Papers

Imaginary-time evolution (ITE) is a widely used technique for preparing the ground state of a target Hamiltonian. Building on the recently proposed framework of Double-Bracket quantum ITE (DB-QITE), we introduce a probabilistic variant (PDBQITE) that achieves an exponential reduction in circuit depth. Our algorithm requires only a single ancilla qubit and a quantum circuit that consists of one controlled real-time evolution and one mid-circuit measurement per iteration. The trade-off is an exponential decay of the total success probability with the number of iterations, for which we derive an analytical lower bound that is independent of system size. Our numerical simulations show that the per-step success probability remains close to unity, making it realistic to execute many iterations, thus providing a substantial improvement over prior constructions. We show that PDBQITE outperforms the near-term probabilistic algorithm PITE for ground-state preparation of molecular Hamiltonians, and that a hybrid QAOA-PDBQITE scheme achieves higher approximation ratios than QAOA at the same circuit depth on random regular graphs for the MaxCut problem.

This paper gives quantum query lower bounds for two relational graph problems, triangle listing and explicit multiplicative spanner construction, in the general graph query model, where quantum adjacency, degree and neighborhood queries are all available in arbitrary superposition. Both results are obtained by reductions via intermediate multi-block search problems. We extend the quantum query recording framework by Zhandry (CRYPTO 2019) and Hamoudi and Magniez (ToCT 2023) to handle a recording architecture for bidirectional oracles and give a generic blockwise soundness framework, which for arbitrary families of local accepting projectors, gives an exact operator-norm characterization of their maximum overlap with the subspace of bounded weight records. Using the intermediate search problems, we exhibit a family of $n$-vertex graphs with $\Theta(n)$ triangles on which listing any constant fraction of the triangles requires $\Omega(n^{3/2-o(1)})$ quantum queries. This is the first nontrivial quantum lower bound for triangle listing, a question raised by Jiang and Peng (ICML 2026). Further, we show that, for every fixed $k\ge 7$, constructing a multiplicative $k$-spanner requires $\Omega(n^{1+\frac{1}{2\mu_k}})$ quantum queries, where $\mu_k=k/3+O(1)$. For $k\in\{7,8\}$ the bound is $\Omega(n^{5/4})$, which matches what would be implied by an unproven instance of the Erdős girth conjecture, and for large $k$ the exponent $1+\frac{3}{2k}$ exceeds the $1+\frac{4}{3k}$ implied by the provable high girth dense graphs due to Lazebnik, Ustimenko and Woldar, 1995.

We demonstrate proximity-induced localization and re-entrant delocalization in a detuned quasiperiodic ladder. In the weak-quasiperiodic regime, where uncoupled Aubry-André chains are extended, a staggered detuning applied to only one leg induces localization in the neighboring leg through inter-leg hybridization. With further increasing detuning, the pure chain re-enters a delocalized regime, producing a delocalized-localized-delocalized sequence without directly modifying its quasiperiodic potential. We identify these regimes using static localization diagnostics and demonstrate experimentally accessible dynamical signatures on IBM Quantum hardware: strongly suppressed wavefunction spreading in the proximity-localized regime and its re-entrant recovery at larger detuning. These results establish leg-selective detuning as a programmable control knob for inducing and reversing localization in coupled quasiperiodic systems.

We theoretically investigate quantum correlations and energy storage in a single-electron silicon double quantum dot (eDQD) coupled to a single-mode microwave cavity. The charge and spin degrees of freedom are hybridized by Rashba spin-orbit coupling (SOC), while the cavity interacts with the eDQD through spin-photon and charge-photon couplings. Using the reduced thermal density matrix, we characterize spin-charge entanglement by concurrence and quantum coherence by the $l_1$ norm. At low temperature, the cavity strongly modifies the spin-charge correlations, producing distinct regimes of enhanced and suppressed entanglement. We identify a nonlinear crossover between an eDQD-dominated regime and a photon-dressed regime. This crossover is revealed independently by two signatures: a rapid change in the concurrence and the onset of finite photon occupation in the cavity. Its boundary exhibits a dominant $G_{\rm c}\propto\sqrt{\Omega}$ dependence and is robust against cavity Hilbert-space truncation. Beyond the Rashba coupling studied in [Ferreira et al., Phys. Rev. A 107, 052408 (2023)], we show that the cavity provides an additional tunable means of controlling quantum correlations and energy storage. We further characterize the eDQD as a quantum battery under coherent charging and evaluate its stored energy and ergotropy. The ergotropy varies markedly across a cavity-dressing crossover resembling that identified from entanglement, connecting cavity-induced modification of the dressed states to extractable work. Optimization over the charge-photon coupling reveals parameter-dependent optimal charging regimes and enhanced maximum ergotropy with increasing Rashba coupling. Our results establish cavity coupling and SOC as complementary controls of quantum correlations and extractable energy in semiconductor eDQD-cavity systems.

This paper develops a Quantum optimisation Unit Commitment (UC) framework that benefits from Pauli Correlation Encoding (PCE) as a qubit reduction preprocessing technique and Sample-based Quantum Diagonalisation (SQD) as a hybrid post-processing refinement method. The UC problem is formulated in binary form, then transformed into a QUBO problem and mapped onto an Ising representation suitable for adiabatic quantum evolution within the Qibo framework. This study analyses how PCE and SQD techniques affect qubit scalability, computational effort, feasibility, and probability of recovering the optimal solution as the number of generators increases. The results show that PCE reduces the effective size of the encoded problem, SQD improves the concentration of probability on feasible and optimal solutions, and their combined application provides the best overall behaviour in several scalable case studies, achieving more reliable solutions with lower effective computational cost than the unreduced formulation. The repository is available open source in Github (link is not yet available until work is published. However, if requested by the reviewers, we can make it open for a scific time window, if requested).

Solomonoff's universal inductive inference [Inf. Control. 1964] is one of the most general frameworks for learning from sequential data and predicting future observations. It provides prediction guarantees for any computable stochastic source. We ask whether an analogous universal theory of induction can be developed for quantum systems. To this end, we introduce universal quantum inductive inference, a framework for learning and predicting quantum sources that may exhibit arbitrary correlations and entanglement across time. Given the outcomes of past measurements and the corresponding post-measurement quantum systems, the learner produces a joint state that predicts the next measurement outcome and post-measurement system while preserving correlations with the past systems. We model a quantum source as a multipartite quantum state whose description is generated by an unknown $s$-bit program within a given time bound. The learner is required to produce a state that is $\epsilon$-close in trace distance to the target with probability at least $1-\delta$. We establish an information-theoretic inference algorithm with round complexity $O(s\epsilon^{-2}\delta^{-1})$. We further prove a matching lower bound in the relevant parameter regime, even for classical sources. As a second result, we construct a non-i.i.d. state tomography algorithm that outputs a classical description of the conditional state of the next system given past measurement outcomes. Our information-theoretic tomography algorithm achieves round complexity $O(s\min\{2^s,2^n\}\epsilon^{-2}\delta^{-1})$, where $n$ is the number of qubits received in each round. Finally, we investigate the computational hardness of universal quantum inductive inference under quantum cryptographic assumptions.

Grover's algorithm achieves a quadratic speedup for unstructured search when a global oracle for the target set is available. However, its success probability is generally below $100\%$. To overcome this issue, Long presented a modified version of Grover's search algorithm by introducing a phase-matching condition, which can search for the target set with zero theoretical failure rate. Nevertheless, in many applications, the target set is determined by the intersection of multiple constrained sets. Most existing Grover-based methods are limited to the intersection of only two sets and are not always successful in searching for the target set. In this work, based on Long's method, we propose an algorithm for exactly searching the intersection of multiple sets. We derive and prove a phase-matching condition for searching the intersection exactly. Furthermore, we present a quantum circuit that implements exact intersection search. Finally, we support our theoretical results with numerical simulations.

Quantum steering captures the ability of one party, through local measurements on a shared entangled state, to affect the conditional states held by distant parties in a way that admits no local explanation. Its device-independent (DI) quantification – requiring no characterization of any state or measurement – has been developed in the bipartite setting, notably through the framework of assemblage moment matrices (AMMs) [Phys. Rev. Lett. 116, 240401 (2016)], but has remained largely unexplored beyond it. Here, we extend the AMM framework to tripartite steering, covering both the scenario in which two parties jointly steer a third one (2-steer-1) and that in which a single party steers the remaining two (1-steer-2). For each scheme, we construct a semidefinite program that, from the observed correlations – or merely from the violation of a Bell inequality – yields lower bounds on the steering robustness of the underlying assemblage. As a by-product, our bounds also certify, in a DI manner, the incompatibility of the measurements performed by the steering parties.

Modeling the response of driven many-body quantum systems from input–output data is difficult: the dynamics are nonlinear, history dependent, and expensive to simulate as system size grows. A paradigmatic case is High-Harmonic Generation~(HHG), where a strong field drives a medium to emit radiation that is highly sensitive to the drive and encodes long-range temporal correlations. We introduce a dissipative quantum reservoir computing~(DQRC) framework that builds a digital twin of such a system, learning its input–output map directly from data while the reservoir—itself a small open quantum system—stays fixed and only a classical readout is trained. We show that a minimal single-qubit reservoir reproduces the HHG response of a substantially larger Ising spin chain, and on a representative benchmark matches and on several metrics surpasses previously reported temporal convolutional and Kolmogorov–Arnold-network models, while using a simpler, physically realizable system. A single fixed reservoir further generalizes across a broad range of drives, indicating that it learns a shared physical response structure rather than memorizing trajectories. These results establish dissipative quantum reservoirs as compact, physically grounded digital twins for nonlinear, memory-dependent quantum dynamics. Code is available at \href{https://github.com/AI-and-Quantum-Computing/DQuRC}{https://github.com/AI-and-Quantum-Computing/DQuRC}.

The Holevo Cramér-Rao bound sets the asymptotically attainable precision of quantum multiparameter estimation, but its attainability with finite quantum state copies remains unresolved in general. While a gap between the single-copy optimum and the Holevo bound persists at any finite copy number for two-parameter estimation models, the higher-parameter case has remained open. Here we disprove universal finite-copy gap persistence beyond two parameters by constructing a three-parameter rank-two model whose Holevo bound is unattainable with one copy but attained exactly with two. We further prove finite-copy persistence for full-rank models with any number of parameters. We then experimentally implement the two-copy collective measurement for the constructed model using superconducting qubits. We observe a two-copy estimation error below the single-copy and separate-measurement bounds determined from state tomography. By introducing controlled depolarization, we show that this precision advantage persists over a finite noise range. Our results connect the attainability of ultimate quantum precision to the structure of the multiparameter estimation model and the resources available for collective measurements.

Digital quantum simulation of the quantum Rabi model in the deep strong coupling regime motivates the need for an exact characterization of Trotterization errors, so far known only through qualitative bounds. Here we derive such a characterization in the exactly solvable degenerate limit, valid for arbitrary coupling strength. The first-order Trotter operator admits a closed expression, showing that the generated phase-space trajectory lies exactly on a circle that is rescaled and rigidly rotated with respect to the exact one, rather than merely approximating it. This geometric renormalization yields a closed form for the state fidelity, structurally identical to the physical survival probability governing revivals in the deep strong coupling regime: both are the same geometric question – the distinguishability of a displaced Fock state – applied to two different pairs of points in phase space. We further show that this geometric error is exactly correctable by promoting the Trotter-step coupling to a complex amplitude, at no additional circuit cost. These results, obtained in the exactly solvable degenerate limit, provide a benchmark for Trotterization errors in the general Rabi model.

We show that the measurement–feedback interval alone can induce ordering in a quantum Boltzmann sampler with fixed programmed couplings and a fixed local dissipative rule. At zero environmental dephasing, repeated projective measurements remove transverse coherence regenerated during relaxation, thereby enhancing the local longitudinal response. We derive the exact finite-interval Markov kernel for successive measurement records and obtain the ordering threshold in the Curie–Weiss limit. For couplings below the equilibrium Curie–Weiss threshold, shortening the readout interval drives a continuous transition to an ordered stationary state even though the programmed Boltzmann distribution remains disordered. The stationary response and equation of state map exactly onto those of a continuously interacting model with dephasing, while the linear relaxation rates remain distinct at matched static response. Exact finite-size stationary calculations reveal the transition through bimodal magnetization distributions and mean-field critical scaling.

We examine the spectrum and quantum states of small lattices with cylindrical and toroidal topology subject to a scalar gauge potential that introduces a position dependent phase in the inter-site coupling. Equivalency of gauges assumed in infinite lattices is generally lost due to the periodic boundary conditions, and conditions that restore it are identified. We trace the impact of various system parameters including gauge choice, boundary conditions and inter-site coupling strengths, and an additional axial field. We find gauge dependent appearance of avoided crossings and persistent degeneracies, and we show their impact on the associated eigenstates. Smaller lattices develop prominent gaps in spectral lines associated with edge states, which are suppressed in the thermodynamic limit. Toroidal lattices have counterparts of most of the features observed in cylindrical lattices, but notably they display a transition from localization to delocalization determined by the relation between the field parameter and the number of lattice sites.

Global variational quantum algorithm (VQA) frameworks for solving partial differential equations (PDEs) often rely on a single expressive ansatz over a uniform grid, which becomes structurally inefficient when solutions exhibit spatially heterogeneous complexity such as localized singularities or thin boundary layers. A localized nonsmooth feature can degrade the convergence of the entire global quantum representation, imposing unnecessary circuit depth and amplifying the risk of vanishing gradients in barren plateaus. To overcome this limitation, we propose a block-wise VQA framework for PDEs characterized by spatially heterogeneous solution complexity. The computational domain is decomposed into locally represented quantum subproblems, where grid budgets and ansatz families are dynamically assigned based on a computable difficulty indicator. Artificial block interfaces are coupled through unified jump penalties for state values, derivatives, and physical fluxes, while adaptive reblocking tracks moving rough regions to maintain local accuracy without excessive global qubit overhead. The error analysis rigorously separates spatial discretization, ansatz expressivity, optimization convergence, finite-shot sampling noise, transfer errors from reblocking, and interface-coupling contributions. Reproducible residual-based simulations on representative elliptic, advection–diffusion, and Burgers problems demonstrate lower approximation errors and reduced peak local circuit width compared to global VQA approaches. These results substantiate block-wise quantum resource localization, confirming that high-fidelity PDE solutions can be achieved on near-term quantum devices without requiring a single globally expressive ansatz.

Accurate evaluation of $\pi$–$\pi$ stacking interactions requires a description of electron correlation on a small energy scale. Direct variational quantum calculations of the benzene dimer with CAS(28e,20o) require 40 qubits and deep circuits. Here, we propose Divide-and-Conquer Quantum-Selected Configuration Interaction (Deep QSCI), combining the divide-and-conquer idea of Deep VQE with QSCI, and apply it to the sandwich benzene dimer. We perform QSCI for one monomer and construct a reduced local basis by applying particle-number-conserving one-electron excitations to its ground state. Using this basis and the intermonomer interaction Hamiltonian, we build and diagonalize an effective Hamiltonian of the dimer. Since the monomers have the same structure, the QSCI result is reused for both monomers and across intermolecular distances, reducing the required quantum register from 40 to 20 qubits for the monomer CAS(14e,10o). With 6-31G**, the model gives an attractive minimum of -0.91 kcal/mol at 4.0 $\r{A}$, compared with the counterpoise-corrected CCSD(T) value of -1.14 kcal/mol. With cc-pVDZ, a large cancellation appears between the product-state interaction energy and the energy lowering by diagonalization. This may reflect insufficient consistency of the active orbitals and reduced local bases between monomers. Orbital consistency, reduced-space convergence across basis sets, and charge transfer remain key issues for quantitative accuracy. Deep QSCI thus provides a resource-efficient approach to constructing model interaction curves without repeating monomer quantum calculations at each separation.

Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era and have attracted increasing attention in quantum machine learning. However, most existing QGAN methods rely on patch-based decomposition strategies, which weaken the global consistency of generated images and increase quantum resource overhead. In this work, we investigate a simpler approach: pixel-level, end-to-end image generation using a single-quantum-circuit QGAN. By analyzing the structural matching relationship between the quantum prior and the target data distribution in Hilbert space, we provide a new theoretical perspective for understanding the training behavior of naive end-to-end QGANs. Specifically, we introduce the Quantum Fidelity Landscape (QFL), defined as the pairwise-fidelity structure induced by an ensemble of quantum states and preserved under shared unitary transformations of the quantum generation process. We show that, under a fixed Lipschitz readout, this invariant imposes a one-sided bound on decoded sample separation, motivating calibration of the prior-induced QFL before adversarial training. To validate this theoretical insight, we propose BasicQGAN, a QGAN framework incorporating quantum prior calibration. Before adversarial optimization, BasicQGAN aligns the prior-induced QFL with the data-induced QFL. Experimental results on small-scale grayscale image datasets show that BasicQGAN achieves stable and effective end-to-end pixel-level image generation while requiring fewer qubits and trainable parameters than representative patch-based quantum generators. Furthermore, experiments with different initial quantum-state ensembles show that QFL-calibrated ensembles achieve better generative performance.

Hamiltonian simulation is a central application of quantum computing. Randomized Hamiltonian simulation approximates the target dynamics by sampling quantum circuits and often allows simpler circuit implementations. We develop a framework for randomized Hamiltonian simulation in which Hamiltonian terms are sampled with arbitrary probabilities and the corresponding short-time evolutions are implemented sequentially. The leading channel error relative to ideal time evolution is governed by the variance of the sampled generators. Minimizing the variance-based upper bound on the worst-case error recovers qDRIFT, a leading randomized Hamiltonian simulation algorithm that samples terms in proportion to their operator norms. For typical input states, however, this sampling distribution need not be optimal. Minimizing Haar-averaged error bounds instead yields sampling probabilities proportional to the Hilbert-Schmidt norms of the terms. This choice can provide smaller average-error bounds than conventional qDRIFT. For Hamiltonians expressed as sums of Pauli strings, we combine this framework with commuting Pauli grouping to obtain smaller error bounds than conventional qDRIFT at fixed $R_z$ depth. Numerical benchmarks for the Sachdev-Ye-Kitaev model show error-bound improvement factors consistent with linear scaling in the number of qubits. For the 108-qubit FeMoco Hamiltonian, the error bounds are reduced by a factor of about 5.65. These results establish average-error optimization as a practical design principle for randomized Hamiltonian simulation.

An efficient single-photon nonlinearity is a key resource for photonic quantum technologies. Single-photon nonlinearities have been demonstrated with quantum dots (QDs) across a range of cavity and waveguide geometries. However, none of these realizations simultaneously provide high-efficiency direct fiber coupling and compatibility with scalable on-chip integration. The nanobeam cavity addresses these limitations through its in-plane geometry, which enables highly efficient, direct fiber coupling alongside scalable homogeneous and heterogeneous on-chip integration. Here, we report a single-photon nonlinearity from a nanobeam cavity coupled to a single quantum dot. The nanobeam interfaces directly with a single-mode fiber with an efficiency of 60%. Driven by resonant picosecond pulses, the platform achieves a single-photon nonlinearity with a low threshold of 0.24 incident photons per pulse. The system operates in the strong-coupling regime, exhibiting a coupling rate of g/2{\pi} = 29.1 GHz and a cooperativity of C = 3.98. These results establish the nanobeam cavity-QD system as a compact, chip-integrable, and fiber-compatible building block for scalable photonic quantum technologies.

Due to their tight temporal coupling, entangled photon pairs produced by spontaneous parametric down-conversion offer a new route to improved network synchronization among quantum-enabled devices. In the emerging space-based quantum internet, such synchronization will be pivotal to operational success. Here, we propose and experimentally demonstrate a new polarization-assisted protocol that allows quantum-enabled devices to remain synchronized under the high loss conditions anticipated for satellite-to-ground quantum channels. Our experimental results show that polarization assistance extends reliable synchronization acquisition to higher channel losses and an increase of up to 40$\%$ in satellite-to-ground distance. Supporting Monte Carlo simulations show that this performance improvement extends over a wide range of photon-starved conditions. Collectively, these results not only provide a proof of concept for polarization-assisted network synchronization but also demonstrate its pragmatic importance in significantly extending the range under which quantum-enabled devices forming the backbone of the quantum internet can remain highly synchronized.

Passive transformations of an $n$-mode squeezed probe become locally unidentifiable when the quantum Fisher matrix loses rank. For pure zero-mean Gaussian probes, we show that the $C_{n}$ restricted-root system of $\mathrm{Sp}(2n,\mathbb{R})/\mathrm{U}(n)$ governs this exact sloppiness. For squeezing magnitudes $r_{1},\ldots,r_{n}$ in the canonical basis, the roots $2r_{j}$ and $r_{j}\pm r_{k}$ label the local phase rotations and two beam-splitter quadratures, and their vanishing identifies every Fisher-null direction. The associated Fisher weights quantify the approach to each singular wall. At fixed positive mean photon number, we solve the E-optimal probe-design problem of maximizing the smallest passive Fisher eigenvalue in two explicit generator normalizations. In the canonical phase and beam-splitter angle convention, the unique optimum in the fundamental Weyl chamber is an arithmetic progression approaching the consecutive-odd-integer dual-Weyl direction at large resource. With an invariant generator norm, the smallest eigenvalue is independent of the passive frame and the optimum lies exactly on that ray at every resource. Uniform squeezing instead minimizes the local-phase A-optimal cost while leaving one beam-splitter quadrature unidentifiable for every mode pair. Finally, the two quadratures of each identifiable mode pair saturate the quantum-geometric incompatibility bound, and the largest compatible passive submodel has $n(n+1)/2$ parameters at a regular spectrum. The resulting classification separates local identifiability, sensitivity optimization, and simultaneous attainability.

A layer of diagonal quantum gates can be specified by an integer-valued function on computational basis states. This description suggests implementing the layer by evaluating that function reversibly and translating its value into a phase using a shared quantum register. We develop this viewpoint for parallel phase kickback (PPK): the weighted sum of several rotation parameters is computed coherently, added once to a phase-gradient state, and then uncomputed. The construction separates the cost of representing a phase function from the cost of applying it. We prove correctness, give explicit error bounds, and analyze logical gate counts for general weights, pairs of rotations, and weights with disjoint binary support. With Clifford-only encoding, a batch uses at most $4(b-1)$ T gates at $b$-bit phase resolution, excluding initialization. The first phase reference is prepared with independent rotations synthesized by Gridsynth; additional phase-gradient states then follow from the disjoint-support construction with $O(b)$ T gates each. The resulting bounds identify when shared phase arithmetic reduces amortized rotation cost and when encoding overhead limits that advantage.

Quantum Fisher information (QFI) quantifies the sensitivity of a quantum state to a parameter change and plays a key role in quantum metrology. Meanwhile, quantum coherence is a crucial resource for quantum information processing. However, despite extensive studies on various aspects of quantum metrology, the interplay between the dynamics of the QFI and quantum coherence remains unexplored. Here, we explore the factorization relationship between the QFI and quantum coherence, both theoretically and experimentally. We prepare pure states in qubit and qutrit systems and apply unitary evolution to investigate this relationship. Our results confirm the factorization law, offering strong evidence for this relationship. Furthermore, we establish a lower bound on the variance of the estimated parameter that does not require the final state information. This approach reveals a deep connection between the QFI and quantum coherence, providing insights into quantum parameter estimation. These findings have potential applications in quantum sensing and quantum metrology.

We develop a generalized effective spin-chain (GESC) formalism for strongly interacting multicomponent anyons in a one-dimensional (1D) optical lattice. By mapping particle motion onto spinless fermions and spin states onto an ordered chain, this framework provides a spin–charge-separated perspective on the physical effects of fractional exchange statistics. In the strong-interaction regime, the leading-order charge Hamiltonian becomes independent of the statistical phase; instead, virtual tunneling through doubly occupied states directly imprints this phase on the spin-exchange coefficients. The GESC formalism captures ground-state properties of the full Anyon–Hubbard model reported in S.~Basak, X.-W.~Guan, and H.~Pu, unpublished manuscript (2026), and reveals that their statistical dependence is predominantly encoded in the explicit anyonic structure of the observables rather than the underlying spin ground state. This formalism uncovers that, out of equilibrium, successive spin exchanges accumulate direction-dependent phases; the resulting interference drives crossover from dispersive to localized impurity transport and generates inversion-asymmetric propagation at intermediate statistics. Suppressed expansion persists for identical and distinguishable impurities, with differences in propagation governed by the interplay between impurity identity and interaction anisotropy. Ultimately, GESC offers a versatile, computationally efficient theoretical framework that connects macroscopic dynamics to microscopic virtual exchange processes, conferring a unique spin–charge-separated vantage for resolving how fractional exchange statistics survives strong interactions and manifests distinctly across static and dynamical regimes.

We investigate the ground-state and dynamical properties of multicomponent interacting anyons confined in a one-dimensional (1D) optical lattice. Adopting the Anyon–Hubbard model, we explore spin and charge correlations as functions of interaction strength and the anyonic statistical parameter. Our results demonstrate that multicomponent effects combined with fractional exchange statistics substantially reshape both charge and spin correlations. For fractional exchange statistics, the canonical symmetric shell structure with prominent singularities in the spinor fermionic momentum distribution undergoes notable structural reconstruction, exhibiting emergent asymmetry, new spectral peaks, and broadened singular features. Unlike the Tonks–Girardeau bosonic gas, pseudobosons, representing a limiting case of anyons, cease to display a dominant zero-momentum peak. Instead, a quasi-fermionic shell structure emerges with finite-momentum peaks, whose locations are modulated by lattice site occupancy and interaction strength. The structure factor of spin correlations uncovers antiferromagnetic ordering in spinor anyon systems, with correlation magnitudes tunable by anyonic exchange statistics. Furthermore, quench dynamics analysis reveals the statistical phase parameter as an effective tuning knob. It enables the switching of dipole oscillations between underdamped and overdamped relaxation regimes and governs overall cloud expansion dynamics. Our findings pave the way for exploring statistics-driven ground-state and dynamical phase transitions in spinor anyon systems.

It is widely believed that there are no exact global symmetries in quantum gravity. We review recent works showing that global symmetries can nevertheless emerge after ensemble averages of theories, as encountered in holography. As a precision case study, we discuss the ensemble average of two-dimensional Narain-type conformal field theories associated with an even quadratic form of general signature, which is computed by the Siegel-Weil formula and interpreted as a sum over geometries in a three-dimensional Abelian Chern-Simons theory. Global symmetries of the bulk anyons can emerge after the average, as T-dualities relating different theories in the ensemble are "folded" into symmetries of a single theory (duality origami). We discuss the relation to the Swampland program, including the emergence of global symmetries at infinite distance. Finally, we recast strong-to-weak spontaneous symmetry breaking (SWSSB) of mixed states as an emergence of symmetries in ensemble averages; in holography, wormholes connecting the two sides of a thermofield-double construction provide its bulk realization. This motivates a new Swampland conjecture.

Leakage from a known encoding subspace can be detected by projection. However, the corresponding projector is unavailable when the encoding subspace is not classically specified. Here we show how independent quantum references prepared by the same encoder enable leakage detection without a classical description of the encoding. A coherent measurement on the references and a single message leaves every state within the two-dimensional encoding subspace, including its entanglement with a remote system, exactly unchanged. We derive the exact detection law for $M$ ideal references and prove optimality among tests with zero false alarm for every encoding. For orthogonal leakage, the miss probability is asymptotic to $4/M$, independent of the ambient dimension $d$. Measuring all references first, even collectively, gives zero detection at every finite budget under the same zero-false-alarm requirement. For a fixed detection target between zero and one, the optimal measurement-first cost is $\Theta(d/\epsilon)$ at sufficiently small tolerance $\epsilon$ on normal false alarm and conditional disturbance; a dimension-independent coherent budget suffices. For one logical qubit encoded in three physical qubits, seven coherent references achieve at least $50\%$ orthogonal-leakage detection, whereas any measurement-first receiver needs at least $588$ under the same $1\%$ normal tolerances. Quantum references thus support leakage checks without classical reconstruction, with a sample advantage exponential in the number of physical qubits.

We construct a supersymmetric factorization for generalized driven anisotropic Rabi–Stark Hamiltonians using an affine factor $\hat{A}=\hat{C}+\hat{a}\hat{D}$ in an inhomogeneous orthosymplectic superalgebra, with two real invertible spin matrices $\hat{C}$ and $\hat{D}$. The six real pencil parameters constrain nine Hamiltonian frequencies and one energy offset up to an overall frequency scale. The normal product $\hat{A}^{\dagger}\hat{A}$ has an exact zero-energy ground doublet and, with its antinormal partner $\hat{A}\hat{A}^{\dagger}$, gives a Witten index of two. For distinct pencil roots with finite baseline slopes, coherent gauges define a Bargmann hierarchy and endpoint conditions for exceptional finite-polynomial states. The spin grading organizes four limiting cases, comprising two driven-oscillator families with every baseline exact and the constrained anisotropic-Rabi and Rabi–Stark sheets, which meet at the degenerate-qubit isotropic Rabi model. An aligned interior example adds direct and longitudinal drives, Stark deformation, and intensity-dependent coupling while retaining the factorization.

We compute the evanescent-wave Johnson noise (EWJN) in the vacuum half-space above a superconductor, and the resulting relaxation time ($T_1$) of spin and charge qubits placed at nanometer distances from the surface. The electromagnetic response is described by a single microscopic transverse current-response kernel $Q(q, \omega)$ for a BCS superconductor. This is computed for varying densities of both non-magnetic impurities and magnetic impurities, for arbitrary frequency and temperature and for wave vectors $q \ll k_F$ (the Fermi wavevector). When combined with the fluctuation-dissipation theorem and the nonlocal surface impedances of the half-space, this yields the magnetic and electric field noise at any distance $z \gg k_F^{-1}$ from the surface, from which we obtain $T_1$. Just below $T_c$ the magnetic noise is enhanced relative to the normal state by the coherence (Hebel-Slichter-type) peak of the dissipative conductivity and drops exponentially at lower temperatures; the electric noise shows no coherence peak. The theory predicts that there is a zero-temperature noise floor induced by magnetic impurities. In the gapless regime produced by pair breaking, the finite subgap density of states $\nu(0)$ yields a temperature-independent noise spectral density and a relaxation rate bounded by $T_1^{-1}(T)\le[\nu(0)/\nu_F]^{2}\,T_{1,N}^{-1}(T)$ for $T\ll T_c$, with equality in the extreme nonlocal regime. Here $\nu(0)$ and $\nu_F$ are the superconducting and normal-state densities of states at the Fermi energy, and $T_{1,N}(T)$ is the relaxation time the same electrode would produce in its normal state at the same temperature.

Decomposing finite Abelian black-box groups into cyclic factors is a basic problem in quantum computation. We give a quantum algorithm for decomposing finite Abelian black-box groups by adapting the quantum sampling and classical lattice-reduction method of Regev's factoring algorithm. The algorithm computes an invariant-factor decomposition and corresponding cyclic generators with high probability. For a group G of order at most 2^n with unique encodings and reversible group-operation cost T_op = Omega(sqrt(n)), our algorithm uses O(sqrt(n)) quantum circuits, each with O~(n T_op) gates and executed at most O(n) times. For comparison, we consider the Cheung-Mosca algorithm, which decomposes the Sylow p-subgroups separately. We also consider the common-modulus implementation of the extended Cheung-Mosca algorithm. Our algorithm reduces the sum of the circuit gate counts from O~(n^2 T_op) to O~(n^(3/2) T_op). The total quantum time bound reduces from O~(n^3 T_op) to O~(n^(5/2) T_op), and the quantum space bound reduces from O(n^2) to O(n) qubits. The classical computation uses polynomially many bit operations and group-operation queries. These improvements rely on two technical ingredients. We prove that the integer relation lattices used in our algorithm admit, with high probability, integral bases whose vectors have Euclidean norm at most exp(O(sqrt(n))). We preserve the circuit-size advantage of Regev's algorithm for group decomposition by incorporating all O(n) sampled generators while keeping the lattice-reduction dimension at O(sqrt(n)).

Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the near term and the longer term has not been systematically discussed. This paper studies this question through two complementary experiments. First, we evaluate near-term quantum-kernel support vector machines (SVMs) on practical network-security classification tasks and compare them with classical SVM baselines on KDD Cup 1999, CICIDS2017, and BoT-IoT. Across these runs, quantum kernels are competitive. They can match or improve classical baselines in some settings, while classical RBF kernels remain stronger in others. This suggests that near-term quantum-kernel methods should be evaluated as practical, dataset-dependent alternatives to classical kernels rather than as uniformly superior replacements. Second, we use quantum oracle sketching (QOS) to study a longer-term memory advantage for classification with streaming classical samples. In QOS, samples are processed online and used to incrementally construct an approximate quantum oracle, which provides coherent query access for downstream quantum algorithms without retaining the entire dataset. Under the QOS-inspired machine-size estimate, comparable accuracy corresponds to a substantially smaller effective memory-size proxy than explicit sparse/QRAM-style storage. Compared with a simple streaming proxy, the result is more nuanced because aggressive feature filtering can make the streaming dimension small. This suggests that the long-term value of quantum computing for network-security classification may lie in memory-efficient data access rather than immediate runtime speedup. Together, these experiments show how quantum computing may contribute to network-security classification from near-term classification performance and longer-term memory efficiency.