hamiltonian. Instead, the algorithm obtains the adiabatic Hamiltonian by integrating a classical differential equation that ensures that one follows the imaginary time evolution state trajectory. We introduce some heuristics that allow this protocol to be implemented on quantum architectures with limited resources. We explore the performance of this algorithm via classical simulations in a one-dimensional spin model and highlight essential features that determine its cost, performance, and implementability for longer times, and compare to the original quantum imaginary time evolution for ground-state preparation. More generally, our algorithm expands the range of states accessible to adiabatic state preparation methods beyond those that are expressed as ground states of simple explicit Hamiltonians.

The influence of a Gaussian environment on a quantum system can be described by effectively replacing the continuum with a discrete set of ancillary quantum and classical degrees of freedom. This defines a pseudomode model which can be used to classically simulate the reduced system dynamics. Here we consider an alternative point of view and analyze the potential benefits of an analog or digital quantum simulation of the pseudomode model itself. Superficially, such a direct experimental implementation is, in general, impossible due to the unphysical properties of the effective degrees of freedom involved. However, we show that the effects of the unphysical pseudomode model can still be reproduced using measurement results over an ensemble of physical systems involving ancillary harmonic modes and an optional stochastic driving field. This is done by introducing an extrapolation technique whose efficiency is limited by stability against imprecision in the measurement data. We examine how such a simulation would allow us to (i) perform a quantum simulation of the effects of complex nonperturbative and non-Markovian environments in regimes that are challenging for classical simulation; (ii) conversely, mitigate potential unwanted non-Markovian noise present in quantum devices; and (iii) restructure some of the properties of a given physical bath, such as its temperature.

The impact of a 3d divalent substitution for Cu using $M^{2+}$ ions not presenting elongated ${M\mathrm{O}}_5$ pyramids has been explored in ${\mathrm{YBaCuFeO}}_5$ as a new strategy to upgrade the magnetic properties of this high-temperature spiral magnet and potential multiferroic. In this work we investigate the incidence of the $\mathrm{C}{\mathrm{u}}^{2+}$ with $\mathrm{C}{\mathrm{o}}^{2+}$ substitution in $\mathrm{YBa}\left({\mathrm{Cu}}_{1-x}{\mathrm{Co}}_x\right){\mathrm{FeO}}_5$ compounds in which B-site disorder is kept invariant (disorder $n_d\approx 36\%$). The study validates the reduction of the uniaxial elongation distortion at Cu sites as an efficient alternative way to disorder that enables one to enhance the thermal stability of the spiral order. In addition, the rotation plane of the helix is reoriented by the Cu/Co substitution and nearby the triple point (when $T_S$ is maximal) the spiral order adopts an almost complete cycloidal magnetic configuration. These results lay the foundation of a new mechanism to tune the stability of the magnetic spirals in layered perovskites based on reducing the uniaxial elongation associated to the (4+1)-coordination of $\mathrm{C}{\mathrm{u}}^{2+}$ ions in square ${\mathrm{CuO}}_5$ pyramids.

Solving inverse problems to identify Hamiltonians with desired properties holds promise for the discovery of fundamental principles. In quantum systems, quantum entanglement plays a pivotal role in not only characterizing the quantum nature but also developing quantum technology such as quantum computing. Nonetheless, the design principles of the quantum entanglement are yet to be clarified. Here we apply an inverse design framework using automatic differentiation to quantum spin systems, aiming to construct Hamiltonians with large quantum entanglement. We show that the method automatically finds the Kitaev model with bond-dependent anisotropic interactions, whose ground state is a quantum spin liquid, on both honeycomb and square-octagon lattices. On triangular and maple-leaf lattices with geometrical frustration, it generates numerous solutions with spatially inhomogeneous interactions rather than converging to a specific model, but it still helps to construct unprecedented models. The comparative study reveals that bond-dependent anisotropic interactions, rather than isotropic Heisenberg interactions, amplify quantum entanglement, even in the systems with geometrical frustration. The present study paves the way for the automatic design of new quantum systems with desired quantum nature and functionality.

For decades, most research of high harmonic generation (HHG) considered matter as quantum but light as classical. Recently, HHG driven by quantum states of light such as bright squeezed vacuum was predicted to reach beyond the classical HHG cutoff. Moreover, in squeezed coherent illumination, it was shown that the underlying dynamics are significantly modified by the photon statistics effective force. Here we show that HHG driven by quantum light results in quantum high harmonics. We derive a formula for the quantum state of the high harmonics, when driven by arbitrary quantum light states, and then explore specific cases of experimental relevance. Specifically, for a moderately squeezed pump, HHG driven by squeezed coherent light results in squeezed high harmonics. Harmonic squeezing is optimized by syncing ionization times with the pump's squeezing phase. Beyond this regime, as pump squeezing is increased, the harmonics initially acquire squeezed thermal photon statistics, and then occupy an intricate quantum state which strongly depends on the semiclassical nonlinear response function of the interacting system. Our results pave the way for generation of squeezed extreme-ultraviolet ultrashort pulses, and more generally, quantum frequency conversion into previously inaccessible spectral ranges, which may enable ultrasensitive attosecond metrology.

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle.

Multipolar spin systems provide a rich ground for the emergence of unexpected states of matter due to their enlarged spin degree of freedom. In this study, with a specific emphasis on $S=1$ magnets, we explore the interplay between spin nematic states and spin liquids. Based on the foundations laid in the prior work [R. Pohle et al., Phys. Rev. B 107, L140403 (2023)], we investigate the $S=1$ Kitaev model with bilinear-biquadratic interactions, which stabilizes, next to Kitaev spin liquid, spin nematic and triple-$q$ phases, also an exotic chiral spin liquid. Through a systematic reduction of the spin degree of freedom—from ${\mathbb{CP}}^2$ to ${\mathbb{CP}}^1$ and ultimately to a discrete eight-color model—we provide an intuitive understanding of the nature and origin of this chiral spin liquid. We find that the chiral spin liquid is characterized by an extensive ground-state degeneracy, bound by a residual entropy, extremely short-ranged correlations, a nonzero scalar spin chirality marked by ${\mathbb{Z}}_2$ flux order, and a gapped continuum of excitations. Our work contributes not only to the specific exploration of $S=1$ Kitaev magnets but also to the broader understanding of the importance of multipolar spin degree of freedom on the ground state and excitation properties in quantum magnets.

A theory describing the dynamics of quantum systems interacting on a classical spacetime was recently put forward by Oppenheim et al. Quantum states may retain their coherence, at the cost of some amount of stochasticity of the spacetime metric, characterized by a spacetime diffusion parameter. Here, we report existing experimental upper bounds on such spacetime diffusion, based on a review of several types of experiments with very low force noise over a broad range of test masses from single atoms to several kilograms. We find an upper bound at least 15 orders of magnitude lower as compared to the initial bounds for explicit models presented by Oppenheim et al. The results presented here provide a path forward for future experiments that can help evaluate classical-quantum theories.

We investigate the nonequilibrium steady state of the anisotropic open quantum Rabi model, which exhibits first-order and second-order dissipative phase transitions upon varying the degree of anisotropy between the coupling strengths of rotating and counter-rotating terms. Using both semiclassical and quantum approaches, we find a rich phase diagram resulting from the interplay between the anisotropy and the dissipation. First, there exists a bistable phase where both the normal and superradiant phases are stable. Second, there are multicritical points where the phase boundaries for the first- and second-order phase transitions meet. We show that a new set of critical exponents governs the scaling of the multicritical points. Finally, we discuss the feasibility of observing the multicritical transitions and bistability using a pair of trapped ions where the anisotropy can be tuned by controlling the intensity of the Raman transitions. Our study enlarges the scope of critical phenomena that may occur in finite-component quantum systems, which could be useful for applications in critical quantum sensing.

The Mach-Zehnder interferometer is a powerful device for detecting small phase shifts between two light beams. Simple input states, such as coherent states or single photons, can reach the standard quantum limit of phase estimation, while more complex states can be used to reach Heisenberg scaling; the latter, however, require challenging preparation and measurement strategies. The quest for highly sensitive phase estimation therefore calls for interferometers with nonlinear devices which would make the preparation of these complex states more efficient. Here, we show that the Heisenberg scaling can be recovered with simple input states (including Fock and coherent states) when the linear mirrors in the interferometer are replaced with controlled-swap gates and measurements on auxiliary qubits. These swap tests project the input Fock and coherent states onto NOON and entangled coherent states, respectively, and allow optimal or near-optimal measurements, leading to improved sensitivity to small phase shifts in one of the interferometer arms. We analyze auxiliary qubit errors in detail, showing that biasing the qubit towards phase flips offers a great advantage, and perform thorough numerical simulations of a possible implementation in circuit quantum electrodynamics with an auxiliary Kerr-cat qubit. Our results thus present a viable approach to phase estimation approaching Heisenberg-limited sensitivity and demonstrate potential advantages of using biased-noise qubits in quantum metrology.

Bacterial persistence is a phenomenon where a subpopulation of cells can survive antibiotic treatment, and it is often linked to extremely slow growth or a dormant state. However, the mechanisms and factors that govern dormancy are not well understood. We propose a simplified growth model that treats the main cellular components as discrete variables and allocates resources among them according to different strategies. The model can reproduce some of the observed features of bacterial persistence, such as wide distribution in division times, long division times after a nutrient downshift, and the existence of different dormant phenotypes. We also show how the growth structure, i.e., whether cells grow in a lineage or a branch, affects the dormant cells' occurrence and distribution due to the growth states' mother-daughter correlation. Our model provides a framework to explore the complex interactions between cellular processes and environmental conditions that lead to bacterial persistence.

Synchronization of action potentials is one of the important phenomena for neural networks to achieve biological functions. How to find the optimal parameter space of neural networks in a synchronized state is of great significance for studying their synchronization. This study explores the parameter space of triplet motif-based higher-order networks in a synchronized state through the dynamic learning of synchronization (DLS) technique, which dynamically modulates the connection weights between motifs to alter their firing patterns. Our study delves into regular, Erdós-Rënyi random graphs, small-world, and scale-free networks, emphasizing the high-order motif interactions that characterize these networks. Our key findings indicate that the DLS technique successfully promotes synchronization within high-order motif networks with various connection patterns, although the degree of synchronization in networks where motifs are interconnected by chemical synapses is slightly weaker than those interconnected by electrical synapses. Additionally, we demonstrate the pattern of weight changes during the regulation of network firing states by DLS, finding that the evolution of weight distributions correlates with the network's topological structure. This work might provide new insights into complex network synchronization and lays the foundation for further exploration of using DLS technology to synchronize higher-order networks through external factors.

Schrödinger bridges have emerged as an enabling framework for unveiling the stochastic dynamics of systems based on marginal observations at different points in time. The terminology “bridge” refers to a probability law that suitably interpolates such marginals. The theory plays a pivotal role in a variety of contemporary developments in machine learning, stochastic control, thermodynamics, and biology, to name a few, impacting disciplines such as single-cell genomics, meteorology, and robotics. In this work, we extend Schrödinger's paradigm of bridges to account for integral constraints along paths, in a way akin to maximum caliber—a maximum entropy principle applied in a dynamic context. The maximum caliber principle has proven useful to infer the dynamics of complex systems, e.g., model gene circuits and protein folding. We unify these two problems via a maximum likelihood formulation to reconcile stochastic dynamics with ensemble-path data. A variety of data types can be encompassed, ranging from distribution moments to average currents along paths. The framework enables inference of time-varying potential landscapes that drive the process. The resulting forces can be interpreted as the optimal control that drives the system in a way that abides by specified integral constraints. This, in turn, relates to a similarly constrained optimal mass transport problem in the zero-noise limit. Analogous results are presented in a discrete-time, discrete-space setting and specialized to steady-state dynamics. We finish by illustrating the practical applicability of the framework through paradigmatic examples, such as that of bit erasure or protein folding. In doing so, we highlight the strengths of the proposed framework, namely, the generality of the theory, its elegant analytical structure, the ease of computation, and the ability to interpret results in terms of system dynamics. This is in contrast to maximum caliber problems where the focus is typically on updating a probability law on paths.

Quantum computing technology has the potential to revolutionize the simulation of materials and molecules in the near future. A primary challenge in achieving near-term quantum advantage is effectively mitigating the noise effects inherent in current quantum processing units (QPUs). This challenge is also decisive in the context of quantum-classical hybrid schemes employing variational quantum eigensolvers (VQEs) that have attracted significant interest in recent years. In this paper, we present a method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations, focusing on VQEs. Our approach, grounded in probabilistic machine learning, exploits a custom prior based on the VQE ansatz to capture the underlying correlations between VQE outputs for different variational parameters, thereby enhancing both accuracy and efficiency. We demonstrate the effectiveness of our method on a two-site Anderson impurity model and a eight-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit, showcasing substantial improvements in the accuracy of VQE outputs while reducing the number of direct QPU energy evaluations. This paper contributes to the ongoing efforts in quantum-error mitigation and optimization, bringing us a step closer to realizing the potential of quantum computing in quantum matter simulations.