Papers

We present a cross-cavity system in which steady-state superradiance is achieved using solely collective dissipative dynamics. Two cavities symmetrically couple an ensemble of four-level atoms by driving transitions between two electronic states and two motional states along perpendicular cavity axes. Both cavities operate in the bad-cavity regime: one cavity mediates collective atomic decay, while the other cavity, together with a coherent drive, mediates collective pumping via an off-resonant Raman transition. With this, we find steady-state superradiant states that possess nonclassical properties, such as super-Poissonian photon statistics. The system thus requires a beyond mean-field description, and so we develop an exact master equation simulation technique utilizing strong symmetries of the system's jump operators. Because superradiant decay is accompanied by a momentum impulse along the corresponding cavity axis, the system exhibits substantial hybrid entanglement between the atoms' spin and motional degrees of freedom at steady state. We also demonstrate that heralded measurements of the two cavity outputs prepare a state with significant particle-particle entanglement with prospects for quantum-enhanced acceleration sensing.

We report on the fabrication and characterization of superconducting coplanar waveguide (CPW) resonators based on titanium nitride (TiN) thin films deposited on 200\,mm diameter high-resistivity Si(100) substrates. We systematically investigate how deposition conditions, dry-etch power and in-situ resist strip temperature affect morphology, superconducting properties and dielectric losses. By tuning reactive sputtering conditions, three distinct preferred out-of-plane crystal orientations - (111), (200), and a mix of both are achieved. Our results demonstrate that all films exhibit similar minimal two-level system (TLS) losses, with TiN111 exhibiting the lowest median TLS losses $\tilde{\delta}_\mathrm{TLS}$, and greater robustness against reoxidation. The applied structuring process, in contrast, has a far greater influence on the TLS loss than the crystal orientation of the TiN film and, consequently, the intrinsic material properties of the superconducting layer. The lowest TLS losses for all TiN depositions were achieved with a low power etch and low temperature resist strip. An additional buffered oxide etch (BOE) treatment could remove high-loss interfacial oxides at the metal-air (MA) and substrate-air (SA) interface and recover the etch-induced TLS losses. Consequently, TiN resonators exhibiting $\tilde{\delta}_\mathrm{TLS}$ values as low as $9.67 \times 10^{-7}$ were realized. The corresponding median low-power loss, $\tilde{\delta}_\mathrm{LP}$, amounts to $11.04 \times 10^{-7}$, which translates to an internal quality factor approaching one million. These findings highlight the critical role of process induced oxide formation at the MA and SA interfaces in limiting the performance of TiN resonators and provide a scalable, low-loss process compatible with industry-grade 200\,mm CMOS qubit fabrication workflows.

Beyond binary classification, learnability can become a logically fragile notion: in EMX, even the class of all finite subsets of $[0,1]$ is learnable in some models of ZFC and not in others. We argue the paradox is operational. The standard definitions quantify over arbitrary set-theoretic learners that implicitly assume non-operational resources (infinite precision, unphysical data access, and non-representable outputs). We introduce physics-aware learnability (PL), which defines the learnability relative to an explicit access model – a family of admissible physical protocols. Finite-precision coarse-graining reduces continuum EMX to a countable problem, via an exact pushforward/pullback reduction that preserves the EMX objective, making the independence example provably learnable with explicit $(\epsilon,\delta)$ sample complexity. For quantum data, admissible learners are exactly POVMs on $d$ copies, turning sample size into copy complexity and yielding Helstrom(-type) lower bounds. For finite no-signaling and quantum models, PL feasibility becomes linear or semidefinite and is therefore decidable.

We present an Ising-compatible framework for formal neural-network robustness verification under bounded input perturbations. For piecewise activations (both continuous piecewise-linear and discrete piecewise-constant), the Exact Logarithmic Piecewise Model (Log-PW) provides an exact, sound, and complete formulation with a state-optimal logarithmic encoding, reducing the binary variables per neuron from linear $O(\kappa)$ to information-theoretically minimal logarithmic complexity $O(\lceil \log_2 \kappa \rceil)$ via logarithmic SOS2 (Log-PWL) and SOS1 (Log-PWC) constraints. For general element-wise activations, the Asymptotic Step-Envelope Model (Step-Env) leverages Log-PWC to construct sound, refinable endpoint enclosures. An exact monotonic reduction based on Jordan decomposition further extends this framework to arbitrary non-monotonic activations. We further develop a hybrid Benders solver with output-sensitive iteration bounds, leveraging a Helly-based conflict compression theory to avoid generic exponential cut explosion. In particular, for shallow networks with fixed input dimension, this establishes an unconditionally polynomial Benders iteration complexity in network size. Interval pruning, certificate transfer for pruned networks, and layerwise classical–Ising partitioning further reduce spin requirements. Experiments demonstrate exact certification fidelity for piecewise-linear networks and near-reference accuracy for non-linear activations under compact spin budgets. On MNIST benchmarks, Log-PW halves binary decision spins over One-Hot formulations, while Helly-based cuts achieve up to a $13.41\times$ speedup and an $87.6\%$ reduction in master queries over standard baselines.

We develop a comprehensive axiomatic framework for quantum-inspired distance metrics on projective Hilbert spaces, providing a unified foundation that organizes and generalizes existing measures in quantum information theory. Starting from five fundamental axioms, projective invariance, unitary covariance, superposition sensitivity, entanglement awareness, and measurement contextuality, we show that any admissible distance depends solely on state overlap and establish the uniqueness of the Fubini-Study metric as the canonical geodesic distance. Our framework further yields a hierarchy of comparison results relating the Fubini-Study metric, Bures distance, Euclidean distance, measurement-based pseudometrics, and entanglement-sensitive distances. Key contributions include an entanglement-geometry complementarity principle, high-dimensional concentration bounds, and operational interpretations connecting distances to state discrimination and quantum metrology. This work places the geometry of quantum state spaces on a rigorous axiomatic footing, bridging abstract metric theory, information geometry, and operational quantum principles.

Randomness is intrinsic to quantum mechanics; the outcome of a measurement on a quantum state is a random variable. This feature has been applied to randomness certification, where one party must decide whether the data they receive is truly random. However, existing demonstrations are not black-box, to avoid falsely certifying deterministic data, assumptions must be made on how the data was generated. Here we demonstrate genuine randomness certification in the black-box setting – one in which no deterministic adversary, even with unlimited computational power, will succeed in getting their data certified. We use it to provably generate random numbers using only measurements on single particle states and without a random seed.

Ultracold molecules are becoming an increasingly important technology for quantum simulation, computation, and sensing, but their state preparation in large, low-entropy arrays remains a key challenge. We propose to deterministically load single molecules into optical tweezer arrays or lattices from either thermal or degenerate gases, with a high probability of occupying the tweezer's motional ground state. Strong repulsion between microwave-shielded molecules prevents multiparticle occupancy. Our proposal represents a robust scheme for deterministic single molecule preparation directly in the motional ground state with expected fidelities exceeding 99 percent for small trap volumes and highly polar species. This method can be scaled to thousands of traps limited by the reservoir molecule number, opening the door to large, low-entropy polar molecule arrays for quantum computation, quantum simulation, and precision measurement.

The year 2025 marked the centennial of quantum mechanics, inaugurated by Heisenberg's matrix formulation and the foundational contributions of Pauli, Schrodinger, and Dirac. Concurrently, 2026 marks the centennial of the Klein - Gordon equation, the second-order relativistic wave equation from which both the Schrodinger and Dirac equations were derived. This article supplements the recent review published in J.Phys. A: Math.Theor.,58 (2025) 053001 by providing a more detailed examination of the formative period 1925 - 1928, with particular attention to contributions that have received insufficient recognition in the standard narrative. We reconstruct Kramers' independent derivation of the Dirac equation - obtained essentially simultaneously with Dirac's own result yet unpublished for seven years - and discuss its relation to Van der Waerden's group-theoretical approach. The role of Charles Galton Darwin in elucidating the physical content of the Dirac equation is also highlighted. In addition, we present two modern derivations not catalogued in the earlier review: one based on Operational Dynamical Modeling, which deduces the Dirac equation from relativistic Ehrenfest relations and the canonical commutation algebra, and one rooted in the Madelung hydrodynamic formulation. Three broad periods of quantum theory development – foundational, consolidation, and the modern era of quantum information – are briefly surveyed.

Inspired by measurement incompatibility and Bell-family inequalities in quantum mechanics, we propose the Non-Classical Network (NCnet), a simple classical neural architecture that stably exhibits non-classical statistical behaviors under typical and interpretable experimental setups. We find non-classicality, measured by the $S$ statistic of CHSH inequality, arises from gradient competitions of hidden-layer neurons shared by multi-tasks. Remarkably, even without physical links supporting explicit communication, one task head can implicitly sense the training task of other task heads via local loss oscillations, leading to non-local correlations in their training outcomes. Specifically, in the low-resource regime, the value of $S$ increases gradually with increasing resources and approaches toward its classical upper-bound 2, which implies that underfitting is alleviated with resources increase. As the model nears the critical scale required for adequate performance, $S$ may temporarily exceed 2. As resources continue to grow, $S$ then asymptotically decays down to and fluctuates around 2. Empirically, when model capacity is insufficient, $S$ is positively correlated with generalization performance, and the regime where $S$ first approaches $2$ often corresponding to good generalization. Overall, our results suggest that non-classical statistics can provide a novel perspective for understanding internal interactions and training dynamics of deep networks.

We present a systematic study of the Rayleigh–Ritz variational method for quantum oscillators in the Segal–Bargmann space. We rigorously derive the normalizability condition $|\alpha| < \tfrac{1}{2}$ for generalized Gaussian trial functions $\psi(z) = e^{\alpha z^2 + \beta z}$ through convergence analysis of Gaussian integrals in the complex plane. Applications to the harmonic oscillator demonstrate exact recovery of the ground state in Segal–Bargmann space when the trial family contains the true solution. For the quartic anharmonic oscillator ($\hat{H} = -\tfrac{1}{2}\partial_x^2 + \tfrac{1}{2}x^2 + \lambda x^4$), adaptive Gaussian ansätze in position space yield a cubic stationarity equation and perturbative energy expansions beyond first order, capturing anharmonic wavefunction narrowing. In contrast, monomial trial functions ($\psi_n(z) = z^n$) in the Segal–Bargmann space – while providing rigorous upper bounds $E_n = n + \tfrac{1}{2} + \tfrac{3\lambda}{4}(2n^2 + 2n + 1)$ for excited states – lack width adaptability and are limited to first-order accuracy for ground-state calculations. We further analyze displaced Gaussians and displaced monomials for asymmetric potentials (e.g., $x^3 + x^4$), showing that displacement parameters are essential to capture parity breaking and stabilization effects ($E_0 \approx \tfrac{1}{2} + \tfrac{3\mu}{4} - \tfrac{9\lambda^2}{4} + \cdots$).

Microfabricated surface-electrode traps are a scalable platform for trapped-ion quantum processors. Recent advances in fabrication techniques have enabled the design of increasingly complex multi-layer structures. Yet the control electrodes remain mostly unchanged and of rectangular shape. We systematically analyze asymmetric inner control electrode shapes for simultaneous axial and radial control in multi-layer surface traps, characterize and compare a selection of different shapes, and verify their capabilities in realistic use-case scenarios for ion transport and micromotion compensation. Eliminating the need for the commonly used additional outer control electrodes, asymmetric inner control electrodes increase the compactness and space efficiency of surface-electrode traps while concurrently reducing the number of control signals. The improved control voltage efficiency of using solely inner electrodes enables the device's entire direct-current (DC) supply to be provided by integrated Cryo-CMOS circuits, further enhancing the scalability of the processor.

Nonbenzenoid carbon frameworks expand low-dimensional material design via controlled asymmetry. Here, we show the experimentally realized 4-5-6-8 carbon nanoribbon establishes a topology-driven paradigm for multiproperty engineering, not just a graphene variant. Using hybrid DFT, tight-binding, and molecular dynamics in a multiscale framework, we demonstrate the symmetry-broken lattice stabilizes hierarchical bonds within standard energy ranges. This geometry produces a robust semiconducting state (hybrid gap >1 eV) and enables strain as a controllable modulation parameter. A tight-binding Hamiltonian fitted only at equilibrium accurately captures strain-dependent band evolution, proving the essential physics is topology-dominated. Mechanical analysis reveals high stiffness with fracture governed by the largest polygons, showing asymmetry redistributes stress without compromising integrity. Intrinsic phonon scattering suppresses thermal conductance, enabling favorable thermoelectric performance without extrinsic disorder. Optical response confirms non-equivalent ring connectivity reorganizes interband transitions, promoting strong visible absorption and efficient photocarrier generation. These results position topology as a governing parameter coupling elasticity, electronics, thermal transport, and optics, establishing the 4-5-6-8 nanoribbon as a unified platform for predictive design of multifunctional carbon materials.

Constrained combinatorial optimization problems are challenging for quantum computing, particularly at utility-relevant scales and on near-term hardware. At the same time, these problems are of practical significance in industry; for example, the Unit Commitment (UC) problem in energy systems involves complex operational constraints. To address this challenge, we apply copula-QAOA (cop-QAOA), a hardware-efficient approach for constrained optimization to a single-period UC that can be reduced to a one-dimensional knapsack. Cop-QAOA biases the quantum state toward feasible solutions using constant-depth mixers and appropriately biased initial states. We implement our benchmark on problem instances that are confirmed to be hard for classical solvers such as Gurobi. Our results show that cop-QAOA often finds solutions better than a lazy greedy baseline and very close to, and in some instances surpasses, those obtained by Gurobi, with only a few QAOA rounds. This work presents the largest successful demonstration of the knapsack problem on IBM Quantum hardware using up to 150 qubits, and more generally, the largest demonstration of constrained combinatorial optimization where constraints are enforced via shallow mixers.

We investigate the scattering features of a non-Hermitian rectangular potential within the framework of space-fractional quantum mechanics. Using the Riesz fractional derivative, we analytically derive locus equations for spectral singularities (SSs) and their time-reversed counterparts, coherent perfect absorption (CPA), in a dimensionless complex-potential parameter space. This geometric locus formulation provides a transparent representation of the SS and CPA conditions and enables direct visualization of how fractional quantum dynamics modifies non-Hermitian scattering. We show that reducing the Lévy index $\alpha$, which enhances nonlocal transport associated with Lévy-flight dynamics, systematically lowers the gain-loss strength required for the emergence of SSs and CPAs, while increasing the mode index further suppresses this threshold. In addition, for fixed potential parameters, we demonstrate that decreasing $\alpha$ induces a blue shift of the SS energy, in direct agreement with earlier studies. From this perspective, the Lévy index $\alpha$ emerges as a tunable control knob for SS-CPA settings in fractional non-Hermitian quantum systems. Beyond its quantum-mechanical setting, this study may find applications in fractional waveguides and metamaterials governed by fractional wave equations. This work also bridges the gap between non-Hermitian quantum mechanics and space-fractional quantum mechanics.

The quantum switch is a basic network primitive that allows one to connect multiple nodes in a quantum network via a central node. We show that the same functionality can be achieved with a different geometry that does not rely on a powerful and large central unit, but instead utilizes evenly distributed resources. This approach is resilient against node failures. We provide a nested construction with logarithmically many qubits per node and a total of $O(n\log n)$ Bell pairs, in contrast to other distributed approaches based on pre-shared entanglement that scale as $O(n^2)$. The construction achieves fully flexible pairwise connectivity, where the shared resource state can be locally transformed into $n/2$ arbitrarily distributed Bell states. We also present a graph state variant with just one qubit per node, which allows one to generate $O(n/\log^2 n)$ Bell pairs.

Quantum magic resources, or nonstabilizerness, are a central ingredient for universal quantum computation. In noisy many-body systems, the interplay between these resources and errors leads to sharp magic phase transitions. However, the microscopic mechanism behind these critical phenomena is still an open question, especially since early empirical evidence showed conflicting results regarding their universality classes. In this work, we provide a comprehensive picture of magic phase transitions for the class of encoding-decoding quantum circuits to resolve these ambiguities. We analytically show that the behavior of magic resources is fundamentally dictated by the chosen measurement protocol. When we fix, or post-select, the class of measurement syndromes, the magic transition inherits the universal features of the error-resilience phase transition in the circuits. Interestingly, this clean transition survives even for fully random Haar encoders showing that it is a consequence of initial's state retrieval, and not an artifact of the Clifford encoders. On the other hand, if we consider realistic Born-rule sampling, the intrinsic statistical fluctuations of a given syndrome measurement act as a relevant perturbation. This brings in strong finite-size drifts and an apparent multifractality, which end up altering the critical behavior of the system. We reveal that magic phase transitions are actually direct manifestations of error-resilience thresholds, rather than independent critical phenomena, reconciling conflicting observations from the earlier literature. Ultimately, our framework clarifies how the quantum computational power can survive, or be irreversibly destroyed, due to the competition between scrambling, measurements, and errors.

Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning. Contemporary quantum machine learning is generally limited to toy examples or heavily restricted datasets with few elements. This is not only due to the current limitations of available quantum hardware but also due to the absence of inductive biases arising from application-agnostic designs. Current quantum solutions must resort to tricks to scale down high-resolution images, such as relying heavily on dimensionality reduction or utilizing multiple quantum models for low-resolution image patches. Building on recent developments in classical image loading to quantum computers, we circumvent these limitations and train quantum Wasserstein GANs on the established classical MNIST and Fashion-MNIST datasets. Using the complete datasets, our system generates full-resolution images across all ten classes and establishes a new state-of-the-art performance with a single end-to-end quantum generator without tricks. As a proof-of-principle, we also demonstrate that our approach can be extended to color images, exemplified on the Street View House Numbers dataset. We analyze how the choice of variational circuit architecture introduces inductive biases, which crucially unlock this performance. Furthermore, enhanced noise input techniques enable highly diverse image generation while maintaining quality. Finally, we show promising results even under quantum shot noise conditions.

Floquet codes define fault-tolerant protocols through periodic measurement sequences that drive a dynamically evolving stabilizer group. They provide a natural framework for hardware supporting two-qubit parity measurements but no unitary entangling gates. However, few known constructions achieve both high encoding rates and high thresholds. We close this gap by introducing Stairway codes, a family of high-rate Floquet protocols obtained by Floquetifying Abelian two-block group algebra codes, a class that includes the bivariate bicycle codes. By representing the static code as a foliated ZX-calculus network within a $(w{-}1)$-dimensional space-time lattice and rotating the time axis, we decompose its weight-$w$ stabilizers into a periodic sequence of pairwise measurements. This reduces the design of new codes within this family to the selection of favorable periodic boundary conditions. We identify instances with competitive parameters, analyze their distance under circuit-level noise, and demonstrate logical error rates surpassing those of other Floquet codes at comparable encoding rates. Remarkably, our construction requires fewer than 300 physical qubits to match the distance and encoding rate of semi-hyperbolic Floquet codes that use over 1300 qubits.

We present a joint experimental and theoretical study of a ratchet implemented in arra ys of evanescently coupled plasmonic waveguides with tailored losses. In this setup the time-periodic dissipation is the only active mechanism and notably, we find better rectified transport and lower losses in the transmitted signal with increased local dissipation. Using Floquet theory, we uncover a driving regime that allows efficient directional tr ansport for suitable driving frequencies and loss rates, which are linked to linear qu asienergy bands with minimal losses. These regions are separated from non-resonant beh avior by sharp transitions with characteristic exceptional points in the spectrum. Direct experimental observation of the Floquet-dissipative ratchet effect using a comb ination of real- and Fourier-space leakage radiation microscopy is provided.

We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operator-valued decision rule derived from quantum state discrimination. The method associates each class with an encoded mixed state and performs classification through a single POVM construction, thus providing a genuinely multi-class strategy without reduction to pairwise or one-vs-rest schemes. In this perspective, classification is reformulated as the discrimination of a finite ensemble of class-dependent density operators, with performance governed by the geometry induced by the encoding map and by the overlap structure among classes. To assess the practical scope of this framework, we apply the PGM-based classifier to two biomedical radiomics case studies: histopathological subtyping of non-small-cell lung carcinoma (NSCLC) and prostate cancer (PCa) risk stratification. The evaluation is conducted under protocols aligned with previously reported radiomics studies, enabling direct comparison with established classical baselines. The results show that the PGM-based classifier is consistently competitive and, in several settings, improves upon standard methods. In particular, the method performs especially well in the NSCLC binary and three-class tasks, while remaining competitive in the four-class case, where increased class overlap yields a more demanding discrimination geometry. In the PCa study, the PGM classifier remains close to the strongest ensemble baseline and exhibits clinically relevant sensitivity–specificity trade-offs across feature-selection scenarios.

We study a harmonic oscillator weakly coupled to a thermal bath and evolving under the standard quantum-optical master equation. For zero-mean Gaussian states, entropy alone does not determine the internal energy because the latter also depends on nonequilibrium Gaussian structure. We show, however, that thermal relaxation yields the exact representation $ E=E(S,\dot S) $ for every mixed Gaussian state evolving under this thermal quantum-optical master equation. Explicitly, \[ E(S,\dot S)= \frac{\omega\nu(S)}{\nu_{\rm th}} \left[ \nu(S)+ \frac{\dot S}{\gamma S'(\nu(S))} \right]. \] Once the thermal GKLS equation is assumed, no further expansion in the damping rate, entropy rate, or distance from equilibrium is made. The entropy rate is not an alternative instantaneous state coordinate: it depends on the open-system dynamics and therefore carries information about the coupling to the bath. This is exposed by the isolated limit, where $\dot S=0$ for all Gaussian states and the representation no longer determines the energy. The result thus identifies an exact thermalization-induced dynamical energetic relation, distinct from rate dependence generated by an external driving protocol. We also describe an operational test based on time-resolved purity and energy measurements. At fixed frequency the bath only relaxes pre-existing squeezing. We therefore also consider frequency modulation, which can drive an initially thermal state away from the instantaneous Gibbs family by generating off-Gibbs Gaussian structure. Within a local Gaussian continuation, the bath then relaxes this driven deformation, providing a dynamical realization of the entropy-rate dependence.

Overlap-based distinguishability measures, such as fidelity- or Chernoff-type quantities, play a central role in quantum sensing and quantum illumination. In strongly lossy and fluctuating environments, however, these quantities may become numerically compressed and therefore less informative for optimization, monitoring, or adaptive control. In this work, we investigate transport-based distinguishability criteria for lossy quantum sensing. We first introduce an isotropic Gaussian transport metric defined on first and second moments and compare it with a fidelity-based benchmark in a thermal-loss model. We then show analytically that, within an isotropic thermal-reference geometry, this metric locally disfavors squeezing relative to coherent displacement, thereby distinguishing global phase-space robustness from directional metrological advantage. We next introduce a projected transport metric adapted to quadrature-resolved measurements and show that its optimization over the measurement quadrature is analytically tractable, reducing to a boundary choice between the principal axes of the output noise ellipse. We further extend the framework to a measurement-aware metric defined on detector output statistics, and derive an explicit Gaussian formula for a noisy quadrature measurement chain. Finally, in a fading setting, we show that the isotropic metric and the projected metric aligned with the coherent displacement retain first-order sensitivity to the transmissivity in the strong-loss regime, whereas the orthogonal projected metric is compressed to second order. These results support a hierarchical view of transport-based distinguishability in quantum sensing, ranging from global robustness indicators to measurement-adapted operational metrics.

The discovery of chaotic quantum circuits with (partially) solvable dynamics has played a key role in our understanding of non-equilibrium quantum matter and, at the same time, has helped the development of concrete platforms for quantum computation. It was shown that solvability does not prevent the generation of chaotic dynamics, however, it imposes non-trivial constraints on the generated correlations. A natural question is then whether it is possible to gain insight into the generic case despite the latter being very hard to access. To address this question here we introduce a family of 'asymptotically solvable' quantum circuits where the solvability constraints only affect correlations on length scales beyond a tuneable threshold. This means that their dynamics are only solvable for long enough times: for times shorter than the threshold they are generic. We show this by computing both their dynamical correlations on the equilibrium (infinite temperature) state and their thermalisation dynamics following quantum quenches from compatible (asymptotically solvable) non-equilibrium initial states. The class of systems we introduce is generically ergodic but contains a non-interacting point, which we use to provide exact analytical results, complementing those of numerical experiments, on the non-solvable early time regime.

Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high-dimensional Hilbert spaces and provide an alternative approach to model expressivity. We compare classical models and a variational quantum classifier on the XOR problem. Logistic regression, a one-hidden-layer multilayer perceptron, and a two-qubit variational quantum classifier with circuit depths 1 and 2 are evaluated on synthetic XOR datasets with varying Gaussian noise and sample sizes using accuracy and binary cross-entropy. Performance is determined primarily by model expressivity. Logistic regression and the depth-1 quantum circuit fail to represent XOR reliably, whereas the multilayer perceptron and the depth-2 quantum circuit achieve perfect test accuracy under representative conditions. Robustness analyses across noise levels, dataset sizes, and random seeds confirm that circuit depth is decisive for quantum performance on this task. Despite matching accuracy, the multilayer perceptron achieves lower binary cross-entropy and substantially shorter training time. Hardware execution preserves the global XOR structure but introduces structured deviations in the decision function. Overall, deeper variational quantum classifiers can match classical neural networks in accuracy on low-dimensional XOR benchmarks, but no clear empirical advantage in robustness or efficiency is observed in the examined settings.

The Kochen-Specker no-go theorem established that hidden-variable theories in quantum mechanics necessarily admit contextuality. This theorem is formally stated in terms of the partial Boolean algebra structure of projectors on a Hilbert space. Each partial Boolean algebra provides a semantics for interpreting propositional logic. In this paper, we examine the complexity of propositional satisfiablity for various classes of partial Boolean algebras. We first show that the satisfiability problem for the class of non-trivial partial Boolean algebras is NP-complete. Next, we consider the satisfiability problem for the class of partial Boolean algebras arising from projectors on finite dimensional Hilbert spaces. For real Hilbert spaces of dimension greater 2 and any complex Hilbert spaces of dimension greater than 3, we demonstrate that the satisfiablity problem is complete for the existential theory of the reals. Interestingly, the proofs of these results make use of Kochen-Specker sets as gadgets. As a corollary, we conclude that deciding quantum homomorphism in these fixed dimensions are also complete for the existential theory of the reals. Finally, we show that the satisfiability problems for the class of all Hilbert spaces and all finite-dimensional Hilbert spaces is undecidable.

Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circuit compilation and execution management on multiple quantum devices in the network. The latter aspect is very challenging because, while reducing the makespan of job batches remains a relevant objective, novel quantum-specific constraints must be considered, including QPU utilization, non-local gate rate, and the latency associated with queued DQC jobs. In this work, a range of scheduling strategies is proposed, simulated, and evaluated, including heuristics that prioritize resource maximization for QPU utilization, node selection based on heterogeneous network connectivity, asynchronous node release upon job completion, and a scheduling strategy based on reinforcement learning with proximal policy optimization. These approaches are benchmarked against traditional FIFO and LIST schedulers under varying DQC job types and network conditions for the allocation of DQC jobs to devices within a network.

Bosonic quantum error-correcting codes encode logical information in a harmonic oscillator, with the Gottesman-Kitaev-Preskill (GKP) and number-phase (NP) codes representing two fundamentally different encoding paradigms. Although both have been extensively studied, it remains unclear under what physical noise conditions (including photon loss and dephasing) one encoding intrinsically outperforms the other. Here we estimate a quantitative performance boundary between GKP and NP codes under general photon loss-dephasing noise. By optimizing code parameters within each encoding family, we identify the noise regimes in which each code exhibits a fundamental advantage. In particular, we find that the crossover occurs when the dephasing strength is approximately two orders of magnitude smaller than the loss strength, revealing a sharp separation between operational regimes. Beyond this specific comparison, our work establishes a practical and extensible methodology for benchmarking bosonic codes and optimizing their parameters, providing concrete guidance for the experimental selection and deployment of bosonic encodings in realistic noise environments.

Superconducting quantum processors operate at microwave frequencies in millikelvin environments, making it challenging to interconnect distant nodes using conventional microwave wiring. Coherent microwave-to-optical (M2O) transduction enables superconducting quantum networks by interfacing itinerant microwave photons with low-loss optical fiber. However, many state-of-the-art transducers provide efficient conversion only over a narrow frequency span, complicating deployment with heterogeneous superconducting devices that are detuned by gigahertz-scale offsets. Here we demonstrate a frequency-agile microwave-optical interface that overcomes this bandwidth mismatch by cascading an electro-optic M2O transducer with a multimode microwave-to-microwave (M2M) frequency converter, with in situ tunability of the microwave resonances in both stages. Using this architecture, we realize continuous frequency coverage from 5.0 to 8.5 GHz within a single system. As an application relevant to superconducting-qubit networking, we use the cascaded M2M-M2O interface to optically read out a superconducting qubit whose readout resonator is detuned by 1.7 GHz from the native M2O microwave resonance, demonstrating a scalable route toward fiber-linked superconducting quantum nodes.

Entanglement between solid-state quantum emitters (QEs) is a key resource for photonic quantum technologies. Achieving such entanglement requires strong and controllable long-range interactions between QEs. However, engineering such coupling remains challenging, particularly for on-chip distant solid-state QEs. Here, we introduce a forward-designed platform that enables ultracompact nanophotonic architectures to mediate enhanced long-range QE-QE interactions via engineered surface plasmon polariton interference. Using this strategy, we realize two distinct configurations: a phase-conjugated elliptic design for energy funneling, and a co-radiating hyperbolic design for its suppression. We experimentally demonstrate large enhancement and suppression of energy transfer rates compared to bare substrates. Furthermore, we predict transient entanglement between spatially separated QEs with concurrence peaking at 0.493, approaching the theoretical bound in the transient regime. Extending to the multi-QE case, we observe enhanced energy funneling and predict QE-QE entanglement in three-QE configurations. These results establish a compact and scalable framework for on-chip entanglement engineering in integrated quantum nanophotonic systems.

We propose a heralded entanglement generation scheme based on Gaussian sources enhanced by photon addition and subtraction operations. By combining single-mode squeezing, linear interferometers, and conditional photon-number measurements on ancillary modes, our model can probabilistically generate dual-rail encoded Bell, GHZ, and W states. We systematically optimize the squeezing parameters and interferometer settings to maximize both the heralding success probability and the fidelity with the target states. Our results show that photon addition and subtraction significantly enhance the non-classicality of the output states and improve generation performance, while maintaining computational efficiency comparable to single-photon source models. We further analyze the robustness of the scheme under parameter perturbations and find that its performance remains stable under realistic experimental imperfections. This work provides a versatile and experimentally feasible framework for scalable heralded entanglement generation using Gaussian resources with non-Gaussian operations.