Raman scattering of photons into phonons gives rise to entangled photon pairs when the phonon emitted in a Stokes process is coherently absorbed in antiStokes scattering, forming the photonic analog of Cooper pairs. We present a nonperturbative theory for the time evolution of photonic Cooper pairs that treats interacting photons and phonons as a hybrid excitation, the Ramaniton. As the Ramaniton propagates in a waveguide it displays quantum oscillations between photon and phonon occupation, leading to resonant squeezed Stokes-antiStokes light when the phonon occupation becomes equal to zero without recurring back to the photon vacuum. This phenomenon is predicted to generate up to 28 dB of squeezed light even in standard silicon on insulator waveguides.

The effect of an imposed magnetic field on the flow of an electrically conducting fluid in a channel geometry is investigated numerically using high-performance temporal simulations. For the strongest spanwise magnetic fields considered, the turbulent state appears metastable, which makes the determination of the associated tipping point arduous. As an alternative, edge states, i.e., unstable states located on the state space boundary between the laminar and the turbulent basin of attraction, are investigated in detail. Their continuation smoothly leads to the tipping point where they are expected to collide with the turbulent dynamics. As the magnetic field intensity is raised, edge states become, on average, more energetic and more unstable, while their fluctuations become more chaotic, less predictable, and less symmetric. Nevertheless, their continuation allows one to accurately determine the value of the tipping point beyond which the laminar state becomes the only attractor.

In this study, we present an exploration of spontaneous symmetry breaking and pattern formation in the driven-dissipative system of Rydberg exciton polaritons with long-range interactions. Our investigation unravels the pattern formations through modulational instability, characterized by scales in the micron range. We observe the dynamics of the polariton ensemble, studying the emergence of metastable patterns and their eventual collapse in the long-time limit. This phenomenon is attributed to the destructive interference between the polariton state and the external drive within the ensemble. Further, we delineate conditions conducive to the stable formation of patterns under incoherent pumping. These findings open up various avenues for delving into the burgeoning realm of driven-dissipative and long-range interacting gases through the unique characteristics of Rydberg excitons.

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anticrossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak-coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations, and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks.

The dynamics of plasmons in nanoribbons of (hydrogen intercalated) quasi-freestanding single-layer graphene is studied by terahertz spectroscopy both in the steady state and upon photoexcitation by an ultrashort near infrared laser pulse. The use of two-dimensional frequency-domain analysis of the optical pump–terahertz (THz) probe signals allows us to determine the evolution of carrier temperature and plasmon characteristics with $\sim 100$ fs time resolution. Namely, we find that the carrier temperature decreases from more than 5000 K to the lattice temperature within about 7 ps and that during this evolution the carrier mobility remains practically constant. The time-resolved THz conductivity spectra suggest that graphene nanoribbons contain defects which act as low potential barriers causing a weak localization of charges; the potential barriers are overcome upon photoexcitation. Furthermore, the edges of graphene nanoribbons are found to slightly enhance the scattering of carriers. The results are supported by complementary measurements using THz scanning near-field microscopy, which confirm a high uniformity of the THz conductivity across the sample and demonstrate high enough sensitivity to resolve even the impact of nanometric terrace steps on SiC substrate under the graphene monolayer.

The advent of near-term digital quantum computers could offer us an exciting opportunity to investigate quantum many-body phenomena beyond that of classical computing. To make the best use of the hardware available, it is paramount that we have methods that accurately simulate Hamiltonian dynamics for limited circuit depths. In this paper, we propose a method to classically optimize unitary brickwall circuits to approximate quantum time evolution operators. Our method is scalable in system size through the use of tensor networks. We demonstrate that, for various three-body Hamiltonians, our approach produces quantum circuits that can outperform trotterization in both their accuracy and the quantum circuit depth needed to implement the dynamics, with the exact details being dependent on the Hamiltonian. We also explain how to choose an optimal time step that minimizes the combined errors of the quantum device and the brickwall circuit approximation.

By applying the Shiba mapping on the two-particle level, we derive the relation between the local four-point correlation functions of bipartite lattice models with on-site electronic repulsion and those of the corresponding models with attractive interaction in the most general setting. In particular, we extend the results of [Phys. Rev. B 101, 155148 (2020)], which were limited to the rather specific situation of the static limit in strictly particle-hole symmetric models, (i) by explicitly including both magnetic field and different values of the chemical potentials, and (ii) by considering the full dependence of the generalized susceptibilities on the transfer (bosonic) Matsubara frequency. The derived formalism is then applied, as a relevant benchmark, to the Hubbard atom by investigating the general properties of the divergences of its irreducible vertex functions as a function of chemical potential and applied magnetic field. The resulting phase diagrams provide an insightful compass for future studies of the breakdown of the self-consistent perturbation expansion beyond high-symmetric regimes.

The utility of near-term quantum computers and simulators is likely to rely upon software-hardware codesign, with error-aware algorithms and protocols optimized for the platforms they are run on. Here, we show how knowledge of noise in a system can be exploited to improve the design of gate-based quantum simulation algorithms. We demonstrate this codesign in the context of a trapped ion quantum simulation of the dynamics of a Heisenberg spin model. Specifically, we derive a theoretical noise model describing unitary gate errors due to heating of the ions' collective motion, finding that the temporal correlations in the noise induce an optimal gate depth. We then illustrate how tailored feedforward control, best applied at this optimum, can be used to partially mitigate unitary gate errors and improve the simulation outcome. Our results provide a practical guide to the codesign of gate-based quantum simulation algorithms.

Correlated quantum many-body phenomena in lattice models have been identified as a set of physically interesting problems that cannot be solved classically. Analog quantum simulators, in photonics and microwave superconducting circuits, have emerged as near-term platforms to address these problems. An important ingredient in practical quantum simulation experiments is the tomography of the implemented Hamiltonians—while this can easily be performed if we have individual measurement access to each qubit in the simulator, this could be challenging to implement in many hardware platforms. In this paper, we present a scheme for tomography of quantum simulators which can be described by a Bose-Hubbard Hamiltonian while having measurement access to only some sites on the boundary of the lattice. We present an algorithm that uses the experimentally routine transmission and two-photon correlation functions, measured at the boundary, to extract the Hamiltonian parameters at the standard quantum limit. Furthermore, by building on quantum enhanced spectroscopy protocols that, we show that with the additional ability to switch on and off the on-site repulsion in the simulator, we can sense the Hamiltonian parameters beyond the standard quantum limit.

We numerically simulate a non-Abelian lattice gauge theory in two spatial dimensions, with tensor networks (TN), up to intermediate sizes ($>30$ matter sites) well beyond exact diagonalization. We focus on the SU(2) Yang-Mills model in Hamiltonian formulation, with dynamical matter and minimally truncated gauge field (hardcore gluon). Thanks to the TN sign-problem-free approach, we characterize the phase diagram of the model at zero and finite baryon number as a function of the quark bare mass and color charge. At intermediate system sizes, we detect a liquid phase of quark-pair bound-state quasiparticles (baryons), whose mass is finite towards the continuum limit. Interesting phenomena arise at the transition boundary where color-electric and color-magnetic terms are maximally frustrated: For low quark masses, we see traces of potential deconfinement, while for high masses, signatures of a possible topological order.

In this paper, we introduce a supervised learning technique that harnesses artificial neural networks, along with the outcomes of collective entanglement measurements, to estimate the negativity of quantum states in two-qubit and qubit-qutrit systems. The resulting deep-learned collective entanglement witnesses offer the unique capability of continuous sensitivity and selectivity tuning. This instrument enables us to explore the tradeoff between sensitivity and selectivity in entanglement detection, a dimension not accessible to previously employed analytical witnesses. In particular, we demonstrate that there are experimentally cost-effective methods (in terms of the number of measurements) where sensitivity can be significantly improved at a slight expense of the selectivity of entanglement detection. This chosen approach is also favored due to its high generality and potential for superior performance compared to other types of entanglement witnesses. Our findings may pave the way for the development of more efficient and accurate entanglement detection methods in complex quantum systems, especially when considering realistic experimental imperfections.

Reservoir computing leverages the dynamic properties of a fixed, randomly connected neural network to facilitate simplified training and enhanced computational efficiency. Many forms of physical reservoir computers have been proposed. In this paper, we use a three-dimensional (3D)-printed reservoir computer as the design environment, develop analytic models to describe its performance, and validate the models through simulations. This approach offers practical insights for designing physical reservoirs with targeted computational capabilities and enables the assessment of the influence of reservoir parameters such as scale or material choice, on performance metrics, including speed and power consumption. Additionally, the proposed approach may be employed to optimally design physical reservoir computers to solve specific problems. This work contributes to the understanding of physical RC systems by providing a detailed analysis of the physical basis that connects computational performance with multidomain physical interactions at the device level. The methods and results from this work not only propel the development of future 3D-printed physical RC systems but also serves as a framework for evaluating and designing diverse physical RC models based on other approaches.

A variety of living and nonliving systems exhibit collective motion. From swarm robotics to bacterial swarms, and tissue wound healing to human crowds, examples of collective motion are highly diverse but all of them share the common necessary ingredient of moving and interacting agents. While collective motion has been extensively studied in nonproliferating systems, how the proliferation of constituent agents affects their collective behavior is not well understood. Here, we focus on growing active agents as a model for cells and study how the interplay between noise in their direction of movement and proliferation determines the overall spatial pattern of collective motion. In this agent-based model, motile cells possess the ability to adhere to each other through cell-cell adhesion, grow in size, and divide. Cell-cell interactions influence not only the direction of cell movement but also cell growth through a force-dependent mechanical feedback process. We show that noise in the direction of a cell's motion has striking effects on both the emergent spatial distribution of cell collectives and proliferation. While higher noise strength leads to a random spatial distribution of cells, we also observe increased cell proliferation. On the other hand, low noise strength leads to a ringlike spatial distribution of cell collectives together with lower proliferation. Our findings provide insight into how noisy cell motion determines the local spatial organization of cells with consequent mechanical feedback on cell division impacting cell proliferation due to the formation of cell clusters.

The effect of laser pulse duration on energy coupling into a planar silicon target is investigated in experiments at the OMEGA-EP facility by varying the laser pulse length $\tau$—spanning 3 orders of magnitude from 100 ps to 10 ns—while maintaining a constant peak laser intensity, $I_0=5\times {10}^{14}$ W/${\mathrm{cm}}^2$. In theoretical models, the ablation pressure primarily scales for a given material with laser intensity and wavelength, which are all fixed variables here, allowing us to explore the specific role of laser pulse duration. Two-dimensional radiation-hydrodynamics simulations benchmarked with optical probing of the expanding plasma show that the pulse duration is critical for the ablation pressure to reach a steady state. Moreover, the pulse duration impacts shock decay and multiple wave effects, which strongly dictate the evolving shock profile that propagates within the laser-shocked target as ultimately measured by rear-surface diagnostics. The shock velocities inferred from the theoretical model, after considering shock decay, impedance matching, and shock Hugoniot, are found to be in good agreement with velocimetry measurements. However, discrepancies are observed with simulations for the shorter (0.1 ns) and longer (10 ns) pulse durations, which are respectively attributed to unaccounted contributions of kinetic absorption mechanisms and instabilities in simulations.

Exploring the full spectrum of novel behaviors that a system can produce can be an intensive task. Sampling techniques developed in response to this exploration challenge often require a predefined metric, such as distance in a space of known order parameters. However, order parameters are rarely known for nonequilibrium systems, especially in the absence of a diverse set of example behaviors, creating a chicken-and-egg problem. Here, we combine active and unsupervised learning for automated exploration of nonequilibrium systems with unknown order parameters. We iteratively use active learning based on current order parameters to expand the library of behaviors and relearn order parameters based on this expanded library. We demonstrate the utility of this approach in Kuramoto models of increasing complexity. In addition to reproducing known phases, we reveal previously unknown behavior and related order parameters, and we demonstrate how to align search with human intuition.

Estimating a quantum phase is a necessary task in a wide range of fields of quantum science. To accomplish this task, two well-known methods have been developed in distinct contexts, namely, Ramsey interferometry (RI) in atomic and molecular physics and quantum phase estimation (QPE) in quantum computing. We demonstrate that these canonical examples are instances of a larger class of phase estimation protocols, which we call reductive quantum phase estimation (RQPE) circuits. Here, we present an explicit algorithm that allows one to create an RQPE circuit. This circuit distinguishes an arbitrary set of phases with a smaller number of qubits and unitary applications, thereby solving a general class of quantum hypothesis testing to which RI and QPE belong. We further demonstrate a tradeoff between measurement precision and phase distinguishability, which allows one to tune the circuit to be optimal for a specific application.