Quantum software bugs often yield silent, incorrect outputs rather than explicit errors, making them particularly difficult to detect and repair with conventional techniques. Although large language models (LLMs) have shown strong performance on classical software engineering tasks, their ability to debug quantum code remains largely unexplored. To bridge this gap, we propose QBugLM, a multi-agent framework that automates the quantum software debugging pipeline, from taxonomy-driven bug injection to LLM-based detection and repair, and finally to simulation-based validation, for framework-agnostic OpenQASM 3.0 programs. We further conduct a comprehensive case study using QBugLM to benchmark two LLMs, Claude 4.6 Sonnet and Qwen3 Coder Next, across different prompting strategies, bug categories, and quantum programs. Our results show that iterative feedback is critical, as a single retry raises Pass@1 from below 25% to above 80%. Moreover, simpler structured prompting can even outperform Chain-of-Thought and ReAct for reasoning-capable models under fixed-resource constraints. Our work takes initial steps toward benchmarking LLM capabilities for debugging quantum programs and offers practical insights to support future efforts in automated quantum software repair.

In this paper we describe a communication-strategy study for multi-GPU three-dimensional finite-difference time-domain computation with convolutional perfectly matched layer boundary conditions using CUDA. The metrics used to determine the most effective implementation include runtime, throughput in millions of output points per second, strong-scaling efficiency, CPML overhead, host-staged versus direct GPU-to-GPU exchange speedup, and enlarged-ghost speedup. On a single NVIDIA Quadro RTX 6000 GPU, the CPML implementation sustains 2,889--3,290 million output points per second with less than 1\% boundary-layer overhead, providing the single-GPU baseline for the multi-GPU study. The results show that direct GPU-to-GPU peer exchange is the dominant optimization with a 2.46--2.76$\times$ speedup over host-staged exchange, while enlarged ghost regions give only modest benefits because the reduced communication frequency is partly offset by redundant computation and additional memory traffic. On NVIDIA Quadro RTX 8000 GPUs, the implementation gives up to a 1.51$\times$ speedup on two GPUs for the tested strong-scaling cases, while four GPUs enable larger grids that approach or exceed single-GPU memory capacity.

The prediction of masses of atomic nuclei using machine learning can complement theoretical models and advance the exploration of poorly known domains of the nuclear chart. We propose a machine learning technique based on gated recurrent units (GRU), which have demonstrated competitive performance in nuclear-mass prediction by exploiting long-term dependencies. By integrating multiplicative interactions and product-unit transformations within recurrent units, we report significant improvements in nuclear-mass prediction. Computations are performed in the complex domain to jointly capture amplitude and phase dynamics. For interpolation and temporal-extrapolation tasks based on the atomic mass evaluation (AME2016 and AME2020), the complex additive-multiplicative product-unit gated recurrent unit (AM-PU-GRU) model consistently achieves the lowest prediction errors, with an interpolation RMSE of 0.227 $\pm$ 0.004 MeV and an extrapolation RMSE of 0.179 $\pm$ 0.015 MeV. These results surpass other state-of-the-art machine learning models and also outperform the real-valued GRU baseline and product-unit ablation variants, while remaining robust to different theoretical priors, including WS4 and SEMF. Our findings establish complex-valued product-unit recurrent networks as a new benchmark for sequence-based nuclear-mass prediction.

Hydrodynamic models of stochastic particle systems represented by coarse-grained stochastic partial differential equations (SPDE), such as the regularized Dean-Kawasaki (DK) equation, do not accurately capture the short-time system dynamics that is dominated by non-Markovian effects, and low particle density regimes where the distributions are highly non-Gaussian. We develop a generative flow matching method that directly models the probability distribution of fluxes from particle simulations that explicitly incorporates non-Markovian and non-Gaussian effects. As a demonstration, we use this method to simulate the Kramers first passage time problem for a system of non-interacting Brownian particles. We show the model accurately captures the short-time behavior and provides better predictions of the statistical moments of the number density when compared against the solution of the Markovian baseline, regularized DK equation.

In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compress-then-predict pipelines such as PCA-GP (principal component analysis plus Gaussian process regression) handle high dimensionality, but rely on bases optimized for reconstruction rather than prediction. To address this gap, we propose a model that represents each output as a linear-Gaussian decoding of a low-dimensional latent state drawn from a Gaussian process prior. By analytically marginalizing the decoder weights, we couple compression and prediction in a single objective that scales to high-dimensional outputs. We refer to this model as Gaussian process latent factor regression (GPLFR). We demonstrate GPLFR by building the first spatially resolved emulator of global climate models for rocky exoplanets.

The critical question after a correct driving veto is not only whether a maneuver is unsafe, but whether the blocked interaction admits a lawful, auditable, and responsibility-bounded repair. Prediction and game-theoretic planners can suggest plausible cooperation, yet they do not return a proof that the repair respects hard rules, right-of-way, cost allocation, and ego fallback. We introduce CARVE, Certified Affordable Repair of Vetoed maneuvers via Envelopes, a certificate architecture for prediction-free interactive repair. Given a vetoed maneuver, CARVE constructs a finite repair lattice and emits a structured certificate recording the binding rule, selected joint repair, right-of-way-scaled cooperation envelope, responsibility-weighted cost split, and ego-only fallback. This certificate view reveals the algorithmic bottleneck: multi-owner repair induces a product lattice $M = \prod_j |\mathcal{A}_j|$. We therefore introduce CARVE-Q, a verifier-shielded quantum-AI search layer that applies quantum minimum finding only to this black-box lattice while leaving all safety authority classical. In the conservative verifier-oracle model, exact classical minimum finding requires $Θ(M)$ queries in the worst case, whereas Durr-Hoyer/Grover minimum finding uses $O(\sqrt{M})$ oracle queries with high probability. We prove verifier-shielded certificate soundness, priority non-elicitation, black-box query separation, and finite-precision reversible-oracle constructibility. We then demonstrate state-vector minimum finding on CARVE repair oracles up to 65,536 assignments and validate certificate preservation on Lanelet2-grounded INTERACTION replay with 100% right-of-way respect, 100% blame consistency, and zero priority false positives. The result is a trust-bounded quantum-AI pattern for certified autonomy: quantum proposes; CARVE certifies.

Given a 3-manifold $M$ with a network of line and point defects in its boundary, we define the skein module of this configuration, generalizing the well-studied case of 3-manifolds which only admit point defects in the boundary. We prove that when all defects are labelled by semisimple data, our skein module is isomorphic to the state space of $\partial M$ in the defect version of the Reshetikhin-Turaev TQFT constructed by Carqueville-Runkel-Schaumann. Our defect skein modules follow naturally by globalizing the graphical calculus of module categories and functors thereof, and generalize the possible defect data considered in the defect TQFT beyond the semisimple case.

We introduce a measure-based nudging framework for assimilating macroscopic observations into microscopic mean-field particle dynamics. The central difficulty is a representation mismatch: the forecast is a labeled particle system, while the observations specify only a smoothed, permutation-invariant density. To address this mismatch, we define the forecast-observation discrepancy as a quadratic functional on probability measures after applying the same smoothing operator used by the observation process. The Wasserstein gradient of this functional induces a transport velocity on state space, which yields a particle-level correction without constructing particle-to-particle matching, linearizing the dynamics, or estimating ensemble covariances. For a fixed observation scale, we prove well-posedness of the assimilated McKean-Vlasov dynamics and propagation of chaos for the interacting particle approximation. Under exact smoothed observations and an observability condition at the kernel scale, we establish an $L^2$-stability estimate showing exponential decay up to a bias floor controlled by model misspecification. Numerical experiments on linear, bimodal, chaotic, kinetic, and collective-motion systems demonstrate that the method can recover macroscopic structure from incomplete density-level observations.

We settle the projectedness problem for the compactified unpunctured supermoduli stack in every genus at least two. In genus two, the odd component is split, whereas the even component is non-projected. In every genus $g\geq 3$, both compactified parity components are non-projected.

Consider the $(2+1)$D Discrete Gaussian model (ZGFF) on an $L\times L$ box with a hard floor at height zero and zero boundary conditions, at low temperature. The second author, Martinelli and Sly (2016) showed that the surface has a plateau, filling nearly the full square, at height either $H$ or $H+1$ for an explicit function $H(L)$. In a companion paper, we studied the local laws of the top level lines near the four sides of the box, and showed that after rescaling each by $(L^{2/3-o(1)},L^{1/3-o(1)})$, they converge to a product of Ferrari--Spohn diffusions. Two key features of the top level lines remained unaddressed: their global limit shape, and the critical window marking the transition from a top plateau at height $H$ to one at height $H+1$. These features are intrinsically linked: deriving the global limit of the top level line is needed for determining whether it is preferable to be at height $H$ or $H+1$ near criticality. This work completes this picture as follows. First, we obtain the global limit of the top level lines: for every fixed $n$, the $n$-th from-the-top level line converges in Hausdorff distance to a deterministic shape $\mathscr{L}_n$ that features the Wulff shape at scale $N_n=L^{1-o(1)}$ near the four corners of the box. Second, we identify, for every $h$, the point of emergence of a macroscopic $h$ level line: the probability of this event is monotone increasing in $L$ (up to a $o(1)$ error), and undergoes a sharp transition from near $0$ to near $1$ in a critical window of width $\leq L^{1/2+o(1)}$ around a side length $L=L_c^{(h)}$. This transition is discontinuous in that, once a macroscopic level $h$ emerges, it immediately occupies nearly all the box, and the above global and local scaling limits (Wulff, Ferrari--Spohn) hold for it. The new results extend to the $(2+1)$D $|\nablaϕ|^p$-models (ZGFF is the case $p=2$) for every fixed $p> 1$.

QBism recasts quantum theory as a normative framework for an agent's probability assignments, with the Born rule taking the form of a consistency condition known as the Urgleichung. Motivated by this perspective, qplex theories provide a broader class of probabilistic models in which the sets of valid states and measurements are constrained by QBist-inspired geometric conditions. While qplexes have been extensively studied for single systems, their implications for bipartite correlations remain largely unexplored. In this work, we investigate bipartite correlations in qplex theories by expressing joint expectation values as inner products between suitably defined $C$-vectors. This geometric formulation allows Bell-type inequalities to be studied as optimization problems over qplex-compatible probability assignments. We first analyze the CHSH scenario and show that the shared inner-product structure of the $C$-vectors restricts the maximal value to the Tsirelson bound $2\sqrt{2}$. We then turn to the three-outcome CGLMP inequality $I_{2233}$ and find that the same qplex-derived norm and inner-product constraints allow the algebraic maximum of 4, thereby exhibiting superquantum correlations. These results show that qplex geometry captures enough structure to reproduce an important quantum bound in the two-outcome case, but not enough to recover the full set of quantum correlation constraints. The analysis therefore suggests that additional principles are needed to complete the QBist reconstruction of quantum theory.

The implementation of high-fidelity quantum gates for spin qubits requires accurate control of exchange interactions between electrons confined in quantum dots, but pulse distortions can limit this control accuracy. Although linear-dynamical distortions can be compensated for by appropriately convolving the control signal, determining the necessary convolution requires detailed knowledge of the distortion's transfer function, and therefore the calibration of numerous parameters. Alternatively, control pulses can be designed to have a net-zero time integral canceling out linear-dynamical pulse distortions. We generalize net-zero pulse designs to quasi-zero pulses allowing net-positive but reduced time integrals. Using these pulse designs, we systematically develop complete gate sets for exchange-only qubits, and study the resulting tradeoffs between pulse duration, fidelity, and the required number of tunable parameters, both in simulation and experiment. We benchmark the optimized gate pulses on Intel's Tunnel Falls six-dot device and show they achieve fidelities similar to those obtained with a full filtering approach, with identical pulse durations and fewer tuning parameters. This reduction in complexity opens the door to fast and easily automated calibration schemes compatible with large-scale commercial quantum devices.

We give a general framework for tomography of states that have bounded-extent with respect to a structured class of states. Let $\textsf{C}$ be a family of $n$-qubit states such that: $(i)$ $\textsf{C}$ is succinctly representable and $(ii)$ there is a weak agnostic learner of $\textsf{C}$. We give a tomography protocol for an unknown state $|ψ\rangle$ that is promised to admit a decomposition of the form $|ψ\rangle = \sum_i c_i |ϕ_i\rangle$, where $|ϕ_i\rangle \in \textsf{C}$ with bounded $\ell_1$-norm of the coefficients (which we call extent). Our main contribution is to show that a weak agnostic learner for $\textsf{C}$ can be boosted into a tomography algorithm for states with bounded extent with respect to $\textsf{C}$. Our reduction is black-box and applies broadly across model classes. As an application, when $\textsf{C}$ is the class of stabilizer states, we obtain tomography algorithms for states with stabilizer extent $ξ$ up to trace distance $\varepsilon$, in time $\textsf{poly}(n,(ξ/\varepsilon)^{\log(ξ/\varepsilon)})$, which is improvable to $ \textsf{poly}(n,ξ,1/\varepsilon)$ assuming the algorithmic polynomial Freiman-Ruzsa conjecture in the high-doubling regime. When the unknown state $|ψ\rangle$ is arbitrary, we give an algorithmic decomposition result in the spirit of a weak regularity lemma for quantum states with respect to $\textsf{C}$ and show that the structure in $|ψ\rangle$ that is explainable by $\textsf{C}$ can be efficiently learned. Our main conceptual message is that agnostic learning of a structured base class automatically yields learnability of its low-complexity linear span.

While damped spin dynamics is important for the understanding of magnetic materials, clear signatures of \emph{quantum corrections} to the Gilbert damping mechanism remain elusive. We propose a route to distinguish quantum and classical Gilbert spin damping using ferroelectric control of a magnetic dimer. Ab initio calculations for dimers on ferroelectric substrates show that polarization reversal switches the inter-spin exchange between ferromagnetic and antiferromagnetic regimes. We formulate a magnetization-based diagnostic that relates magnetization traces to entanglement dynamics, which enables ferroelectrical on/off control of dimer entanglement. Material-informed quantum Landau-Lifshitz-Gilbert simulations illustrate how the signature of magnetization dynamics can, in principle, be used to infer the existence of quantum Gilbert spin damping. This minimal and non-volatile platform connects first-principles modeling to experimentally accessible observables and provides a starting point for voltage-controlled quantum entanglement in magnetic spin networks.

The performance of quantum error correction (QEC) codes is limited by the underlying physical noise. Theoretical studies show that coherent and stochastic noise have different effects when performing QEC with either surface or repetition codes. We use the bitflip repetition code, realized in a transmon quantum processor, as a testbed to experimentally study the impact of injecting coherent versus stochastic errors on the logical performance. We adapt a scalable free-fermion simulator to simulate the experiments and we modify a subset sampling technique to efficiently sample stochastic noise in the quantum circuit. In the experiment, we do not observe the difference in logical fidelity predicted by simulation for either the distance-3 or distance-5 repetition codes. We hypothesize that this discrepancy could be explained by small drifts in qubit frequencies, which introduce phase-coherent noise that `stochastifies' the injected coherent errors. Our work contributes to advancing an understanding of how coherent errors affect experimental QEC.

In this work, we present constructions of quantum circuits to implement general measurements on quantum hardware. Firstly, we investigate a quantum circuit ansatz by following the Naimark extension with a universal set of gates, such as controlled-NOT and single-qubit gates; we call it a Naimark quantum measurement. We present a circuit ansatz framed by the Naimark extension, leaving single-qubit gates with parameters, and apply a classical optimizer to determine their parameters to approximate a desired quantum measurement. Secondly, we relax the Naimark measurement with quantum neural-network (QNN) circuits, employing parameterized quantum circuits. We present hybrid Naimark-QNN measurements by incorporating QNN circuits into Naimark measurements. Thirdly, we also consider fully QNN measurements with shallow parameterized circuits. Then, we compare the constructed measurement circuits, Naimark, hybrid Naimark-QNN, and fully QNN measurements, for strategies of state discrimination, such as minimum-error and maximum-confidence measurements. We demonstrate that QNN circuits can efficiently and effectively achieve near-optimal quantum measurements with fewer training iterations.

Constructor theory is a proposal to extend quantum information theory beyond both quantum theory and computation, to cover more general machines than programmable computers -- called constructors. It consists of newly conjectured physical principles that can be expressed as constraints on what tasks are possible, what are impossible, and why. These principles also determine the repertoire of the universal constructor, which is a programmable machine that can perform all physically possible tasks. The principles of constructor theory have novel physical content that supplements current dynamical laws, leading to new predictions for experimental tests. In this paper, we review the main experimental proposals to test the principles of constructor theory and discuss their implications for existing theories of physics and for their successors.

Quasiparticle poisoning bottlenecks superconducting qubits, limiting coherence and the scalability of quantum processors. In this work, we systematically investigate quasiparticle poisoning in superconducting qubits under three infrared (IR) shielding configurations, ranging from a dedicated multi-layer design to a simplified implementation. By measuring quasiparticle-induced parity switching, we demonstrate a suppression of the switching rate by over four orders of magnitude via the implementation of improved shielding. In the best configuration, the rate decreases over time following cooldown and reaches 0.069$\,$Hz on day 34, corresponding to an anticipated quasiparticle density per Cooper pair of $1.88\times10^{-11}$. To our knowledge, this represents the lowest quasiparticle density reported in the literature to date. The remaining quasiparticle population is likely dominated by sporadic phonon bursts stemming from mechanical stress release in the on-chip films, as well as from the surrounding environment. The effective qubit temperature follows the phonon bath down to 17$\,$mK, enabling initialization errors of $\sim 0.01\%$ for 3$\,$GHz qubits. These results demonstrate that proper IR shielding and thermalization are essential for suppressing quasiparticle poisoning and enabling high-coherence, scalable superconducting qubit systems.

Ising machines are a promising approach to solve combinatorial optimization problems. They map these problems onto the Ising model and search for low-energy configurations. However, navigating the rugged energy landscapes of these systems remains difficult. To improve this navigation, classical adiabatic annealing has been proposed in the literature as a heuristic optimization method for classical Ising machines. Using this technique, the Hamiltonian of the Ising machine is gradually transformed from an easily solvable Hamiltonian to the target Hamiltonian. However, its purported effectiveness is primarily motivated by an analogy to quantum adiabatic annealing, and systematic benchmarking has remained limited. In this work, we analyze the classical adiabatic annealing technique using continuation methods. Motivated by insights from this analysis, we propose an optimized annealing strategy we refer to as hybrid classical adiabatic annealing. We benchmark our proposed strategy using MaxCut instances with up to 800 spins and problems with external fields, for which it achieves a marginal improvement for a limited set of problems. We conclude that, although theoretically motivated and occasionally beneficial, the hybrid strategy does not offer a sufficient practical advantage over simpler, existing techniques.

The traveling salesman problem (TSP) is a significant classical NP-hard combinatorial optimization problem. In this work, we demonstrate that combining classical dynamic programming with quantum search can yield an achievable quantum advantage for TSP on the basis of excellent work by the authors of~\cite{ambainis2019quantum}. We design the quantum divide and conquer strategy to provide a parameterized spectrum for this combination. The hybrid algorithm proposed in~\cite{ambainis2019quantum} corresponds to a specific case in this spectrum, while the two extremes of the spectrum represent the purely classical Held-Karp and the purely quantum search algorithm, respectively. Within our parameterized spectrum, we prove that the optimal query complexity is $O^*(1.865666\ldots^n)$, achieved with the 4-subset scheme, while the counting in~\cite{ambainis2019quantum} overlooked half of the recursive branches. The correct query complexity of their algorithm is $O^*(2.225880\ldots^n)$ at their chosen parameter ($α\approx0.055362$), and cannot fall below $O^*(2^n)$ for any $α$ - meaning their $8$-subset scheme, correctly analyzed, never surpasses the classical Held-Karp bound. Furthermore, in previous studies on quantum advantages for NP-hard combinatorial optimization problems, researchers focused only on improvements in query complexity. Our work, however, points out that the quantum advantage stems not only from the quadratic speedup of quantum search but also from the structured quantum state preparation. We argue that structured state preparation is indispensable for realizing the oracle operator while maintaining the total time complexity of $O^*(1.865666\ldots^n)$. Therefore, we design an elegant method for preparing the set partition state, which makes our TSP solver practically executable.

This study investigates the analytical solvability of the Klein-Gordon and Duffin-Kemmer-Petiau (DKP) equations for a scalar particle interacting with a transcendental $α$-attractor-type potential, $V(x) = V_0 e^{a \tanh(bx)}$. We first address the problem of integrability within the framework of Picard-Vessiot theory. By analyzing the differential field extensions associated with the system, we demonstrated that the differential Galois group is the full special linear group $SL(2, \mathbb{C})$. Given that this group is not solvable, we provide rigorous proof for the non-existence of Liouvillian solutions, effectively ruling out any expression in terms of primitives and elementary functions. Building upon this result, we further establish that wavefunctions cannot be represented as finite compositions or transformations of classical special functions, such as those of the Bessel, Whittaker, or Heun families. This second conclusion is supported by the ``double-transcendence'' of the potential; we prove via the Hermite-Lindemann theorem that no rational coordinate transformation $z(x)$ exists that could map the physical equation into an ordinary differential equations(ODE) with rational coefficients. Consequently, the $α$-attractor potential is strictly non-integrable and lies entirely outside the landscape of solvable relativistic quantum systems.

We examine the non-Markovian dynamics and the generation of quantum coherence and entanglement within a triple-layer graphene (TLG) system embedded in a planar microcavity. Using time-dependent perturbation theory, we derive an exact analytic solution for the system and demonstrate how the confined electromagnetic field mediates quantum correlations between the graphene layers. We employ three complementary measures; the relative entropy of coherence (REC) to quantify quantum coherence, the tangle to assess tripartite entanglement, and a non-Markovianity measure derived from the REC to characterize quantum memory effects. Our analysis reveals that these quantum resources exhibit remarkable sensitivity to various control parameters. Specifically, we demonstrate that the number of cutoff modes, the spatial positioning of the layers, the momentum parameter, and the interlayer rotation angles provide effective control over coherence, entanglement, and memory effects. We further show that these measures exhibit an exceptional sensitivity to the rotation angle between the layers. Ultimately, our results establish cavity-confined TLG as a highly tunable platform for exploring vacuum-mediated quantum phenomena, providing a framework for the precise manipulation of quantum correlations in graphene-based photonic and optoelectronic devices.

The characterization of the quantum critical properties of genuine non-Hermitian many-body systems remains ambiguous as neither the state considered nor the definition of expectation values is unique. In this work, we investigate the quantum critical properties of two models of non-Hermitian XY spin chains with magnetic field. Using exact solutions, we systematically investigate the parameter dependence of the energy, the magnetization as well as the long-distance asymptotic behavior of static correlation functions. We compute expectation values within the standard formalism of quantum mechanics as well as within biorthogonal quantum mechanics and take two different states which one might reasonably consider to be the analog of the ground state of a Hermitian model. The critical properties, including such fundamental characteristics as the phase diagram, depend on both the formalism used as well as the state considered. We provide arguments in favor of the use of standard quantum mechanics. Which state to be taken in computations, depends on the (hypothetical) experimental preparation of the system.

In this work we show that long-range interactions can be significantly beneficial for implementing shortcuts to adiabaticity (STA) in many-body open quantum critical systems driven out of equilibrium, as well as for charging quantum batteries in the presence of dissipation. In sharp contrast to short range interactions where passage through criticality may demand STA control with non-zero interactions between infinitely distant spins, using the example of a Kitaev chain with long-range couplings, we find that the corresponding control may involve involve interaction strength with decays algebraically with distance. In case of non-unitary control, the advantage of long-range interactions manifest through reduction in the cost of STA. We further propose a modified STA technique aimed at charging a quantum battery in the presence of dissipation, in which case long-range interactions may enhance the resultant ergotropy. Our results establish long-range interactions as a valuable resource for quantum control, with direct implications for quantum technologies.

Continuous-variable quantum key distribution (CV-QKD), which uses quadratures of the electromagnetic field, enables practical quantum communication using standard telecommunication technologies. Unidimensional CV-QKD (UD-CVQKD) simplifies the implementation by restricting modulation to a single quadrature. In this work, we experimentally demonstrate a free-space Gaussian-modulated UD-CVQKD system operating under a high detector electronic-noise regime (1.4 shot-noise units). The system employs polarized coherent states with signal and local oscillator co-propagating in the same spatial mode in orthogonal polarizations, ensuring stable interference. System security is analyzed under both untrusted (UTD) and trusted (TD) detector noise models. While no positive secret key rate is obtained under the UTD model, the TD model enables secure key generation over a finite range of modulation variances, highlighting the critical role of detector trust in high-noise conditions. A maximum secret key rate of 270 kbps is achieved at an optimal modulation variance of 11.57. Furthermore, secure operation requires high-transmittance (low-loss) channels under such noise conditions. This study demonstrates the practical feasibility of free-space UD-CVQKD in realistic high electronic-noise detection constraints and highlights detector electronic noise as a key limiting factor in practical systems.

Continuous-time Quantum Annealing (QA) is a strategy for preparing the ground state of nontrivial many-body systems. In its standard form, the dynamics is generated by a time-dependent interpolation between a simple driving Hamiltonian and the target problem Hamiltonian, usually implemented through a linear schedule. This approach faces the crucial bottleneck of small spectral gaps, which may require exponentially long annealing times to ensure adiabaticity. Here, we show how to implement quantum control over the annealing schedule in a frustrated Ising ring, one of the simplest models exhibiting an exponentially small bottleneck gap. By optimizing smooth continuous-time annealing schedules with a dressed-CRAB approach, and using a digitized representation of the dynamics to efficiently evaluate gradients, we construct protocols that strongly outperform standard fixed schedules. The optimized dynamics bypasses the bottleneck through a strongly nonadiabatic mechanism, leading to efficient ground-state preparation despite the exponentially small minimum gap. In particular, the annealing time required to reach a fixed residual-energy threshold is found to grow linearly with system size rather than exponentially. We further examine a lowest-order variational counter-diabatic correction and find that, once schedule optimization is allowed, it does not lead to any improvement.

We experimentally demonstrate an improved optical biasing scheme for superconducting nanowire single-photon detectors (SNSPDs), which employs a cryogenic InGaAs-InP photodiode (PD) as a local bias source. It is found that, under illumination from a stable external light source, this PD generates a stable photocurrent in a cryogenic environment (~2.3 K), with fluctuations in the photocurrent primarily attributed to fluctuations in the incident optical power. Furthermore, by screening and effectively blocking stray photons leaking from the PD, which give rise to background dark counts, we have achieved an SNSPD exhibiting an ultra-low intrinsic dark count rate of 1e-4 cps. Utilizing this improved optical biasing technique, our SNSPD achieved performance comparable to that obtained under conventional electrical biasing: a system detection efficiency of 80.7%, a background dark count rate of 32.6 cps, and a minimum timing jitter of 57.5 ps. These results indicate that cryogenic-PD-based optical biasing serves as a viable, low-noise, and low-jitter alternative to traditional electrical biasing. Moreover, this work offers useful design guidance for the future development of PD-based low-noise bias sources and for the construction of all-photonic SNSPD systems tailored for high-precision quantum photonics applications.

Quantum computing offers several algorithms to compute the ground state of a problem Hamiltonian. The most desirable algorithms belong to the Fault Tolerant QuantumComputing (FTQC) regime, such as quantum algorithms with repetitive structure like Quantum Phase Estimation (QPE) and Quantum Signal Processing (QSP). However, in the Noisy In-termediate Scale Quantum (NISQ) regime, the most realistic approaches involve Variational Quantum Eigensolver (VQE) algorithms and their variants. VQE is an algorithm that searches for a parametrized unitary matrix called an ansatz whose purposeis to transform an easily prepared initial state into the groundstate of a given Hamiltonian. Adaptive Derivative-AssembledPseudo-Trotter (ADAPT)-VQE is a variant of VQE that im-proves this approach by constructing the ansatz iteratively so that the associated quantum circuit is as shallow as possible. A major difference between FTQC (i.e. not variational) algorithms and VQE is that FTQC algorithms do not construct a state transitiondirectly. Instead, they construct a projector that identifies the ground state using ancillary qubits that flag the good solution. The desired state is then obtained via amplitude amplification orpost-selection. In this work, we propose a VQE ansatz whose structure is more similar to that of an FTQC algorithm. Depending on its parametrization, this ansatz can be equivalent to either an Intermediate Scale Quantum (ISQ)-QSP or to an ADAPT-VQE quantum circuit structure. Our experimental results show that this first proposal of Projector Variational Ansatz (PVA) converges with a shallower ansatz than the usual ADAPT-VQE.

We study thermal quantum correlations and coherence in Heisenberg $XY$ model with anisotropic interactions under a uniform magnetic field $ B $. Using concurrence $C$, local quantum uncertainty (LQU), Bell-Clauser-Horne-Shimony-Holt (CHSH) nonlocality $ \mathbb{B}$, and coherence $C_l$ as quantifiers, we analyze how magnetic anisotropy $ δ_m $, coupling anisotropy $ δ_c $, Dzyaloshinskii-Moriya (DM) interaction $ D $, temperature $ T $, and magnetic field $ B $ modulate quantum resources. At low temperatures and relevant magnetic fields, the entanglement is maximized, but exhibits sudden death for $ δ_m = 0 $, which turns into a smooth decay as $ δ_m $ increases, highlighting its stabilizing role. LQU shows that stronger anisotropy suppresses quantum correlations, while $ \mathbb{B} $ induces a non-monotonic response peaking at a critical field $ B_c $. Bell-CHSH nonlocality violations ($ \mathbb{B} > 2 $) persist below $ B_c $, but thermal noise ($ T \geq 1 $) suppresses them. Coherence $ C_l $ is most robust to thermal fluctuations, especially for high \( δ_m \), which also dampens abrupt quantum phase transitions. The DM interaction is essential for entanglement generation, with $ D $ and anisotropy synergistically enhancing correlation resilience. We identify a hierarchy of thermal degradation: nonlocality ($ \mathbb{B} $) vanishes first, followed by entanglement ($ C $), then general quantum correlations (LQU), while coherence $ C_l $ persists the longest. These results demonstrate tunable control of quantum resources via anisotropy and external parameters, providing insights for the design of robust spin-based quantum technologies.

Multipartite entanglement forms the core of many networking applications. In the near-term future, it is expected that multipartite distribution will be achieved first through star topologies, making it important to understand the noise incurred during the distribution process. In such networks, elementary links are created stochastically and successful links must be stored while waiting for the remaining links, causing memory decoherence that depends on the random waiting times. We derive analytical expressions for both the average noise and its distribution, when distributing GHZ states under memory dephasing in star networks. We study and compare two distribution protocols: the factory and piecemaker protocol. Furthermore, we find expressions for the case of a global cut-off (allowing fast optimization of the cut-off without requiring Monte Carlo simulations) and extend the analysis for the factory protocol to depolarizing noise for arbitrary states.