Papers

We study the quantum dynamics of the dynamical region of the Reissner–Nordström geometry using a minisuperspace reduction and affine quantization, which is naturally suited for positive-definite geometrical variables. The resulting Wheeler–DeWitt equation becomes separable, yielding Hermite-polynomial modes in one sector and Gaussian-like radial solutions in the other. Affine quantization introduces additional short-distance contributions that modify the small-radius behaviour of the wave function. By constructing normalizable semiclassical wave packets, we analyze the resulting probability distributions in minisuperspace and the role played by the electric charge in the quantum dynamics. Our results extend previous affine-quantization studies of the Schwarzschild case to the charged Reissner–Nordström geometry.

Randomized Hamiltonian simulation methods are often governed by a trade-off between systematic bias and sampling overhead. We study how classical variance-reduction techniques can be applied to such methods without changing their mean channel, and therefore without introducing additional bias. As a motivating unbiased estimator, we formulate continuous time-evolution probabilistic angle interpolation (continuous TE-PAI), a quasiprobabilistic random-circuit protocol whose remaining Monte Carlo error is purely statistical. Continuous TE-PAI removes Trotter discretization error with finite-depth random circuits, whereas deterministic Trotterization does so only in the infinite-depth limit. Further, in tensor-network simulations, we demonstrate that discretization error can cause an unphysical exponential growth in the bond dimension required for Trotterized simulations, whereas comparable-depth continuous TE-PAI circuits avoid this growth. We then show that the variance of randomized product-formula-based estimators admits a canonical decomposition into a classical counting component and a quantum ordering component such that the dominant simulation overhead results from the non-commutative parts of the Hamiltonian dynamics. Motivated by this decomposition, we achieve an $\approx70\%$ error-reduction using the counting-component for small systems whereas our tensor-network simulations of $n=30$ spin-chain dynamics use coarser statistics tailored to the observable and estimator attaining a negligible bias and a reduction of $\approx 80\%$ leading to $\approx91\%$ and $\approx96\%$ sampling-cost reductions, respectively.

Energies of H, He-like ($Z=2-18$) ions, Li, and Be are investigated under a spherically symmetric Gaussian potential through a density functional formalism. The radial Kohn-Sham equation has been solved by invoking a work function-based exchange potential. The effect of electron correlation is analyzed by incorporating two functionals: a local parameterized Wigner functional and a non-linear gradient- and Laplacian-dependent Lee-Yang-Parr (LYP) functional. The generalized pseudospectral method is employed to provide accurate numerical eigenfunctions and eigenvalues. This allows nonuniform, optimal spatial discretization fulfilling the Dirichlet boundary conditions. This work demonstrates a possible manipulation of energy by controlling dot parameters. Apart from ground states, exploratory results are also reported for low-lying excited state $1s2s$ ($^{1,3}S$) of He atom. Companion calculations are also performed for various information-theoretic measures, such as Shannon entropy in position ($S_{r}$), momentum ($S_{p}$) spaces, and Fisher information in position space ($I_{r}$). The behavior of correlation functionals in presence of Gaussian potential is examined critically. We find that energy increases, $S_{r}$ exhibits minima, while $S_{p}$, $I_{r}$ attain maxima for a decrease in the width of potential, whereas an increase in potential depth further amplifies these effects across all properties. The Fisher-Shannon plane reveals a progressive localization as well as the compression of electronic density, and thereby indicates a weakening of relative electron-correlation effects. In the Collin's conjecture, it gives rise to a non-linear loop-like feature. Much of the results are presented here for the first time.

It has been observed that the mutual scalar products of the vectors in a SIC-POVM are given by algebraic units, and at least in some cases by square roots of Stark units. The full picture is somewhat more complicated, especially if non-minimal SIC-POVMs are considered. We present a mixture of exact and numerical evidence suggesting that the overlap units are always products of integral powers of square roots of Stark units from ray class fields all of which are attached to the maximal ring of integers in the base field. In the non-minimal case a lattice of such ray class fields is involved. In every second dimension (counted in a certain way) some of the overlap units equal $\pm 1$, and we show that this follows from a special property of the ray class fields. Our observations are complementary to but consistent with the claim that the overlap units can be calculated directly from the Shintani–Faddeev modular cocycle.

Floquet prethermalization can endow interacting quantum solids with long-lived, approximately conserved quantities, enabling Hamiltonian engineering and new dynamical probes. Using a hyperpolarized network of dipolar-coupled $^{13}$C nuclear spins in diamond driven by pulsed spin-locking, we access a rotating-frame prethermal plateau with quasi-conserved transverse magnetization and cycle-resolved inductive readout. Within this prethermal manifold we observe a robust rotating-frame solid echo: after an apparent decay of the rotating-frame free-induction signal over a delay $\tau$, the magnetization revives at time $2\tau$ following a single $(\alpha)_y$ pulse, with maximum amplitude near $\alpha\simeq\pi/2$. The echo envelope decays as a stretched exponential with characteristic time $T_2'\approx 13\,$ms. Analytical arguments and toy-model simulations attribute the revival to Floquet micromotion that transfers coherences between operator subspaces, so that only a subset of the many-body dephasing dynamics is inverted by the $y$ pulse. These results translate classic echo physics into the prethermal rotating frame and point to continuously interrogated prethermal spin ensembles as a versatile platform for high-throughput spectroscopy, Hamiltonian engineering, and long-duration quantum sensing.

Readout of state populations in nitrogen-vacancy centers from fluorescence measurements at room-temperature is routinely achieved via contrast-based calibration. The fidelities achieved by this conventional approach are limited by reducing the dynamical fluorescence behaviour of the NV center to a scalar value, and calculating the population of each possible state independently. To address these limitations, we use regression models trained on experimental data to map the fluorescence signals onto ideal simulated populations. Additionally, we enhance the informational content of the fluorescence signals by performing measurements during induced Rabi oscillations. Our results demonstrate that including these dynamical signals significantly reduces state readout errors across multiple tested models. Notably, linear ridge regression performs nearly on par with a non-linear kernel-based model, showing that simple models already capture the relevant mapping between the enhanced fluorescence signals and the underlying state populations. This data-driven approach provides a robust alternative that achieves higher fidelities than conventional calibration in our setting, paving the way for high-fidelity state readout in solid-state quantum registers.

Fractional quantum Hall (FQH) states are a central paradigm of strongly correlated quantum matter and a key platform for topological quantum computation. Here, we propose a purely dissipative protocol based on local loss and pump channels for preparing Laughlin-like states at filling $1/3$, with a possible extension to other $1/M$ filling states. We show that the Laughlin-like state is the exact unique steady state of the Lindbladian under open boundary conditions. Finite-size analysis of the Lindbladian gap suggests efficient dissipative preparation over the system sizes and parameter regime considered. We further demonstrate adiabatic pumping of a Laughlin-like state through slow modulation of the pump channels during the evolution. Our work opens a feasible route to preparing and manipulating FQH states on near-term quantum simulators.

Optical knots are topologically structured light fields whose phase or polarization singularities trace linked or knotted trajectories during propagation, making them promising candidates for high-dimensional optical information carriers. Their use in communication or quantum-information protocols, however, requires a practical readout method that can distinguish a chosen knot alphabet with low crosstalk. Here, we demonstrate a proof-of-principle full-field sorter for optical knots using one or two optimized phase-only elements. The sorter maps each input knot to a predefined output region and is optimized directly from the output intensity distributions to enhance correct assignment, suppress crosstalk, and avoid degenerate mappings between distinct knots. We apply the method to an alphabet composed of the Hopf link, trefoil, and cinquefoil optical knots. Two optimized phase planes improve the sorting performance relative to a single plane and enable high distinguishability for the three-knot alphabet. We further benchmark the sorter under common experimental imperfections. These results extend full-field optical mode sorting to topologically structured light and provide a readout route for knot-based high-dimensional optical communication.

Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In this work, we explore a Quantum Convolutional Neural Network (QCNN) as a surrogate model for predicting temperature variations in groundwater induced by geothermal heat pumps in the city of Munich. To comply with the scalability constraints of current quantum hardware, the original high-dimensional simulation output is reduced to a compact set of representative parameters that serve as training targets for the surrogate. The proposed QCNN architecture consists of a quantum convolutional layer, a quantum pooling layer, and a fully connected quantum readout stage. Convolution and pooling operations are realized via parameterized quantum circuits based on rotational gates and measurement-driven decoding, while a Hamiltonian-inspired feature-encoding scheme is used to prepare informative input states on the quantum device. We evaluate the QCNN across multiple execution backends, including an ideal statevector simulator, a noisy simulator, IBM's 127-qubit Kyiv quantum processor, and the same hardware augmented with advanced error-mitigation techniques. Realistic noise models are employed to approximate device behavior and to assess the impact of mitigation strategies. Model performance is benchmarked using mean squared error (MSE) on both training and testing sets. The results show that, although classical neural networks still achieve the highest predictive accuracy, the QCNN attains competitive and consistent performance on simulators and exhibits noticeable improvement under error-mitigated hardware conditions. These findings indicate that quantum-enhanced surrogate modeling is a promising direction for future groundwater temperature prediction as quantum hardware and error-mitigation techniques continue to mature.

In the domain of quantum communication, cryptographic protocols often require users to have access to trusted qubit sources or detectors. Recently, it was shown that on an architecture called the Qline, several protocols can equivalently be performed by parties capable only of single-qubit rotations. In this work, we introduce two composably secure constructions that together show how in most quantum cryptographic protocols, parties traditionally required to perform trusted qubit preparation or measurement can delegate these tasks to an untrusted provider and instead rely on a trusted single-qubit rotation device. Our first construction implements single-qubit measurement and is universally applicable across any context. In contrast, our second construction, which addresses qubit preparation, relies on specific assumptions regarding the underlying protocol. We show, however, that these assumptions are inherently satisfied by the vast majority of common quantum cryptographic protocols. A notable consequence of our results is the formal validation of the Qline as a versatile architecture capable of supporting a wide range of single-qubit protocols.

Bell's theorem reveals that quantum theory is in tension with classical causal reasoning and, in particular, the notion of local causality. This is now understood as a particular example of non-classicality in the study of correlations in (Bayesian) networks with both unobserved and observed nodes: the correlations are probability distributions over the observed nodes. There is a great deal of work aiming to understand the bounds on quantum and classical correlations in such networks and one approach is to consider outer approximations to the former. Along these lines, we consider quasiprobabilistic models for Bayesian networks, which can be seen as classical models but the probability distributions involving unobserved nodes are "replaced" with quasiprobabilities that respect normalisation but not positivity. We denote the set of correlations resulting from these models as the quasi set. Such models have a history in the study of Bell-type non-classicality where it has been shown that they can produce all non-signalling correlations. We show a generalisation of this result for a broad class of networks, which motivates a conjecture that the quasi set recovers the so called nested Markov model. Our work utilises a connection to tensor network decompositions, which may be of independent interest.

Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus. UPCs offer a pristine electromagnetic pathway: coherent vector-meson photoproduction generates patterns of diffraction and two-source interference that directly encode the nuclear spatial density. Turning these patterns into quantitative constraints is, however, a challenging inverse problem, complicated by correlated sensitivities to deformation and neutron skin, phase smearing, and experimental backgrounds. Here we introduce an interpretable Multitask deep-learning framework that maps transverse momentum distributions to multiple nuclear-structure indicators simultaneously and identifies the kinematic regions driving each inference. We demonstrate the approach with coherent $J/\psi$ photoproduction in $^{96}_{40}\text{Zr} + ^{96}_{40}\text{Zr}$ collisions, showing that the learned features separate diffraction-dominated and interference-dominated information and provide analysis-ready observables for future high-luminosity data.

Detailed balance underlies conventional Markov-chain Monte Carlo (MCMC) algorithms. Yet in classical systems, breaking detailed balance generates irreversible probability currents and can accelerate sampling. Whether irreversibility can similarly enhance quantum MCMC remains an intriguing question. Here we show that irreversibility provides a new route to improving the recent quantum-enhanced MCMC (QEMC), which combines quantum proposals with classical acceptance. By introducing state-dependent proposals that break detailed balance while preserving the target stationary distribution, we develop an irreversible quantum-enhanced Monte Carlo (IQEMC). Guided by Landau-Zener transitions, IQEMC promotes large energy descents from high-energy states while maintaining stable transitions near low-energy states. On spin-glass benchmarks, IQEMC outperforms QEMC without increasing computational complexity and, unlike the annealing baseline, exhibits a spectral gap that increases with system size and annealing speed. These results establish irreversibility as a physically grounded mechanism for enhancing quantum MCMC.

In this paper, the phenomenon generally classified as confirmation bias is formulated on the space of square-root probabilities (or equivalently, using the structures of quantum probability). In this framework, observations are modelled by matrices, rather than random variables on a probability space. In the problem of binary hypothesis testing, an optimal evidence choice minimises the expected error probability. We show that the resulting optimal choice of evidence leads to a confirmation bias, thus revealing a surprising aspect of rationality that encompasses confirmation bias. Specifically, in sequential evidence sampling, the implicit optimality leads to two remarkable evolutionary advantages, namely, (a) the decision maker requires only the smallest memory capacity, and (b) the error probability can be reduced exponentially in sample size. A complementary approach based on the framework of active inference – where the decision maker seeks evidence that provides maximum information – is then considered. The resulting optimal evidence is shown to agree with the one obtained by minimising error probability. Our framework provides an easy-to-implement protocol for an active quantum inference, whereby the optimal evidence choice for making an inference is sought over the space of matrices.

Shortcuts to adiabaticity have made topological edge-state transfer fast in the single-particle regime, but their extension to interacting systems is obstructed by nonlinear phase accumulation. We show that this obstruction can be removed in Su-Schrieffer-Heeger chains by making the next-nearest-neighbor shortcut hopping phase tunable. In the mean-field regime, this yields an exact nonlinear shortcut: one hopping quadrature keeps the state on the instantaneous dark-state trajectory, while the orthogonal quadrature cancels the interaction-induced self-phase modulation. The resulting protocol is nonperturbative in the mean-field interaction strength. When applied to the full Bose-Hubbard dynamics, the mean-field shortcut remains beneficial but saturates below unit fidelity, exposing genuinely many-body corrections beyond the product-state picture. We then optimize the transfer directly in the many-body Hilbert space and find that complex, phase-tunable next-nearest-neighbor hoppings recover near-perfect fidelity. Our results show that hopping phases are not merely a technical refinement, but a key control resource for fast and high-fidelity transport in interacting topological systems.

We show that the Scarf I potential problem in a position-dependent mass background of the type $m(\alpha;x) = (1 + \alpha \sin x)^{-2}$, $0<\alpha<1$, can be solved by using a point canonical transformation mapping the corresponding Schrödinger equation onto that of the Scarf I potential with constant mass. The inverse point canonical transformation then provides some exactly-solvable rational extensions of the Scarf I potential with positive-dependent mass associated with $X_m$-Jacobi exceptional orthogonal polynomials of type I, II, or III. The Scarf I potential problem with position-dependent mass is shown to exhibit a deformed shape invariance property in a deformed supersymmetric framework. Such a property is also valid for extended potentials of type I and II. The results are illustrated with a simple example.

Matrix Product State (MPS) methods are among the most effective approaches for the classical simulation of quantum circuits, but their practical performance depends strongly on simulator hyperparameters, and default settings are often suboptimal. In this work, we propose a two-stage framework for automatic hyperparameter selection for quantum MPS simulation. In the first stage, we perform offline single-objective CMA-ES optimization under a fidelity constraint and construct a database of circuit–configuration–performance evaluations. In the second stage, we define a set of static circuit features designed to capture MPS-relevant structural properties and train a circuit-aware hybrid ranking model to recommend configurations for different quantum circuits. We evaluate the approach on multiple scalable circuit families using leave-one-family-out and size-based validation. The results show that offline optimization often improves over default settings, although the magnitude of the gain depends strongly on the backend, circuit family, and circuit scale. The learned predictor recovers a meaningful fraction of this gain, with better performance under size-based validation than under family-based transfer, but generally remains below the offline optimum.

We consider a general and practical scenario of quantum key distribution (QKD) over an unknown, stationary, unital qubit channel. Furthermore, due to practical limitations, e.g., relative movement and rotation of communicating parties, a global shared reference frame cannot be established. This scenario can routinely appear in satellite QKD. We propose two methods to overcome the physical qubit noise and the lack of shared reference frame. The first proposed approach involves constructing the Pauli transfer matrix (PTM) description of the channel, which we achieve without requiring a shared reference frame, by absorbing the lack of shared reference frame in the channel definition. This is followed by the identification of singular vectors of PTM as the Bloch vectors for optimal signal states. In the optimized local bases, the resulting correlations are equivalent, up to outcome relabeling, to those of a Pauli channel, allowing us to show the optimality of the BB84 and six-state QKD protocols under these conditions. The second approach, called the sequential basis matching (SBM) involves sequentially identifying the channel-optimized local bases that enable QKD. We show that both of these approaches result in the same effective key exchange rate for QKD.

These notes present a self-contained introduction to Wess-Zumino (WZ) terms in quantum systems with $SU(N)$ symmetry, emphasizing the interplay between geometry, topology, and condensed-matter applications. We begin with the $SU(2)$ spin coherent-state path integral, where the Berry phase appears as a WZ term encoding the symplectic structure of the Bloch sphere. This example is then used to introduce the geometric origin of topological terms, their relation to integral cohomology classes, and the role of Berry curvature as the first Chern class of the canonical $U(1)$ bundle. We next discuss physical realizations in which such geometric terms affect dynamics, including adiabatic Berry phases and geometric quantum noise in magnetic quantum dots. A substantial part of the notes is devoted to the condensed-matter motivation for higher $SU(N)$ symmetries, covering $SU(N)$ Heisenberg models, $SU(4)$ spin-orbital and spin-pseudospin systems, multipolar exchange interactions, and higher-spin multipolar orders. Finally, we develop the 0+1-dimensional $SU(N)$ superspin coherent-state construction, identify the phase space with $CP^{N-1}$, and derive explicit local WZ terms for $SU(3)$ and $SU(4)$. The appendices provide algebraic dictionaries connecting the abstract superspin language with concrete physical embeddings, including multipolar generator bases and several useful $SU(4)$ parametrizations.

Semi-device-independent QRNG frameworks represent a particularly attractive approach, combining strong security guarantees with high randomness generation rates while relying only on reduced and practical physical assumptions. A recently proposed approach based on photon-number constraints is particularly suited to photonic implementations, where these assumptions can be easily assessed experimentally. Here, we experimentally demonstrate a quantum random number generator within this framework, enabling the direct computation of lower bounds on the certifiable Shannon entropy via semidefinite relaxation techniques. When combined with entropy accumulation methods, this approach enables finite-size randomness certification without assuming independent and identically distributed rounds. We realize the protocol using a four-state coherent-state constellation symmetrically distributed in phase space and measured by heterodyne detection, certifying 0.223 bit per measurement, which is the highest value reported to date for a continuous-variable semi-device-independent QRNG. The implementation combines a low-loss integrated photonic heterodyne receiver with a simple transmitter assembled from commercial components, providing a practical and high-speed architecture for semi-device-independent randomness generation.

We establish the first super-polynomial quantum advantage for the tolerant junta testing problem in the adaptive setting. Specifically, we show that within a certain parameter regime, tolerant $k$-junta testing with high precision can be solved using $\mathrm{poly}(k)$ quantum queries, whereas any classical algorithm requires at least $k^{\Omega(\log k)}$ queries. The problem of tolerant $k$-junta testing is as follows: given parameters $(k, \epsilon_1, \epsilon_2)$, with $0\le \epsilon_1<\epsilon_2 \le 1/2$, and black-box access to a Boolean function $f$ (defined on $n$ variables), distinguish whether $f$ is $\epsilon_1$-close to some $k$-junta or $\epsilon_2$-far from every $k$-junta. We show the quantum advantage for a range of parameters close to $1/2$, for example, $\epsilon_1 = 1/2-1/k$ and $\epsilon_2 = 1/2-1/(2k^2)$. The (non-adaptive) quantum tester we use was given by a recent work of Bao, Liu, Yao, Ye, and Zhang (SOSA 2026). We slightly adapt their analysis to show that it holds in the above parameter regime. On the other hand, our classical lower bound requires substantial new ideas. Inspired by the lower bound techniques of Chen and Patel (FOCS 2023), we introduce a new hard distribution of “yes” instances (i.e., instances with distance at most $\epsilon_1$ to $k$-juntas) that is based on planting an “approximate-junta” as follows: we randomly pick $k$ out of $n$ coordinates, and for each fixing of the $k$ coordinates, the $2^{n-k}$ values in the restricted subcube are drawn randomly except for the set of points in an error-correcting code on which we place the same random bit. We show that this distribution is much closer to $k$-juntas than the uniform distribution, but on the other hand, they are indistinguishable with respect to any classical algorithm making $k^{o(\log k)}$ queries.

We develop an operator-frame formulation of entropic uncertainty relations in the Hilbert-Schmidt space of operators. For general continuous indexed operator frames, we derive an entropic uncertainty relation for the associated coefficient distributions by combining endpoint norm estimates with Riesz-Thorin interpolation. We then identify a distinguished class of mutually unbiased operator frames, defined through constant-modulus trace overlaps. Under suitable structural conditions, the corresponding coefficient amplitudes are related by a bilinear Fourier transform, leading to a stronger Hirschman-Beckner-type entropic uncertainty relation. As canonical realizations, we consider Weyl displacement operators and Wigner kernels, as well as Cartesian dyadic frames generated by position and momentum eigenstates. These examples recover familiar continuous-variable Fourier dualities while extending entropic uncertainty relations beyond measurement outcomes to operator representations themselves.

We investigate the angular-time evolution – a parameter-time evolution generated by the entanglement Hamiltonian – for the bipartitioned ground state of the S=1 bilinear-biquadratic chain under the open boundary condition with the up edge spin. Using a matrix-product-state representation of the ground-state wavefunction, we calculate the angular-time spin correlation functions $\langle S_n^{\alpha }(\tau )S_{n'}^{\alpha }(0)\rangle$ in the Haldane phase, and extract its dominant oscillation mode attributed to the nearly two-fold-degenerate entanglement spectrum associated with the $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry. We also compute the effective edge-spin dynamics under a uniform magnetic field applied to the system part and numerically verify its correspondence to the dominant angular-time mode by precisely comparing the subsystem-size dependence of their amplitudes.

This paper starts with the transition from classical physics to quantum mechanics which was greatly aided by the concept of phase space. The role of canonical transformations in quantum mechanics is addressed. The Wigner phase-space distribution function is then defined which arises from the formulation of the density matrix, followed by the harmonic oscillator in phase space. Coherent and one- and two-mode squeezed states of light as well as the squeezed vacuum are discussed in the phase-space picture. Attention is also drawn to the fact that squeezed states naturally generate entanglement between the two-modes. Coupled harmonic oscillators are also elucidated in connection with the Wigner phase space. It will be noted that the phase-space picture of quantum mechanics has become an important scientific language for the rapidly expanding field of quantum optics. Here, we mainly focus on the simplest form of the Wigner function, which finds application in many branches of quantum mechanics. We make use of several symmetry groups such as Lorentz groups, the symplectic group in two and four dimensions, and the Euclidean group. The decoherence problem of an optical field is examined through a reformulation of the Poincaré sphere as a further illustration of the density matrix.

Quantum secret sharing is one of the core security technologies in the field of quantum communication. Aiming at the problems of high quantum resource consumption and the difficulty in balancing security and efficiency existing in current quantum secret sharing schemes under non-maximal quantum access structure(QAS), this paper focuses on the judgment criteria of non-maximal QAS and the construction methods of efficient and perfect quantum secret sharing schemes on a kind of non-maximal QAS, i.e. hyperstar with three hyperedges. Firstly, we characterizes the forbidden sets and intermediate sets within this non-maximal QAS, and provides a characterization for non-maximal QAS realizable by pure-state encoding. Secondly, the representative element QAS of hyperstar with three hyperedges is defined, which serves as a substructure of this hyperstar QAS, and belongs to non-maximal QAS. Furthermore, we propose a universal, efficient, and perfect quantum secret sharing scheme based on this hyperstar QAS using corresponding classical secret sharing scheme as an auxiliary. By deploying lightweight quantum resources to representative element access structures, the proposed scheme reduces the difficulty in quantum state preparation and distribution, and enhances its security and resource utilization efficiency.

Quantum computers relying on cold atoms are being built and promise a high flexibility in the way information in encoded into the physical system. In particular, the analog mode is spiking interest in the field of optimization as a classically intractable number of configurations can be tackled. In this work, we investigate a problem that requires every-day scheduling of critical tasks involving a large number of actors. Namely, fixing the planning for a Earth Observation satellite fleet composed of several of units exposed to a high density of targets to be scanned. We explore numerical schemes that convert the formulated problem into a cold-atoms friendly setup. We begin by a naive formulation of the Satellite Mission Planning problem without taking account for the agility of the satellites. We then extend the problem to take it into account based on the literature. By formulating the planning problem as a Maximum Independent Set problem, we are able to solve the problem with a QPU based on Rydberg atoms. We explore two ways of solving the MIS problem on the QPU, one relying on the graphs and on the Quadratic Unconstrained Binary Optimization Framework (QUBO). We show that the QUBO methodology is the most relevant and explore it more deeply with numerical experiments. We conclude on the potential utility of using a QPU to solve the Satellite Mission Planning problem in an operational context.

BB84 quantum key distribution derives its security from a physical guarantee that any eavesdropper disturbs the channel in a statistically detectable way. Prior work evaluates this by assuming Eve attacks at a fixed, analytically optimized rate. We examine what happens when Eve is modeled instead as a learning agent. Classical reinforcement learning is used, specifically tabular Q-Learning, SARSA, and Double Q-Learning, to adaptive BB84 eavesdropping. This formulates the attacker's decision as a Markov Decision Process where the agent observes Quantum Bit Error Rate (QBER) feedback and decides, qubit by qubit, whether to intercept or pass. Experiments span three channel noise levels ($\mu_{ch}\in\{1\%,3\%,5\%\}$) and are validated across five independent random seeds (45 training runs per condition, 10,000 episodes each). Against the best non-adaptive analytical baseline, Q-Learning reduces detection from $99.4\%$ to $0.28\%\pm0.27\%$ at $\mu_{ch}=1\%$ while extracting approximately 10.5 correct bits per episode. This is a 355-fold reduction that is statistically significant ($p=0.020$, Mann-Whitney $U$ test). We also report the spontaneous emergence of an end-game burst, where agents independently learn to surge their attack rate at the final block. This exploit vanishes under randomized checkpoint intervals while stealth performance remains statistically indistinguishable. These results motivate the inclusion of adaptive adversary baselines in quantum cryptographic security evaluations.

Chaos is central to secure communication and physical random-number generation. Conventional cavity-optomechanical implementations, however, usually rely on weak single-photon optomechanical coupling and low-frequency mechanical modes, so access to deterministic chaotic dynamics often requires large driving power and careful suppression of thermal noise. Here we theoretically study a hybrid molecular optomechanical system formed by coupling a plasmonic nanocavity to a whispering-gallery-mode (WGM) microcavity. The plasmonic nanocavity provides terahertz-scale single-photon optomechanical coupling to a molecular vibration, while the WGM resonator offers a low-loss photonic channel that mitigates the short plasmon lifetime. By integrating the semiclassical equations of motion and evaluating the largest Lyapunov exponent, we map the nonlinear dynamical regimes in the parameter spaces of WGM detuning, plasmon–WGM coupling, and plasmon–vibration optomechanical coupling. We show that increasing the plasmon–vibration coupling drives the system from self-sustained oscillations to chaos through a period-doubling cascade. At moderate coupling strengths, isolated chaos windows can be opened or closed by tuning the WGM detuning and the inter-cavity coupling. These results identify molecular optomechanics as a controllable room-temperature platform for on-chip chaotic light generation and random-signal applications.

Drawing on advances in superconducting qubit control schemes that unlock enriched native gate sets at the hardware level, we systematically examine how harnessing this enlarged physical two-qubit gate pool—specifically CNOT and CXSWAP—streamlines syndrome extraction for certain qLDPC codes with nonlocal stabilizers. Through an exhaustive search, we discover a set of qLDPC codes with various stabilizer weights and distances that can be implemented on the two-dimensional nearest-neighbor qubit connectivity native to superconducting hardware while achieving performance equivalent to that of the direct CNOT implementation requiring long-range interactions. We refer to those codes as Bunny codes. Across all code distances we examine, the best Bunny codes with weight-6 stabilizers in periodic boundary conditions have a code rate approximately $3\times$ that of the toric code; when converted to open boundary conditions, they retain an approximately $2\times$ code rate advantage over the rotated surface code. In circuit-level simulation, we find that some Bunny codes exhibit logical error rates an order of magnitude lower than toric codes with comparable code rates. Our results demonstrate that high-performance quantum error correction can be achieved using an expanded gate set rather than long-range couplers, thereby significantly reducing hardware complexity.

The Learning With Errors (LWE) problem constitutes the mathematical foundation of modern Post-Quantum Cryptography (PQC). Cryptanalysis of LWE ranges from classical lattice reduction to machine learning and quantum-classical hybrids. We propose CIM-BDD, a hybrid Bounded-Distance-Decoding solver that reduces LWE to a Quadratic Unconstrained Binary Optimization (QUBO) problem through a strictly penalty-free mapping. An algebraic elimination of the secret embeds LWE into a $q$-ary lattice, absorbing the modular arithmetic and recasting the problem as a Closest Vector Problem (CVP). The squared error norm is then used directly as the QUBO energy, so the cryptographic noise is the objective to be minimized rather than a penalized constraint. To realize this general model on current Noisy Intermediate-Scale Quantum (NISQ) devices, we design a special encoding method: a Continuous Relaxed Babai's Nearest Plane (CR-BNP) projection drives an adaptive mixed-radix encoder that greatly reduces both the qubit count and the QUBO coefficient range, so that a single batched hardware submission suffices. We further derive a statistically bounded early-stopping threshold ($T_{\text{early}}$) that acts as a one-sided certificate and doubles as a Decision-LWE distinguisher. We validate the framework on the TU Darmstadt LWE Challenge, giving an end-to-end demonstration for both Search- and Decision-LWE of a $40$-dimensional instance on the Coherent Ising Machine CPQC-550. This work establishes a new algorithm-hardware co-design paradigm for quantum-classical hybrid cryptanalysis.