Ground-state preparation is a central application for quantum computers but remains challenging in practice. In this work, we quantitatively investigate the performance and gate counts of double-bracket quantum algorithms (DBQAs) for ground-state preparation. We propose a practical strategy in which DBQAs refine initial state preparation circuits, and we compile them for Heisenberg chains using controlled-Z and single-qubit gates. Warm-started DBQAs consistently improve both the energy and ground-state fidelity relative to the initial states provided by variational ansätze, indicating that DBQAs offer an effective unitary synthesis method. To demonstrate compatibility with near-term hardware, we executed a proof-of-concept example on IBM devices. With error mitigation, we observed a statistically significant improvement over the corresponding warm-start circuit. Furthermore, numerical emulations for the same system size indicate that executing DBQAs on Quantinuum's hardware could achieve similar cost-function gains without requiring error mitigation. These findings suggest that DBQAs are a promising approach for enhancing ground-state approximations on near-term quantum devices.

It is usually believed that symmetry breaking in photonic systems leads to weaker confinement, such as in the case of metasurfaces when bound states in the continuum are replaced by quasibound states with lower quality $\left(Q\right)$ factors. Here, in contrast, we reveal that symmetry breaking can instead enhance light trapping by strengthening in-plane nonlocal coupling pathways. We consider finite-size arrays of optical resonators supporting Mie resonances (a Mie-tronics platform) and employ diffraction and multiple-scattering analyses to demonstrate that diffractive bands and Mie-tronics supermodes originate from the same underlying Mie resonances but differ fundamentally in their physical nature. Finite arrays exhibit $Q$-factor enhancement driven by redistributed radiation channels, thus reversing the trends predicted for infinite lattices. We demonstrate that controlled symmetry breaking opens otherwise symmetry-forbidden electromagnetic coupling channels, enabling polarization conversion in nonlocal metasurfaces. These findings establish a unified wave-physics platform linking scattering and diffraction theories. They also outline design principles for multifunctional metasurfaces that exploit nonlocality for advanced light manipulation, computation, and emission.

An analytic technique for the investigation of the combined impacts of multiple photon pairs and a non-negligible joint spectral density on the measurement of a Clauser-Horne-Shimony-Holt (CHSH) parameter is introduced. We demonstrate this technique for an asymmetric Gaussian-normal joint spectral density of spontaneous parametric down-converted photons using two different techniques for generating polarization entanglement. We find that the correlation of the joint spectral density can improve the CHSH parameter for certain ranges of the squeezing parameter when the polarization-entangled state is generated using a beam-splitter technique. Additionally, we find that the impacts due to the correlation of the joint spectral density are made worse when asymmetry is present in the joint spectral density.

Tripartite interactions are fundamental for realizing quantum phenomena beyond pairwise physics, yet their native implementation—particularly among distinct quantum systems—remains challenging. Here, we propose a hybrid quantum architecture comprising a magnonic mode [in a yttrium iron garnet (YIG) sphere], an Andreev spin qubit, and a superconducting qubit, to realize a native and strong interaction rather than relying on synthesis from two-body processes, leveraging the coupling between the spin-dependent supercurrent and the magnetic field of YIG sphere. Through analytical and numerical studies, we demonstrate that this interaction induces synchronized collapse and revival in qubit populations when the magnon is initially prepared in a coherent state. Notably, during the collapse region—where populations remain static—the entanglement structure undergoes a dramatic and continuous reorganization. We show that the genuine tripartite entanglement is redistributed into bipartite entanglement between the two qubits, and vice versa, with the total entanglement conserved. These phenomena, unattainable via bipartite couplings, underscore the potential of tripartite interactions for exploring intrinsically new quantum effects and advancing hybrid quantum information platforms.

Turbulence fragments slender fibers in oceans, process industries, and the atmosphere, shaping size spectra, transport, and impact. Mean-field estimates compare typical turbulent bending with a material failure threshold and predict a critical span below which fibers should be effectively unbreakable. We show that this conclusion changes when turbulent intermittency is included. In the quasistatic inertial-range regime, the fiber relaxes elastically faster than the turbulent load changes, so its curvature is set by the velocity increment across the fiber; refined similarity relates the amplitude of that increment to the coarse-grained dissipation at the fiber scale. Rare fluctuations of the velocity increment across the fiber therefore generate rare curvature bursts, opening a subcritical breakage channel for fibers whose mean-field curvature lies far below threshold. In a log-normal cascade model, the resulting hazard decays algebraically with span, rather than with the much stronger Gaussian suppression obtained from a nonintermittent model. The same rare-event statistics can be mapped onto an effective mean dissipation: the mean dissipation that an equivalent nonintermittent model would need to reproduce the intermittent exceedance probability. Under realistic conditions, this effective dissipation can exceed the bare mean by orders of magnitude. Intermittency can therefore control fiber fragmentation in nominally subcritical regimes.

There are several inequivalent proposals in the literature for how to compute the probability distribution of the time that a detector registers for the arrival of a quantum particle. For three of these proposals, based on two kinds of absorbing boundary conditions and imaginary potentials, we compute the predicted distribution for an experimental setup involving a single nonrelativistic quantum particle with spin 0 or 1/2 in a waveguide along the $z$ axis with the detector waiting downstream. We find that the distribution shows signs of partial reflection of the wave function off of the detector; for a spin-1/2 wave function, it is independent of the initial spin orientation for the parameters tested but does depend, for boundary conditions coupling to the spin, on the width of the waveguide. We also compare our predictions with the competing ones of Das and Dürr [Sci. Rep. 9, 2242 (2019)].

Rotating colloids, or spinners, in obstacle arrays exhibit frequency-set stationary orbits and currents set by the competition between an inertial, Magnus-like lift and short-range attraction. Fully resolved lattice-Boltzmann simulations reveal the hydrodynamic coupling and identify the lift mechanism, while a symmetry-based Langevin model captures the resulting balance. In periodic lattices, the superposition of scalar and vector potentials produces two robust orbital regimes: corner states, in which spinners orbit individual posts, and inner states, in which orbits couple across four neighboring obstacles. Slow frequency modulation toggles these states and produce directed, stepwise transport across the grid. This establishes a minimal hydrodynamic mechanism, controlled by a single driving parameter, for programmable guidance of active rotors in structured environments.

We design and implement a quantum combinatorial reasoning framework for large language models (QCR-LLM), integrating a real quantum computer in the hybrid workflow. QCR-LLM reformulates reasoning aggregation as a higher-order unconstrained binary optimization problem. In this sense, reasoning fragments are represented as binary variables and their interactions encode statistical relevance, logical coherence, and semantic redundancy. We tackle the resulting high-order optimization problem both classically, via simulated annealing, and quantumly, through the bias-field digitized counterdiabatic quantum optimizer executed on IBM's superconducting digital quantum processors. Experiments on BIG-Bench Extra Hard benchmarks demonstrate that our QCR-LLM consistently improves reasoning accuracy across multiple LLM backbones, surpassing reasoning-native systems such as o3-high and DeepSeek R1 by up to $+9$ pp. At a matched sampling budget, QCR-LLM also outperforms standard chain-of-thought aggregation baselines (majority vote and self-consistency), as well as a pairwise Quadratic Unconstrained Binary Optimization restriction of our formulation, indicating that the improvement arises from the higher-order energy landscape rather than from multisampling alone. Despite requiring multiple reasoning samples per query, our QCR-LLM reduces the number of generated tokens and the associated LLM-side energy relative to long hidden chain-of-thought models such as o3-high; we emphasize, however, that once the energy of the quantum backend is accounted for, we are not yet in an energy-saving or sustainability regime. These results constitute the first experimental evidence of quantum-assisted reasoning, showing that hybrid quantum-classical optimization can efficiently enhance reasoning coherence and interpretability in large-scale language models. We have opened the doors to the emergence of quantum intelligence, where the higher-order structure of harder prompts maps naturally onto quantum optimizers, opening a paradigm toward genuine quantum reasoning; while we observe a small accuracy gain of the quantum solver over simulated annealing, this result is not conclusive and may stem from statistical fluctuations.

In recent decades, there has been an increasing demand for faster modulation schemes. Electro-optic modulators are essential components in modern photonic systems, enabling high-speed control of light for applications ranging from telecommunications to quantum communication. Conventional inline and Mach-Zehnder modulators, while widely adopted, are limited by bias drift, high operating voltages, and polarization-mode dispersion. Sagnac loop-based modulators have recently emerged as a promising alternative, offering inherent stability against environmental fluctuations and eliminating the need for active bias control. In this work, we present a comprehensive model of the Sagnac modulator that captures both intensity and polarization modulation. We analyze the role of asymmetry in the loop, highlighting its impact on the achievable repetition rate, and propose modulation strategies to overcome these constraints. Finally, we investigate the symmetric Sagnac configuration and demonstrate practical techniques for achieving robust modulation while mitigating experimental challenges, including optical pulse carving and polarization modulation at repetition rates up to 1.5 GHz, the maximum achievable for our hardware implementation without increasing the modulation voltage. Our results establish the Sagnac modulator as a versatile and stable platform for next-generation photonic and quantum communication systems.

The coevolution of structure and dynamics, known as adaptation, is a fundamental property in various systems and drives diverse emergent behaviors. However, the adaptation in most works primarily stems from pairwise situations, which is insufficient to capture ubiquitous higher-order characteristics of real systems. Here, we introduce higher-order adaptation, characterized by baseline rewiring rate and nonlinear higher-order exponent, to model the coevolution of higher-order structure and spreading dynamics. We develop a hyperedge-based theoretical framework that well predicts critical behaviors and outbreak sizes. Notably, the inherent nonlinearity of higher-order adaptation inhibits bistability and explosive transitions, which is qualitatively opposite to the classical findings associated with pairwise adaptation or higher-order contagion. This inhibitory effect is enhanced by rewiring accuracy and remains robust across various adaptive spreading models. Our work establishes higher-order adaptation as a fundamentally distinct mechanism from pairwise adaptation, shedding light on adaptive systems.

In an accompanying paper [B. Tissot et al., Phys. Rev. A 114, L010602 (2026)], we introduced an approach to interface trapped-ion quantum processors with ensemble-based quantum memories by matching a spontaneous parametric down-conversion source to both the ions and the memories. This enables rapid entanglement generation between single trapped ions separated by distances of hundreds of kilometers. In this article, we extend the protocol and provide additional details of the analysis. Particularly, we compare double-click and single-click approaches for the ion edge nodes. The double-click approach relaxes the phase stability requirement but is strongly affected by finite efficiencies. Choosing the optimal protocol thus depends on the access to the phase stabilization as well as the efficiencies of the interfaces of the ions and ensemble-based memories.

Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we benchmark a quantum-classical hybrid solver tailored for particle-hole symmetric AIMs, using the variational quantum eigensolver to prepare the ground state of the model with shallow quantum circuits. The solver uses shallow quantum ansätze and one set of variational parameters to prepare the ground state and its particle and hole excitations, enabling the construction of the impurity Green’s function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment correction to the variational energies, and benchmark the approach by comparing the density of states computed from the impurity Green’s function against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green’s function construction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.

The properties of a remnant black hole can be probed by analyzing the gravitational waves emitted during its ringdown phase. This signal provides a direct test of general relativity in the strong-field regime. In this study, we apply both a time-domain $\mathcal{F}\text{-statistic}$ framework and full Bayesian time-domain sampling to the ringdown of GW231028_153006. We report decisive evidence for multimode content, specifically identifying both the first overtone ($\ell \left|m\right|n=221$) and the higher order ($\ell \left|m\right|n=210$). The detection of the 221 mode is statistically significant, achieving a Bayes factor of $\sim 189.2$ and an amplitude exclusion from zero at $>7\sigma$ credibility for an analysis beginning at $10M$ postpeak. These findings are rigorously validated against the numerical relativity injection SXS:BBH:1282, which confirms that such multimode features are physically expected for a remnant with the inferred high spin and mass ratio. The inclusion of the overtone mode allows for precise constraints on the remnant's properties, yielding a redshifted final mass of $246.2_{-22.4}^{+22.3}{M}_{\odot }$ and a final spin of $0.{81}_{-0.10}^{+0.07}$ (at $90\%$ credibility), consistent with full inspiral-merger-ringdown predictions. A test of the no-hair theorem, enabled by this robust multimode detection, shows consistency with general relativity.

Efficiently certifying non-Gaussian entanglement in continuous-variable quantum systems is a central challenge for advancing quantum information processing, photonic quantum computing, and metrology. Here, we put forward continuous-variable counterparts to the recently introduced entanglement criteria based on moments of the partially transposed state, together with simple readout schemes that require only a few replicas of the state, passive linear optics, and particle-number measurements. We develop a multicopy method that enables the detection of strongly non-Gaussian entanglement for various relevant state families overlooked by standard approaches, which include the entire class of NOON states. Further, it is robust against realistic experimental constraints (losses, noise, and finite statistics), which we demonstrate by extensive numerical simulations.

The fast and efficient preparation of quantum critical states is a challenging yet crucial task for various quantum technologies. This difficulty is most particularly for systems near a quantum phase transition, where the closure of the energy gap fundamentally limits the timescale of adiabatic processes and thus precludes rapid state preparation. We propose a framework using deep reinforcement learning (DRL) to rapidly prepare quantum critical states, with broad extendibility to light-matter interaction systems. Specifically, a DRL agent optimizes a set of time-dependent control Hamiltonians to drive the system from an initial noncritical state to a target critical state within a finite time and over experimentally accessible parameter ranges. As a concrete application, we focus on the quantum Rabi model. The DRL-optimized time-dependent control Hamiltonian yields a final state with high fidelity ($>0.999$) to the target critical state. The protocol can be readily extended to other quantum critical systems described by light-matter interaction models, such as quantum Dicke model. This investigation provides a powerful framework for preparing and manipulating quantum critical states.

Quantum many-body dynamics generate nonclassical correlations naturally described by quantum resource theories. Quantum magic resources (or nonstabilizerness) capture the deviation from classically simulable stabilizer states, while coherence and fermionic non-Gaussianity measure departure from the computational basis and from fermionic Gaussian states, respectively. We track these resources in a subsystem of a one-dimensional qubit chain evolved by random brickwall circuits. For resource-generating gates, evolution from low-resource states exhibits a universal rise-peak-fall behavior, with the peak time scaling logarithmically with subsystem size and the resource eventually decaying as the subsystem approaches a maximally mixed state. Circuits whose gates do not create the resource but entangle neighboring qubits give rise to a ballistic spreading of quantum resources initially confined to a region of the chain. Our results provide a unified picture of spatiotemporal resource dynamics in local circuits and a baseline for more structured quantum many-body systems.

We demonstrate an end-to-end workflow to model chemical reaction barriers with the quantum-classical auxiliary field quantum Monte Carlo (QC-AFQMC) algorithm with quantum tomography using matchgate shadows. The workflow operates within an accelerated quantum supercomputing environment with the IonQ Forte quantum computer and NVIDIA graphic processing units (GPUs) on Amazon Web Services. We present several algorithmic innovations and an efficient GPU-accelerated execution that achieves a several orders of magnitude speedup over the state-of-the-art implementation of QC-AFQMC. We apply the algorithm to simulate the oxidative addition step of the nickel-catalyzed Suzuki-Miyaura reaction using 24 qubits of IonQ Forte with 16 qubits used to represent the trial state, plus 8 additional ancilla qubits for error mitigation, resulting in the largest QC-AFQMC with matchgate shadow experiments ever performed on quantum hardware. We achieve a $9\times$ speedup in collecting matchgate circuit measurements, and our distributed-parallel postprocessing implementation attains a $656\times$ time-to-solution improvement over the prior state of the art. Chemical reaction barriers for the model reaction evaluated with active-space QC-AFQMC are within the uncertainty interval of $\pm 4$ kcal/mol from the reference CCSD(T) (coupled cluster singles and doubles with perturbative triples) result when matchgates are sampled on the ideal simulator and within 10 kcal/mol from reference when measured on quantum processing unit. This work marks a step toward practical quantum chemistry simulations on quantum devices while identifying several opportunities for further development.

Quantum key distribution (QKD) enables secure key generation between distant users, yet its standard device-dependent implementations rely on fully characterized and accurately modeled devices. Device-independent (DI) QKD removes this assumption by certifying privacy solely from Bell inequality violations, but its stringent experimental demands severely hinder scalability. In realistic quantum networks, asymmetric levels of device trust can naturally arise from diverse implementation conditions across nodes. Motivated by this, we investigate partially DI scenarios in which only a subset of parties operate trusted devices. Built upon the Brown-Fawzi-Fawzi method [Quantum 8, 1445 (2024)], we employ a semidefinite programming hierarchy tailored to estimate the conditional von Neumann entropies in such settings. This approach provides efficiently computable lower bounds on asymptotic rates of cryptographic protocols with broad applicability. By reexamining representative bipartite and tripartite communication tasks under the partially DI framework, we demonstrate that enforcing trust on the devices of a subset of parties never degrades key or randomness expansion rates and, in certain cases, can substantially increase them owing to the network asymmetry. Our results highlight the impact of device trustworthiness on cryptographic security and facilitate scalable implementations in practical asymmetric network architectures.