Papers

Geometric phases have been extensively investigated in a wide range of quantum systems, often revealing deep connections to the underlying topology of many-body states. In this work, we examine two geometric phases defined for mixed quantum states$-$the interferometric geometric phase and the Uhlmann phase$-$extracted from two-site reduced density matrices of the transverse-field Ising model with nearest-neighbor interacting spins. By applying coordinated local unitary rotations to the spins, we compute the geometric phases associated with the two-site states across the critical point. We find that the interferometric phase is a more reliable indicator of the quantum phase transition in this model than the Uhlmann phase.

Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient unitary selected configuration Interaction (USCI) ansatz especially designed for quantum sampling is proposed to identify the most chemically significant electronic configurations on the Zuchongzhi 3.1 quantum processor. Subsequently, the transformer model QiankunNet learns from these sparse yet critical quantum data to infer and reconstruct the complete electronic wavefunction with high fidelity. Simulation of the challenging 40-qubit [2Fe-2S] ferredoxin active center achieves chemical accuracy. Simulation of the nitrogenase P-cluster in a 114-electron 73-orbital active space also reaches 12 milli-Hartree-level agreement with the best density matrix renormalization group (DMRG) result. QiankunNet-QSCI thus offers a practical route to accurate quantum-assisted electronic structure calculations on current devices.

High-overtone bulk acoustic wave resonators (HBARs) promise advanced phononics, yet achieving nonlinearity remains challenging. We demonstrate a radiation-pressure-type parametric interaction between GHz HBARs and low-frequency flexural modes in a suspended silicon nitride membrane, where mechanical displacement modulates the external dissipation rate to enable dissipative acousto-mechanical coupling. Benefiting from the high quality factor, the system enters the resolved-sideband regime at room temperature, yielding acousto-mechanically induced transparency. We observe tunable Kerr nonlinearity and generate coherent HBAR frequency combs via two-tone driving. Notably, our dissipative coupling strength is 20 times larger than the dispersive coupling, the highest ratio among reported hybrid dissipative-dispersive coupling systems, resulting in the experimental observation of amplification in the reflection spectra under red-sideband driving. The ability to interface dense HBAR modes with a common mechanical resonator provides a scalable on-chip platform for multimode phononic information processing, with quantum phononics potentially achievable at sub-Kelvin temperatures.

We formulate a framework of Floquet algebraic tomography for finite-dimensional non-Hermitian monodromy matrices from observable trace sequences $\zeta_n^{(O)}={\rm Tr}(OM^n)$. Since these sequences are constrained by the characteristic polynomial of $M$, the inverse problem is a finite-dimensional algebraic reconstruction problem rather than a generic exponential fit. We organize the reconstruction through the observable resolvent, spectral determinant, and Dirichlet spectral data, separating the common spectral skeleton from observable-dependent dressing. Cayley–Hamilton and Hankel methods recover the similarity-invariant spectral data, while multi-observable and Liouville-space extensions connect the construction to realization theory and tomography reconstruction. We further clarify the limits of identifiability from restricted observable algebras: the data determine a visible representative, micromotion can enlarge the sampled visible operator space, and exact symmetries impose residual invisible sectors. Two examples, a driven transmon qutrit and a finite non-Hermitian Floquet SSH chain, demonstrate leakage-induced visibility expansion, observable-dependent phase response, EP-accessible branch geometry, and disorder/probe-dependent observable-dimension readouts.

Quantum information engines leverage information as a thermodynamic resource to facilitate energy conversion. In the operation of such engines, the information flow between the working substance and the controller is pivotal, however, strategies for its efficient manipulation remain largely unexplored. Here, we investigate an autonomous information engine based on a double-quantum-dot setup, where a downstream dot coupled to two reservoirs acts as the working substance, and an upstream dot coupled to a single reservoir serves as the controller. By extending the second law of thermodynamics to incorporate the effects of nonreciprocal couplings between the dots and their electronic reservoirs, we develop a thermodynamic framework that allows us to demonstrate that nonreciprocity can significantly modulate the inter-dot information flow, thereby providing a robust control mechanism. We show that the influence of nonreciprocity can be equivalently understood through a mapping to an effective reciprocal system upon a reparameterization of chemical potentials and the electron-electron coupling strength. We further analyze the impact of nonreciprocity on the engine's performance and operation regime. Our findings establish nonreciprocal coupling as an effective control knob for designing and optimizing quantum dot information engines, surpassing the capabilities of conventional reciprocal configurations.

Physical-level noise on traveling bosonic modes remains a critical bottleneck for scalable quantum information processing. We show that for any single-mode bosonic code (qumode) corrupted by thermal or Gaussian displacement noise at loss rate $\mu$ and amplification $G$, a hybrid continuous-discrete-variable (CV-DV) interferometer using a single qubit ancilla and two controlled-Fourier (CF) gates sandwiching the noise channel suppresses its effects from linear to quadratic scaling. This is achieved without active error correction or destructive measurements of the encoded state, maintaining high success probabilities $\geq 0.5$ when $\mu G \leq 0.5$. When supplemented with multiple ancillas, the protocol converts photon loss into coherent Fock-damping, and thermal or displacement noise into a mixture of Fock-diagonal noise. The protocol is entirely code-agnostic. For the special case of $2^K$-fold rotation-symmetric bosonic codes, it simplifies to conventional error detection and projection with $K$ ancillas. Suppression with simple gates and few ancillas demonstrates a clear hardware-efficient advantage over previously proposed “bypass” schemes, where quantum information transferred to the DV ancillas is readily corrupted by ancilla noise. Finally, we extend the protocol to a qutrit DV ancilla. This demonstrates resilience to both CV noise and composite DV damping noise, achieving a truly hybrid noise suppression scheme that operates effectively even on CV encodings lacking a well-defined photon-number parity syndrome.

Triorthogonal codes are a basic ingredient in magic-state distillation, as they enable transversal implementation of logical $\mathsf{T}$ gates. As a non-Clifford operation, the $\mathsf{T}$ gate enables universal fault-tolerant quantum computation when combined with Clifford operations. We derive an existence criterion for even-weight triorthogonal generator matrices with a target dual minimum distance. The criterion combines triorthogonality constraints with MacWilliams identities via Krawtchouk-polynomial conditions on the dual weight distribution, yielding an integer linear programming formulation for the construction problem. We find novel nontrivial triorthogonal codes (degenerate and non-degenerate) that are not necessarily constructed from triply even binary linear codes. By imposing automorphism-group constraints, the formulation also recovers the known [[49,1,5]] and [[95,1,7]] triorthogonal codes. We also evaluate the performance of the obtained high-distance triorthogonal codes over the dephasing channel using different decoding methods, showing that automorphism ensemble decoding (AED) and guessing random additive-noise decoding (GRAND) are promising options.

We report an example of a many-body system, derived from the double kicked top (DKT), with non-chaotic yet mean-ergodic dynamics that displays strong eigenstate thermalization hypothesis (ETH) in the quantum regime. The analysis addresses a key open question: whether strong ETH is a quantum analog of ergodicity (or mean-ergodicity). Despite non-chaotic dynamics, the fluctuations of the diagonal matrix elements of an observable scale as $D^{-1/2}$, where $D$ denotes the Hilbert space dimension. Furthermore, the off-diagonal matrix elements show parameter-independent distribution, together with a smooth function $f_O(\bar{E}, \omega)$ that becomes nearly uniform in the large-$k_\theta$ domain. Our findings show that even mean-ergodic and non-chaotic systems can exhibit strong ETH.

Quantum error correction (QEC) is essential for realizing scalable quantum computation. However, when evaluating its benefits, most analyses assume idealized components, overlooking the imperfections inherent in realistic fault-tolerant (FT) implementations. In this paper, we investigate the performance of QEC schemes taking into account that quantum gates and measurements are themselves error-prone. We derive bounds for the failure probability of Shor-style FT-QEC schemes using limited structural information, such as the number of flag qubits and quantum gates. Our analysis separates and quantifies two key contributors to the failure rate: decoding errors and residual errors arising from circuit-level faults. The derived bounds highlight fundamental limitations in Shor-style FT-QEC performance and quantify how circuit imperfections degrade error correction capabilities, under the assumption of depolarizing noise.

A process-theoretic approach to electrodynamics based on persistent Kac-type stochastic processes is developed. Finite-velocity stochastic propagation is taken as primary, while relativistic wave equations arise as emergent descriptions after analytic continuation of Telegrapher-type dynamics. The Dirac and Maxwell equations are interpreted as arising from closely related persistent propagation structures differing only in spin representation. The framework is not intended to modify the successful empirical predictions of quantum electrodynamics, but to provide a different underlying ontology. Particles and fields are not treated as primitive entities with fixed intrinsic properties. Instead, relativistic particle and field structures emerge as stable collective modes of coupled persistent stochastic dynamics. Mass and charge acquire interpretations respectively as persistence and stochastic coupling scales. Stationary bound states are interpreted as metastable persistent stochastic modes with nontrivial internal sector dynamics. Spontaneous emission is viewed as stochastic destabilization of such modes, while stimulated emission arises through resonant synchronization of persistent transition currents by incident radiation. Gauge interactions are introduced at the level of propagation-sector amplitudes prior to the emergence of observable probabilities. Radiative effects, including the anomalous magnetic moment of the electron, are interpreted as effective stochastic dressing of coupled matter–radiation processes. Some comments on gauge symmetry, equilibration and the Standard Model are also included.

We study the impact of the potential parameters of the q-deformed modified Pöschl-Teller potential on the thermodynamic performance of a quantum Otto cycle, where the $q$-deformed modified Pöschl-Teller potential serves as the working substance. Analytical expressions for the energy spectrum and wave functions are derived, enabling a systematic investigation of heat exchange, work output, efficiency, and coefficient of performance. We show that $q$-deformation modifies the energy spectrum and creates distinct performance regions in the ($q$, $\Delta$) parameter space. Low ($\Delta$) and high ($q$) favour optimal heat engine efficiency, whereas high ($\Delta$) and low ($q$) improve refrigerator performance. The heat engine efficiency peaks in the low-($\Delta$), high-($q$) regime. These results highlight the q-deformed modified Pöschl-Teller potential as a versatile and tunable platform for exploring potential parameter-driven effects in quantum thermal machines.

We develop an exact analytical framework for studying the nonequilibrium dynamics of interacting quantum harmonic oscillators coupled to a modelled thermal reservoir and driven by external classical fields. The total Hamiltonian is diagonalized via successive Bogoliubov transformations, enabling a nonperturbative treatment of dissipation and driving. For two coupled oscillators with an external source applied to the first, we derive explicit energy expressions and analyze their dependence on coupling and bath parameters. The phase-space structure is characterized through Husimi $Q$-functions for initial separable coherent and number states. We obtain the reduced density matrix elements in the number-state basis and demonstrate that, in the absence of driving and at zero temperature, the reduced density matrix of the main system satisfies the Lindblad master equation. A generating function for the Husimi function is introduced to facilitate computation of higher-order correlations. The analysis is then generalized to $n$ interacting oscillators, with analytical energy expressions provided and the case $n=3$ examined in detail. Our results establish a versatile and exact framework for driven-dissipative quantum systems, with potential applications in quantum thermodynamics, open quantum systems, and many-body physics.

Quantum contextuality is a concept used to describe the property of hidden-variable theory that measurement outcomes predetermined by the hidden variables depend on the measurement context. The term measurement context can have different meanings, giving rise to different flavours of quantum contextuality. The first discovered flavour is Kochen-Specker (KS) contextuality where measurement outcomes will depend on what compatible measurements are jointly performed with the selected measurement. Another flavour, here to be compared with KS contextuality, is that referred to in Bohmian mechanics where outcomes of some specific measurements are not completely specified by the model state, but depend also on specifics of the measurement device used. It has been claimed that this type of Bohmian contextuality is necessary to enable KS contextuality in a hidden variable model. In this paper we show that this is not the case. The recently proposed Contextual Ontological Model (COM) [Hindlycke and Larsson, Phys. Rev. Lett. 2022] produces KS contextual predictions but does not have the Bohmian contextuality; the outcome of every measurement allowed by COM can be predicted from the model state itself. This distinguishes Bohmian contextuality from KS contextuality, and enables individual study of the two concepts.

We investigate the behavior of tetrapartite entanglement in a four-qubit Dicke state under relativistic motion by employing the Unruh-DeWitt detector model, where one detector undergoes uniform acceleration. We show that the entanglement exhibits a non-monotonic evolution: it first decreases and subsequently increases toward a finite value as the acceleration grows. In contrast to the conventional view that the Unruh effect leads to a monotonic degradation of multipartite entanglement, our results demonstrate that it can instead enhance multipartite entanglement within a finite parameter regime. This behavior reveals a dual role of the Unruh effect in multipartite systems. Our findings therefore provide a refined understanding of relativistic multipartite quantum correlations, indicating that Dicke states constitute more robust multipartite quantum resources against Unruh-induced decoherence and may offer advantages for relativistic quantum information processing tasks.

Fermi-Dirac machines were proposed recently as an approach to solving semidefinite optimization problems on quantum computers. Here, we reinterpret them as canonical quantizations of classical neurons. By viewing a classical neuron as an activation function applied to a parameterized classical Hamiltonian, we quantize this model by replacing classical variables with operators whose eigenvalues encode their possible values. This follows the standard approach to canonical quantization in quantum mechanics. Crucially, when the Hamiltonian consists of commuting operators, our construction reduces exactly to a classical neuron. More generally, our approach yields an activation observable, defined as an activation function applied to a parameterized quantum Hamiltonian. The output of this quantized neuron is a random variable with expectation value equal to that of the activation observable with respect to an input state. We develop efficient hybrid quantum-classical algorithms for evaluating outputs and gradients of our quantized neurons, enabling evaluation and training. These algorithms rely on basic primitives that include random sampling, Hamiltonian simulation, and the Hadamard test. We also quantize a whole host of other activation functions, including the smooth rectified linear unit (ReLU), sigmoid linear unit, Gaussian-smoothed ReLU, and Gaussian error linear unit (GeLU), which are known to be useful for deep learning applications. Numerical experiments indicate that neurons based on quantum Hamiltonians can learn functions that classical neurons cannot. We further define a computational decision problem based on Fermi-Dirac neurons and prove that it is BQP-complete, providing complexity-theoretic evidence against efficient classical simulation. Finally, we generalize our approach to continuous quantum variables and sketch two different ways of composing these neurons into networks.

This work explores the quantum propagator $K(x,t)$ as a solution of the system's dynamical equation. We develop a generalized propagator framework in which the propagator is written in the form $K=\exp\!\left(R+\frac{i}{\hbar}S\right)$, where $S$ governs the semiclassical phase structure and $R$ governs the amplitude transport and weighting of configurations. Starting from nonrelativistic quantum mechanics, the classical Hamilton–Jacobi equation emerges in the semiclassical limit, while $R$ reduces to the logarithm of the Van Vleck determinant. The formalism is then extended to relativistic field theory and minisuperspace quantum cosmology using functional methods and generalized Hamiltonian constraints. Then the resulting semiclassical equations recover the corresponding classical dynamics along characteristic flows. In minisuperspace models, the formalism yields coupled equations for geometric and matter sectors analogous to semiclassical Wheeler–DeWitt systems. In semiclassical gravitational settings, the gravitational contribution may admit an entropy-like interpretation, consistent with thermodynamic weighting factors appearing in Euclidean gravitational path integrals. The resulting framework suggests a unified semiclassical description in which propagator phases encode dynamical evolution while amplitude functionals encode transport and configuration weighting. This perspective motivates viewing mechanics as a relation between the differential structure of spacetime and the differential structure of the space of dynamical configurations.

The rise of artificial intelligence has led to a growing demand for computational power. In this situation, energy-efficient computing methods are being actively investigated, which raises a fundamental question: what is the relationship between computational error and thermodynamic cost? Here, we derive the error-cost trade-off in the binary classifier by considering classification based on nonequilibrium thermodynamics. We derive trade-off relations showing that classification error cannot be lower than fundamental bounds set by thermodynamic quantities: entropy production, which measures irreversibility, and dynamical activity, which quantifies the activity of the system. Our results show that when entropy production or dynamical activity vanishes, the classification error reaches $1/2$, equivalent to random guessing, whereas greater thermodynamic costs enable lower error. The results establish a quantitative trade-off in physical information processing, potentially leading to the development of energy-efficient computing devices.

We investigate the effectiveness of entanglement detection based on multiple fidelities via the geometry of the joint separable numerical range. When all reference states are product states, we derive a necessary and sufficient criterion for such detection: either some pair of reference states has nontrivial moduli of the local inner products on both subsystems, or the orthogonal complement of the span of the reference states is completely entangled. We further show that there exist sets of reference product states for which no proper subset is effective for entanglement detection, whereas the full set is. A typical example of this phenomenon is provided by unextendible product bases. Moreover, for a pair of reference product states on a bipartite system with arbitrary local dimensions, we characterize both the joint numerical range and the joint separable numerical range, showing that the joint separable numerical range is determined solely by their local fidelities, as illustrated by a representative two-qubit example. Our results offer a systematic approach to designing effective entanglement witnesses and lay the groundwork for extensions to higher-dimensional and multipartite scenarios.

We study impurity-induced particle growth and scrambling in clean one-dimensional free-fermion systems. We show that a single local impurity can act as a branching source: particle or operator weight propagates coherently into the free bulk, returns to the impurity, and is locally converted into additional degrees of freedom. We develop this branching picture in three complementary settings: a monitored free-fermion model with feedback, a fully unitary interacting particle model, and Heisenberg operator dynamics with an interacting impurity. In the monitored model, we find a feedback-driven transition for both boundary and bulk impurities. In the unitary particle and operator models, a boundary impurity gives rise to an analogous transition from saturation to sustained growth and scrambling. These results reveal how a single impurity can generate complex many-body dynamics in an otherwise clean and free quantum system.

Quantum machine learning is often motivated by the idea that quantum systems can expose useful high-dimensional structure that is difficult to access with classical models. We isolate one central component of this claim: the fixed data-encoding map. Amplitude, angle, and basis encoding are evaluated as deterministic feature maps for classical supervised learning under matched output dimensionality and strong classical controls. The benchmark compares these encodings against raw linear models, random Fourier features, polynomial features, PCA, RBF SVMs, and shallow neural networks across diverse classical datasets. Rather than treating performance as a single endpoint, we analyze the geometry of each representation through effective rank, condition number, centered kernel alignment, predictive performance, and practical overhead. The resulting picture is mechanistic: amplitude encoding can remove magnitude information through unit-sphere normalization, angle encoding can become geometrically redundant with raw linear features, and basis encoding can impose a binary Hamming geometry that is poorly aligned with smooth decision structure. These findings do not argue against quantum computation, however, they show that fixed quantum-inspired encoding geometry alone is not a reliable source of machine-learning advantage on classical data.

The recent arXiv posting arXiv:2605.20443 by Lohmiller and Slotine attempts to address the omission of the Bohm quantum potential in their proposed exact equivalence between classical action and the Schrödinger equation. They introduce a position-dependent time transformation to argue that the spatial derivatives of the probability density amplitude vanish for the Feynman kernel. A rigorous mathematical examination of this transformation reveals a violation of the multivariable chain rule. The spatial derivatives do not vanish in the physical reference frame. The mathematical framework presented by the authors remains identical to the well-established semiclassical Van Vleck propagator, which is exact exclusively for quadratic potentials.

Large-scale entanglement-based quantum key distribution (QKD) networks are commonly assumed to require authentication resources scaling quadratically with the number of users. We show that realistic quantum communication networks operating under loss, decoherence, and LOCC constraints exhibit fundamentally different scaling laws. Using Pauli transfer matrix (PTM) transport, we demonstrate that Bell correlations decay exponentially along entanglement-swapping paths, generating finite operational correlation lengths and sparse operational entanglement graphs. In sparse metropolitan quantum networks, the number of CHSH-usable Bell pairs consequently scales linearly with network size, while authentication complexity scales as \[ \Theta(N\log N), \] under sparse-mixing assumptions. We further present an ancilla-assisted distributed Bell-state verification framework for realistic E91 quantum metropolitan infrastructures. Our results suggest that scalable authentication in quantum communication networks emerges directly from the physics of entanglement transport.

While classical Sinkhorn scaling applies to nonnegative matrices, we show that any complex square matrix whose element-wise absolute value has total support can be mapped to a phased doubly stochastic matrix, or alternatively embedded into a larger doubly stochastic matrix via matrix completion. Standard Birkhoff-von~Neumann and Pauli decompositions represent such matrices as linear combinations of $O(N^2)$ permutation or Pauli terms, leading to a large ancilla overhead in a quantum Linear Combination of Unitaries (LCU) implementation. We prove that a bottleneck variant of Birkhoff's algorithm reduces the number of permutations to $O(N\log(N/\varepsilon))$, where $\varepsilon$ is the $\ell_1$-norm approximation error of the reconstructed matrix, and demonstrate empirically that a largest-weight greedy variant requires only $\approx 2N$ terms for dense matrices (the exact average observed is $\approx 2.4N$). The quadratic reduction in term count directly shrinks the ancilla register from $2\log_2 N$ to $\log_2 N$ qubits, shortens the SELECT circuit, and is especially valuable in fixed-Hadamard LCU architectures, where—under the standard uniform-coefficient assumption—the success probability scales as $1/K$; our convex decomposition achieves $\alpha=1$ exactly and therefore avoids this penalty. The approach enables compact quantum implementations of dense operators appearing in optimal transport, non-Hermitian simulation, and other settings amenable to Sinkhorn preconditioning. Furthermore, because the decomposition is a convex combination, the LCU normalization constant for the scaled matrix $S$ is exactly $\alpha_S = 1$, and the uniform superposition is an eigenvector of $S$ with eigenvalue~1. This structure can be exploited to achieve high success probability without amplitude amplification in many practical scenarios, including quantum walks and Markov chain simulations.

Polarization skyrmions are usually characterized through a unit Stokes vector field, which defines a map from the transverse plane to the Poincaré (Bloch) sphere and allows one to assign a skyrmion number under appropriate boundary conditions. This description assumes complete polarization. In many optical situations, however, the local polarization state is partially polarized and is therefore described by a spectral polarization matrix rather than by a single Jones vector. We formulate a density-matrix-based effective extension of the skyrmion-number construction for statistically stationary, quasi-monochromatic paraxial fields in terms of the normalized spectral polarization matrix. Its eigenvectors define two orthogonal polarization eigenstates, corresponding to antipodal points on the sphere, while the eigenvalue imbalance is fixed by the local degree of polarization \(P({\bf r})\). Averaging the opposite skyrmion-density contributions of these two eigenpolarizations leads to an effective skyrmion number in which the usual pure-state density is weighted by \(P({\bf r})\). The resulting quantity reduces to the conventional skyrmion number for fully polarized fields and vanishes in locally unpolarized regions. In general it is not an integer topological invariant; rather, it is an integrated effective measure, constructed from local Stokes parameters, of the skyrmionic content associated with the polarized part of a mixed optical field. We illustrate the construction with an analytic Gaussian-beam example in which a degree-one polarization texture is combined with a radially varying degree of polarization.

Leveraging noise bias, where phase-flip errors dominate over bit-flips, can drastically reduce the hardware overhead of fault-tolerant quantum computation, but existing approaches require bias-preserving CNOT gates whose implementation remains experimentally challenging and is provably impossible for strictly two-dimensional systems. We show that high-fidelity quantum non-demolition (QND) multi-qubit Pauli $Z$ measurements provide an equally powerful yet more accessible primitive. We demonstrate that such measurements can fully replace bias-preserving CNOT gates for compiling all operations required by bias-tailored error correction, including stabilizer measurements for repetition codes, XZZX surface codes, and LDPC codes. We propose concrete physical implementations of this primitive for two platforms: solid-state nuclear spins coupled to electron spin ancillas, and dissipatively stabilized superconducting cat qubits. Through circuit-level numerical simulations, we show that an asymmetric XZZX surface code implemented with weight-four QND $Z$ measurements achieves a phase-flip threshold of $\sim\!1.25\%$ and provides a qubit overhead reduction of up to $6\times$ compared to a bias-unaware surface code at noise bias $\eta = 10^4$. In the regime of very large bias, a repetition code with QND $Z$ measurements attains a threshold of $\sim\!2.3\%$ and achieves overhead comparable to that of a bias-preserving CNOT scheme, without requiring such a gate. Our results establish QND multi-$Z$ measurements as a practical and hardware-efficient route to fault-tolerant quantum computation for a broad class of biased-noise platforms.

Multi-output time-series forecasting in energy systems is challenging because of nonlinear dynamics, multi-scale seasonality, and strong dependencies across correlated series. In this work, we investigate two hybrid quantum-classical frameworks for multi-stream time-series forecasting on a real Smart Meter dataset comprising 103 household electricity consumption time-series, with experiments executed on the $ibm\_marrakesh$ superconducting quantum processor. The first model, Kernelized Quantum Reservoir Computing with Repeated Measurement (KQRC-RM), combines coupled quantum reservoirs, ancilla-assisted repeated measurement, and kernelized readouts to model temporal dynamics and cross-stream correlations jointly. For a 3-stream time-series input and output, the KQRC-RM model using 114 qubits achieves an MAE of 0.0811 on MPS simulator (36.92% improvement over its classical analog) whereas performance degrades to an MAE of 0.1524 on hardware. The second, a Projected Quantum Kernel Gaussian Process (QGP), replaces fidelity-based kernels with projected kernels constructed from local reduced-state statistics. Using a topology-aware 100-qubit QGP model to predict 100 multi-output time-series values, we observe 49% of time-series outputs achieve high-accuracy predictions (MAE $<0.15$), with an average MAE of $0.082$ for this low-error group. The medium-error regime (MAE $0.15$-$0.35$) has an average MAE of $0.229$, while the high-error regime (MAE $>0.35$) has an average MAE of $0.664$. Overall, this reduces the average MAE relative to the classical GP baseline by 62.01% on MPS simulator and 40.37% on hardware. Together, these results demonstrate the feasibility of hybrid quantum machine learning for multi-input, multi-output time-series forecasting at the 100+ qubit scale on NISQ devices.

We study the statistical detectability of intra-block temporal drift in finite-key entanglement-based quantum key distribution, with particular relevance to E91-type parameter estimation and monitoring. Drift is modeled as a mean-preserving Lipschitz perturbation of Bernoulli observables, capturing structured temporal variation that is invisible to global-average tests. For a block of size $n$ and confidence levels $(\alpha,\beta)$, we formulate a minimax hypothesis-testing problem and define the minimal detectable amplitude. We derive matching lower and upper bounds yielding $\delta_{\min}(n,\alpha,\beta)=\Theta(n^{-1/2})$: if $n\delta^2 \to 0$, no level-$\alpha$ procedure can guarantee nontrivial uniform power over the admissible drift class, whereas a calibrated CUSUM statistic detects drift at the matching scale. Explicit constants for linear, sinusoidal, and step profiles, together with simulations, confirm the predicted scaling collapse. The result quantifies a finite-block monitoring-resolution limit and is distinct from composable security certification.

Theoretical descriptions of collective light–matter dynamics often rely on the mean-field (MF) or single-excitation (SE) approximations, yet the parameter regimes where they apply are rarely clearly delineated. Here we show that representative limiting regimes are characterized by two independent parameters: the number of molecules $N$ and the excitation number $N_{\rm exc}$. In the Tavis–Cummings model, when $N\gg 1$ and the excitation density $N_{\rm exc} / N \to 0$, MF and SE descriptions agree and yield linear collective dynamics, showing harmonic Rabi oscillations. At finite excitation density ($N_{\rm exc} / N \sim \mathcal{O}(1)$), the large-\(N\) limit remains accurately described by MF dynamics but becomes nonlinear in $N_{\rm exc} / N$, manifested by a Duffing equation for the cavity amplitude with anharmonic Rabi frequency. We further show that cluster expansion systematically restores finite-$N$ correlations beyond MF. When local vibronic interactions are included, the same linear collective limit is reached by both approximations, with SE reaching it through polaron decoupling and MF through linearization. This two-parameter regime map clarifies the limits in which different theoretical descriptions provide controlled descriptions of collective light–matter dynamics.

The rapid development of coherent short-wavelength light sources in the extreme ultraviolet (EUV) and soft X-ray (SXR) regimes has created a growing need for advanced optoelectronic detection capabilities, particularly for quantum-noise-limited measurements, microelectronics and semiconductor metrology, and emerging quantum information applications. However, extending balanced photodetection to these wavelength regimes is severely hindered by a fundamental bandwidth-noise trade-off imposed by the exceptionally large junction capacitance of EUV-SXR silicon photodiodes. Here, we report a novel wideband photoreceiver architecture that overcomes this bottleneck via a bootstrapped transimpedance amplifier design. By leveraging a low-noise junction field-effect transistor interface, we effectively isolate the photodiode capacitance and suppress the apparent input capacitance seen by the core amplifier. Combined with active compensation of parasitic feedback reactance, this architecture mitigates the conventional trade-off between detector active area and signal bandwidth. Experimentally, we achieved a system-level input-referred noise floor of 13 $fA/\sqrt{Hz}$, closely approaching theoretical thermal limits. Furthermore, we achieved a six-fold extension in signal-to-noise limited bandwidth and, through the implementation of a novel grounded field plate, demonstrated a common-mode rejection ratio (CMRR) exceeding 30 dB up to 100 kHz. This highly scalable, silicon-based architecture effectively bridges the short-wavelength detection gap, establishing a robust experimental platform for next-generation quantum-noise-limited and quantum-enhanced X-ray measurement, as well as ultra-sensitive inspection and metrology applications in high-numerical-aperture EUV lithography.

Frequency-modulated continuous-wave radar sensing often relies on labeled measurements that are costly, restricted, or difficult to collect at scale. This work evaluates physics-informed digital twins as controlled testbeds for early-stage quantum-classical radar learning. Two synthetic radar benchmarks are considered: unmanned aerial vehicle classification from range-Doppler maps and human fall detection from Doppler-time spectrograms. For both tasks, inputs are standardized, reduced using principal component analysis, and classified using either a radial basis function support vector classifier or a quantum support vector classifier. All quantum-kernel results are obtained using noiseless classical simulation; no quantum hardware is used, and no quantum-advantage claim is made. Across five random seeds, the quantum support vector classifier improves the UAV benchmark from four principal components onward, reaching an accuracy of 0.941 +/- 0.012 at eight components, compared with 0.880 +/- 0.029 for the classical baseline. On the fall-detection benchmark, both classifiers perform similarly, with a small quantum-kernel improvement at higher feature dimensions. A Gaussian-noise robustness study shows limited performance degradation across the tested noise levels, while preserving the UAV quantum-kernel gain. These results support digital twins as useful, controlled environments for radar-QML benchmarking prior to measured-data validation and hardware execution.