Papers

Continuous quantum feedback uses time-resolved measurement records to steer monitored systems toward pure states. Yet how the information available to a controller determines the ultimate purification speed remains unresolved. We establish this relation for a qubit under fixed-spectrum Hermitian monitoring with detector loss, obtaining the exact long-time impurity-moment spectrum optimized over causal basis controls at each horizon. Rare records with nearly canceled evidence then make all moments from half order upward decay at the Bhattacharyya information rate between two quantum nondemolition record laws. Aligned quantum nondemolition monitoring preserves that binary distinguishability and attains the limit, while complete detection restores an order-dependent branch. The mechanism extends to higher dimensions, where an attainable rank-two ceiling lies above the full-rank qutrit upper bound over a finite moment interval, establishing retained record distinguishability as a purification resource.

Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias. We present a hybrid quantum-classical framework for synthetic data generation using a Quantum Circuit Born Machine (QCBM) to address these limitations. The proposed approach exploits quantum mechanical properties – superposition and entanglement – within a parameterized variational quantum circuit to model complex probability distributions that are difficult for classical generative methods to capture. Experiments are conducted on two tabular benchmark datasets: the Iris dataset and the Telco Customer Churn dataset. Preprocessing includes normalization and PCA-based dimensionality reduction to enable efficient basis encoding for quantum circuits. The QCBM is trained by minimizing Kullback-Leibler (KL) divergence between real and generated data distributions using a gradient-based parameter-shift optimization rule. Augmenting training data with QCBM-generated synthetic samples at 40-50% of the minority class improves F1-score by approximately 5-15% and minority-class recall by 10-25%. Cross-domain evaluations (Train on Synthetic, Test on Real; and Train on Real, Test on Synthetic) reveal a performance gap of only 3-10%, indicating strong distributional fidelity. Comparative analysis against classical oversampling methods – SMOTE, Borderline-SMOTE, KMeansSMOTE, and SVM-SMOTE – shows that QCBM achieves competitive classification performance and produces lower Maximum Mean Discrepancy (MMD) on the Telco dataset, suggesting superior structural similarity in certain imbalanced settings. These findings establish QCBM as a viable complementary tool for data augmentation, particularly for low-dimensional structured tabular data with class imbalance.

Symmetry imposes fundamental constraints on quantum measurement and control. The Wigner-Araki-Yanase theorem and its quantitative extensions capture this restriction for continuous symmetries, in terms of fluctuations of conserved generators. Such generator-based bounds, however, do not provide quantitative limitations for discrete unitary symmetries or for antiunitary symmetries. Here we establish quantitative WAY-type theorems for symmetry-breaking projective measurements and unitary gates under arbitrary unitary and antiunitary symmetries. Our approach is based on a two-target no-programming inequality: if a single processor approximately implements two operations that amplify distinguishability, then the corresponding program states must themselves be distinguishable. Applied to symmetric implementations, this converts the error of an asymmetric measurement or gate directly into a lower bound on the asymmetry of the apparatus state, quantified by its fidelity with its symmetry-transformed copy. Our results apply to discrete and antiunitary symmetries, thereby providing a fundamental limit for symmetry-limited quantum measurement and control beyond the continuous-symmetry regime.

We define a two-particle quantum walk of identical particles on a graph $G$ via the one-particle Grover walk on the Kronecker product $G \otimes G$, and call it the two-particle Grover walk. In systems of identical particles, quantum mechanics requires that quantum states have a certain invariance with respect to the exchange of particles. Focusing on the symmetry of the Kronecker product $G \otimes G$ as a graph, we show that the time evolution operator of this walk commutes with the swap operator, which ensures that this requirement is satisfied. Furthermore, we study the entanglement entropy of quantum states evolved by this walk. For the complete bipartite graph $K_{n,n}$, we completely determine the values of $n$ for which the quantum states evolved from specific initial states attain the upper bound of the entropy at some time, and prove that they are exactly $1$ and $2$.

Quantum simulation of open quantum systems in the noisy intermediate-scale quantum (NISQ) era is hindered by the non-unitary nature of dissipative dynamics and the limited quantum resources available on near-term quantum processors. In this work, we propose a resource-efficient algorithm for simulating Lindbladian dynamics on NISQ devices. For open quantum systems with Pauli dissipations, we first derive a compact and stable mixed-unitary adjoint channel that approximates the target dissipative dynamics and enables ancilla-free implementation through trajectory sampling. To further reduce the circuit depth required for implementing the sampled trajectories, we introduce an adaptive variational quantum trajectory compression framework. In this framework, a depth-adaptive parameterized quantum circuit is trained to approximate repeated Trotterized Hamiltonian simulation operators, which are then used to replace repeated unitary segments appearing in the sampled trajectories. Importantly, the training procedure can also be performed without auxiliary qubits. Numerical simulations of the dissipative quantum $XY$ model demonstrate the accuracy and resource efficiency of the proposed algorithm. Our results provide a practical route toward ancilla-free and depth-reduced simulation of open quantum systems on near-term quantum hardware.

Incompatibility constitutes a fundamental aspect of quantum mechanics. However, not every quantum observable non-classical property arises from incompatibility, nor can all quantum scenarios be fully captured by incompatibility alone. Within the framework of partial Boolean algebra (pBA), we research the structural properties of incompatibility scenarios. We introduce a unified method to realize any incompatibility scenario via a classical game, and the construction is extendable to any scenario embeddable into a Boolean algebra. The exclusivity graph offers a precise characterization of incompatibility scenarios. We prove that every exclusivity graph is the atom graph of an exclusive pBA, which is embedded into a Boolean algebra. These results provide a necessary condition for exclusivity graphs and a sufficient condition for atom graphs.

Quantum logic is usually presented as a non-classical departure from ordinary reasoning forced on us by quantum mechanics, with classical logic kept as the secure starting point. We argue for the opposite order of explanation in a finite and fully computable setting. The free orthomodular lattice on two generators has ninety-six elements, the direct product of a six-element non-distributive factor and a sixteen-element Boolean factor. Reading the first factor as a register of contexts and the second as Boolean content, we obtain a calculus whose elements are context–bit-vector pairs and whose operations act component by component. With this calculus we establish three results. First, we classify the six layers by commutativity, identifying the central kernel of context-neutral propositions together with a dual central layer in which all complementary contexts are present. Second, we show that orthocomplementation rearranges the layers exactly as the complementation of the small factor rearranges its elements, which makes the duality among the layers rigid rather than accidental. Third, we prove that the operation forgetting the context is a surjective homomorphism of orthocomplemented lattices whose quotient is the classical Boolean algebra, so that classical logic is a six-to-one, information-losing image of the contextual calculus.

In quantum programs, Bugs4Q is a widely used benchmark containing real quantum defects. However, its evaluation assumes that benchmark labels remain valid and that generated fixes execute in the target environment. We evaluate two Bugs4Q versions containing 67 unique real Qiskit defects, adding executable tests where missing, and re-run all entries across six pinned Qiskit releases (0.25.0, 0.45.0, 1.0.0, 1.1.1, 2.0.0, and 2.3.1). We find that quantum benchmarks can suffer from silent label inversion: entries become invalid without errors when reference fixes stop executing or buggy programs no longer reproduce failures. Thus, correctness depends on the (benchmark, version) pair rather than the benchmark alone. We evaluate four LLMs (GPT-4o-mini, GPT-5o-mini, GPT-5.4, and GPT-5.4-mini), generating up to 10 repair candidates per defect and testing them across all versions. GPT-5.4 achieves the highest pass@10 (48.8%), followed by GPT-5.4-mini (47.3%), GPT-5o-mini (30.3%), and GPT-4o-mini (22.6%). All models perform best on Qiskit 0.45.0 and decline after the Qiskit 1.0 transition. Many failures arise from deprecated or incompatible APIs rather than incorrect repairs, and 64% of successful repairs occur on entries invalid under the target version. We release a re-validated, version-pinned Bugs4Q benchmark and show that benchmark validation must precede repair evaluation.

Accurate noise classification is essential for operating near-term quantum processors, yet existing approaches, such as quantum process tomography, scale exponentially with system size, limiting their practicality for routine calibration. We propose a measurement-efficient noise fingerprinting pipeline that combines structured classical shadow tomography with physics-informed feature engineering to identify noise channels from a fixed set of 3-qubit probe circuits. Each sample is represented by a feature vector constructed from randomized Pauli measurements and derived observables designed to resolve physically similar noise channels that produce overlapping signatures under generic measurement sets. We evaluate random forest, extra trees, and a multilayer perceptron on 10,000 labeled samples spanning ten noise models. The three classifiers achieve comparable performance. In the reported runs, random forest and extra trees perform similarly, achieving approximately 0.736 test accuracy and 0.729-0.730 macro F1, compared with 0.715 accuracy and 0.699 macro F1 for the multilayer perceptron. We further analyze the effect of the noise-strength sampling range and conduct a limited sensitivity check using analogous 2- and 4-qubit probes. Confusion analysis shows that readout error, phase flip, thermal relaxation, and bit flip are classified with high reliability, while most remaining errors occur among channels with similar physical effects.

We present Qupertino, an open-source quantum circuit simulator for Apple Silicon, and use it to ask how far a simulator written purely in MLX array operations can go and what remains for hand-tuned Metal shaders. The framework ships two measured tiers. The pure tier dispatches structured gates to specialized MLX kernels: diagonal gates run as broadcast phase multiplies, controlled gates as masked half-state updates, and SWAP as an axis permutation; a paired dense-path ablation attributes a 25-33x speedup to this dispatch alone. The opt-in shader tier adds hand-written Metal kernels for every structured layer family in our benchmarks, including phase-LUT diagonals, GF(2) affine permutation gathers, fused tensor-product single-qubit layers, radix-4 QFT and Walsh-Hadamard butterflies, and basis-conjugated XX/YY Trotter layers; runtime fusion detectors route work to them while preserving circuit semantics exactly, confirmed by parity tests. In a four-way interleaved campaign on M1 Max (two warmups, ten measured repeats per cell), the shader tier is fastest by mean runtime in all 18 comparison cells against same-machine Qiskit Aer CPU and PennyLane lightning.qubit. At 25 qubits, gate-stream QFT runs in 0.0591 +/- 0.0029 s (paired 47.2x over Aer, 95.3x over PennyLane) and TFIM Trotter evolution in 0.495 +/- 0.035 s (36.2x and 67.2x). Across a 29-workload suite, the shader tier's paired speedup over pure MLX reaches 25x, with 25 of 29 workloads accelerating above parity. The framework also supports variational ansatz workloads, QAOA, QCBM, Trotter-Suzuki Hamiltonian simulation, and OpenQASM 2.0 import (unitary subset); a preliminary MPS backend reaches 150 qubits on limited-entanglement workloads. Correctness rests on 253 Python tests, independent complex128 checks, and a Trotter-error curve against exact diagonalization. State-vector memory remains exponential in qubit count.

Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable, reproducible, and cost-transparent form. We present QuantumNovelty, an open-source skill-orchestrating language agent that both generates quantum-computing artifacts (papers, Pareto-front ansatz candidates, and patent drafts) and reviews them through simulated referee and patent-examiner panels. Its design contribution is an audit-and-falsify layer of deterministic gates – strict Pareto domination, numerical recomputation from on-disk artifacts, Wilson small-sample intervals, and a cross-vendor consensus guard – that constrains, rather than generates, the claims allowed to survive; every model call is logged with backend, token count, and cost. We make no accuracy claim against human experts, and validate only what is checkable without human labels: on a planted adversarial corpus the deterministic gates catch every planted overclaim with no false positives, and on a first deployment (six manuscripts and one granted patent, at a measured cost of about twenty-four US dollars) the panels are directionally more conservative than the public acceptance record, on a one-sided sample. The framework is decision support, not a replacement for peer review or patent examination, and we report in full where its mechanisms remain unexercised on real inputs.

Ultralong-range Rydberg molecules, formed by the interaction between a highly excited Rydberg atom and a ground-state atom, provide a unique platform for exploring quantum phenomena spanning nanometer-to-micrometer distances as well as exotic few-body interactions. The formation mechanisms and resultant physical properties differ markedly between s-wave and p-wave scattering channels. Here we report the experimental observation of node-dependent p-wave molecular signals in Rb(nS)-Rb(5S) Rydberg molecular spectra, where variations in the principal quantum number n directly reveal the shift of molecular binding energies induced by the moving nodal structure of the Rydberg electron wavefunction. This node-dependence is attributed to a cooperative effect between the local gradient of the nS-electron wavefunction and the energydependent p-wave scattering length. In addition, resolving two p-wave bound states associated with adjacent nodes highlights the remarkable sub-nanometer spatial resolution achieved in our experiment. Our findings reveal a more profound quantum control mechanism, wherein the principal quantum number acts as a switch for nodal-selective molecular bound states, and the reported method provides a sensitive spectroscopic probe of electron-atom scattering.

The analysis of highly non-linear stochastic data within non-equilibrium dynamical systems requires computational frameworks capable of detecting latent phase transitions before systemic structural breakdowns occur. Traditional Variational Quantum Algorithms (VQAs) are frequently bottlenecked by vanishing gradients, the barren plateau problem, and prohibitive training overheads. In this paper, we propose a novel Hybrid Quantum Reservoir Computing (nHQRC) framework, which bypasses these limitations by employing a frozen, disordered Transverse-Field Ising Model (TFIM) to project time-dependent stochastic driving forces into an exponentially large Hilbert space. To resolve the physical phase multi-wrapping vulnerabilities present in baseline quantum reservoir models, we introduce a lookahead-free, pre-amplification manifold scaling technique. Multi-qubit configurations are genetically optimized to the "edge of chaos," while quantum state tracking is performed by extracting von Neumann entropy ($S$) and exact mixed-state Quantum Fisher Information (QFI) to act as leading entanglement witnesses. Utilizing these quantum triggers as boundary constraints, trajectory predictions are constructed via a generative Stochastic Schrödinger Bridge (SSB) readout. By subjecting the quantum reservoir to an 8-dimensional non-stationary stochastic driving field, the framework significantly improves systemic drift-to-diffusion efficiency ($\eta$) and actively arrests maximum trajectory decay (MTD) by over 13% compared to standard classical benchmarks. This establishes a robust, $\mathcal{O}(1)$ temporal overhead blueprint for near-term quantum regime detection and macroscopic subsystem stabilization.

Quantum teleportation (QT) over satellite-based free-space optical (FSO) channels is a promising approach for long-distance quantum communication. However, its performance is significantly degraded by atmospheric loss and turbulence. In this paper, we investigate continuous-variable (CV) QT in a dual-downlink scenario, where a satellite distributes entangled states to two ground stations. To mitigate channel-induced degradation, we employ a non-Gaussian entanglement distillation protocol based on the sequential application of photon addition and photon subtraction (PA-PS) on the weaker channel. The results show that the proposed scheme improves teleportation fidelity by up to 7.7% and enhances entanglement negativity by approximately 105% in the low-to-moderate (below 600 km) loss regime. In addition, we identify an optimal squeezing parameter that balances entanglement strength and noise sensitivity. Taken together, these results demonstrate the effectiveness of PA-PS distillation for improving CV quantum communication in realistic satellite networks. We further characterize the trade-off between fidelity gain and the heralded success probability of the protocol.

Efficiently solving nonlinear ordinary and partial differential equations using a quantum computer is a major challenge due its inherent linearity. To circumvent this challenge, the Carleman linearization method has been proposed to transform a nonlinear ordinary differential equation into a linear system of equations, the primary advantage being that existing quantum linear systems algorithms may then be applied to obtain a solution. However, this methodology also brings forth several major challenges that must be addressed to attain a quantum advantage. Herein, we address several of these challenges enabling us to solve the Carleman linearized one-dimensional Burgers' equation on real and simulated quantum hardware. All simulations were performed on BlueQubit's platform allowing for quantum circuits to be run on GPU or QPU's seamlessly. We first demonstrate that the Carleman linearized Burgers' equation can be efficiently loaded onto a quantum computer using the linear combination of non-unitaries method, an alternative to the linear combintaiton of unitaries approach. Once loaded, the linear system is then solved using the variational quantum linear solver. Since a naive implementation of this solver is hindered by the barren plateau phenomenon, we introduce a multigridding method to solve the problem in a series of stages with the solution of the previous stage acting as a warm start for the next stage. This approach is found to significantly improve the accuracy of the solution compared with a naive cold start. Finally, circuits with a combined number of spatial and temporal discretization points totaling up to $2^{80} \approx 10^{24}$ are transpiled onto real quantum hardware demonstrating that the proposed methodology could feasibly produce a quantum advantage on future hardware.

Quantum phase transitions in many-body systems give rise to highly entangled states, and understanding their quantum correlations is crucial for characterizing quantum materials. However, traditional entanglement measures such as entanglement entropy are difficult to interpret for noisy or mixed states and require complex circuits to evaluate. Therefore, we explore the Positive Partial Transpose (PPT) criterion, coupled with overlapping state tomography, as an efficient and scalable spin-spin entanglement witness. It detects pairwise entanglement from reduced density matrices, distinguishes quantum from classical correlations, and applies to both pure and mixed states. It is ideal for studying condensed matter systems prepared on noisy quantum devices as well as future extensions to finite temperatures. We demonstrate the approach on quantum hardware, using variational circuits to prepare quantum critical states with up to 20 qubits and completely map their two-spin entanglement across various quantum phase transitions.

Spontaneous collapse models provide a possible solution to the measurement problem by modifying standard quantum dynamics. The modification consists of adding non-linear and stochastic terms inducing wavefunction collapse in space. Non-Markovian versions of these models are motivated by physical reasons, phenomenological consistency, and potential for relativistic extensions. Here, we investigate a non-Markovian version of the Poissonian Spontaneous Localization (PSL) model, i.e., a model characterized by instantaneous and localized collapse events. We assume that our model is characterized by a typical time scale $\tau_C$ so that, given an initial state $\rho_0$ of standard quantum matter at time $t=0$, we derive an effective long-time ($t\gg \tau_C$) statistical dynamics in terms of a CPTP map $\Phi_t$. We then show how $\Phi_t$ can be made equal to that obtained by non-Markovian CSL models. Moreover, given $\Phi_t$, we obtain the associated time-convolutionless master equation by means of a supercumulant expansion. Finally, we characterize the collapse events process (for events with $t \gg \tau_C$) given an initial quantum state $\rho_0$ at $t=0$.

This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset. Using a novel architecture inspired by previous QCNN and classical convolutional neural network (CNN) implementations, we use a hierarchical partitioning approach to implement a QCNN circuit that can be approximated and simulated efficiently on a classical machine for a large problem. First, the original image is partitioned such that each process handles a smaller portion of the image, which is encoded into independent states. Then, these partitions merge and combine, resulting in states that contain information from both partitions while halving the number of processes. After repeating this until one process remains, we reduce the dimensionality of the state until a single qubit remains for measurement. Using this approach, we can use multiple processes in parallel to simulate a large QCNN program without the need for exponentially growing hardware requirements as the number of qubits increases. In our work, we use this scheme to train a 128-qubit model, which is impossible to run on any classical supercomputer without the novel architecture. We also explore the impact of this new model architecture on prediction accuracy by training it to perform binary classification on the MNIST dataset with a small number of qubits, and comparing it to a model without partitioning. Our initial findings show that partitioning images into smaller sub-images with this architecture does not degrade the model's performance and sometimes even improves it, likely because it reduces the Barren plateaus issue in the partitioning process.

We develop a field-theoretic framework for quantum error correction centred on fusion-space codes in unitary fusion categories. Admissible clusters determine total-charge sectors and orthogonal footprint projectors recording locally visible data left by error histories. The central distinction is between diagnostic footprint algebras and syndrome-admissible commuting algebras: the latter can be measured without revealing logical information and resolve chosen error representatives into measured sectors. For such algebras, exact correctability is equivalent to fibrewise Knill–Laflamme conditions, yielding a measure-then-recover factorization. Under a contractible-vacuum locality hypothesis, closed neutral composites give a categorical sufficient criterion for scalar action on the code. In the Ising theory, four $\sigma$ punctures show that pair-charge footprints can be complementary logical diagnostics and realize an exact one-qubit Clifford shadow. A proper six-$\sigma$ code instead admits a syndrome-admissible pair-charge measurement and exact recovery from an explicit Majorana bilinear error. A second bilinear has the same measured footprint but differs by a logical bit flip, producing a concrete nontrivial footprint fibre and genuine decoding ambiguity. We also formulate conformal-block likelihood data and compute geometry-dependent Ising four-point weights. For growing code families, we prove a conditional Peierls-type threshold theorem: bounded connected-region growth, local stochastic noise, local neutralizability of small residual components, and componentwise decoder balance imply $\Pr_L(\mathrm{fail})\le C|\Omega_L|e^{-cL}$ below a nonzero constant error rate. We conclude with representation-theoretic and algebro-geometric directions involving tube and Hopf algebras, Yangian-type structures, Higgs bundles, spectral curves, Jacobians, and abelian varieties.

We study covert classical communication over quantum multiple-access channels (MACs) with general message sets. Specifically, we consider a fully quantum MAC with arbitrary message sets and an arbitrary number of transmitters. We demonstrate the feasibility of achieving a positive covert rate over this channel and establish general one-shot and asymptotic achievable rate regions. For classical-quantum MACs with general message sets, we establish the covert capacity, when the transmitters are restricted to deterministic encoding. Our result recovers, as a special case, known results for classical communication over classical MACs with general message sets, covert communication of a classical message over a classical channel with two transmitters, and classical communication over quantum MACs. We provide three examples of MACs to which our results can be applied, either directly or indirectly, to achieve positive covert rates. Specifically, we first study covert communication over a finite-dimensional MAC with a helper. We then analyze a classical Gaussian MAC with a helper and derive its covert capacity. Finally, we extend the analysis to a single-mode bosonic MAC with a helper and show that positive covert rates can also be achieved in this setting. To the best of our knowledge, this is the first work to achieve positive-rate covert communication over both classical and quantum MACs.

We report a distinct two-photon blockade (termed as weak TPB) discovered in the strong-coupling regime of a parity-broken generalized quantum Rabi model, in addition to the other two-photon blockade (termed as strong TPB) in the ultrastrong coupling (USC) regime. In contrast to the strong TPB conventionally with opened cascade decay channel, the weak TPB phase emerges unconventionally with closed cascade decay channel. The closure of cascade channel originally allowed to open by parity breaking is unexpected and its opening in the strong-TPB regime is actually delayed. We extract a cubic law for the cascade transition rate that accounts for the suppression of cascade channel and the delayed opening in the two TPB regimes. Furthermore, we find that the population on the cascade state, which is crucial in the strong TPB, contrarily plays a minor role in the weak TPB. Instead, it is the upper state above the cascade that is relevant for the weak TPB. We demonstrate that the interplay of weak anharmonicity and resonant driving is the primary mechanism responsible for the upper-state population and the formation of weak TPB. Our analyses not only provide deeper insights into the nature of different TPBs, but also imply mechanism and manipulation diversities for the multi-photon blockade, which may open more avenues for developing quantum technologies on the manipulation level of individual quanta.

Semidefinite programming hierarchies for commutative and non-commutative polynomial optimization represent a powerful computational tool with many applications in quantum information. In such applications, a given variable is typically not either commuting or non-commuting with all other variables, but instead commutes with some variables and does not commute with others, i.e., the variables satisfy some partial commutation relations. While such partial commutation relations can always be incorporated in a fully non-commutative setting through suitable linear constraints in the semidefinite programming relaxations, exploiting their algebraic properties from the onset can result in more compact relaxations. This leads us to introduce partially-commutative polynomial optimization, a framework that encompasses commutative and non-commutative polynomial optimization, allowing for arbitrary commutation relations among the variables. We point out that the underlying algebraic structure is that of a partially-commutative monoid. We present and review several key aspects of such monoids and show how they can be used to build SDP relaxations for partially-commutative polynomial optimization problems in which the partial commutations are natively implemented in the monomial structure, without the need of additional linear constraints.

Analyzing the dynamics of physical observables in open quantum many-body systems is a fundamental but highly challenging task that has yielded very few exact results. In this work, we identify intrinsic conformal structures that restore exact solvability in $(1+1)$D conformal field theories. For $N$ Majorana fermions with linear mode jumps, the adjoint Lindbladian is triangular on reduced even Majorana monomials, yielding recursive exact Heisenberg evolution. In Wess-Zumino-Witten models admitting conformal Majorana embeddings, this hierarchy gives exact dynamics of affine-current products realized as Majorana bilinears, including regimes where the Kac-Moody current algebra alone does not close. In diagonal rational conformal field theories, Verlinde topological defect lines furnish jump operators whose primary-sector dynamics is exactly diagonal: topological-charge probabilities are conserved, while intersector coherences dephase at rates fixed by the modular $S$ matrix and nonnegative measurement strengths. These examples show that intrinsic conformal structures, such as conformal embeddings and modular data, can organize exactly solvable open conformal dynamics.

Preparing massive particles in coherent spatial superpositions is a central objective of modern quantum science, motivated by applications ranging from fundamental tests of quantum mechanics and gravity to quantum-enhanced sensing. The experimental difficulty of realizing such superpositions is usually attributed to environmental decoherence mechanisms whose impact depends on the details of the experimental implementation. Here we identify a universal constraint arising from momentum conservation and the quantum nature of the preparation apparatus. Any protocol that places a particle of mass $m$ in a spatial superposition with separation $d$ necessarily entangles the particle with the center-of-mass degree of freedom of the apparatus responsible for the splitting. The resulting recoil displacement, fixed by conservation of the mass dipole moment, reduces the coherence of the particle when the apparatus is not included as part of the quantum system. For an apparatus of mass $M$, preserving coherence requires the recoil displacement to remain smaller than the coherence length of the apparatus center-of-mass state, leading to a quantitative bound expressible as a constraint on its temperature and on how rigidly the apparatus is anchored to the laboratory frame. We analyze the implications of this bound for current matter-wave interferometry experiments, proposals for gravitationally mediated entanglement, and tests of quantum mechanics near the Planck scale. Our main result, which may seem counterintuitive, is that the recoil of even heavy macroscopic apparatuses can pose a surprisingly strong constraint on the coherence of spatial superpositions of particles with masses well below the Planck mass. Finally, we discuss why the resulting limitation should be regarded as a conservative estimate and under which conditions it can be interpreted as an instance of false decoherence.

A recent article [Science 388, 199-204 (2025)] investigates the applicability of numerical methods and a quantum processor unit in simulating a quantum annealing protocol. One of the findings indicates that Neural Quantum States - a versatile variational ansatz for the many-body wave function based on artificial neural networks - fail to reach the same accuracy as the quantum processor. In this comment we revisit these concerns, demonstrating that NQS can provide competitive results in some of the cases when accounting for the Monte-Carlo noise and large autocorrelation times between samples obtained from the final state.

Hardware teams building fault-tolerant quantum computers (FTQCs) must decide which imperfections to suppress, and that decision requires the logical performance of the architecture under the device's actual noise. Hardware noise often departs from the stochastic Pauli models used by scalable stabilizer simulators: superconducting transmons leak out of the computational subspace, neutral atoms scatter through intermediate states, trapped ions heat as their motional modes absorb phonons, and miscalibrated controls over-rotate coherently. We present Plaquette, a theoretical framework and software suite that computes the logical performance of fault-tolerant architectures directly from the physics of such imperfections. In Plaquette, a hardware error model is specified once, as Kraus operators, Hamiltonian-Lindblad dynamics, or an experimentally reconstructed quantum channel, and is compiled automatically into the exact or approximate representation required by each of four sampler classes: stabilizer sampling for Pauli noise, the new XPauli sampler for leakage and environment sectors, near-Clifford samplers for coherent errors, and full-state simulation for exact reference calculations. We validate the XPauli and near-Clifford samplers against full-state simulation, which they can match within statistical uncertainty while Pauli twirling can fall short depending on the error model. We demonstrate the framework on three error models: leakage in superconducting qubits, intermediate-state scattering in neutral atoms, and heating in trapped ions. The size of the discrepancy between Plaquette and Clifford-only simulations varies with platform and noise process, so reliable thresholds, error budgets, and overhead estimates require the most accurate simulation available. Plaquette provides a direct path from the open-system physics of a device to the logical performance of the FTQC built on it.

Phenomena that slip beyond the grasp of our classical intuition reveal uniquely quantum effects that deepen our understanding of the physical world and enable advances in information processing, particularly in quantum communication and computation. One such phenomenon is quantum steering, whereby measurements performed by one party influence the conditional states of another when the two share an entangled quantum system. If the observed correlations cannot be explained by a local hidden state model, the state is said to be steerable. In this work, we investigate the typicality of this behavior: given a generic two-qubit state and $m$ Haar-random projective measurements, what is the probability of observing steering? We derive analytical expressions for the steering probability $\mathcal{P}_S$ of Werner states in two- and three-setting scenarios, the latter restricted to coplanar projective measurements on the Bloch sphere. For larger numbers of settings and various random states ensembles, we perform numerical analyses showing that $\mathcal{P}_S$ increases systematically with the number of measurements and substantially exceeds the corresponding probabilities associated with Bell nonlocality. Our results demonstrate that random states with minimal environmental coupling exhibit a high probability of steering for finite $m$ and approach genuine typicality, $\mathcal{P}_S=100\%$, as the number of settings increases. We provide a detailed characterization of $\mathcal{P}_S$ across different state ensembles and specific families, including Bell-diagonal and Werner states, identifying those with the greatest non-classical potential and highlighting their relevance for protocols in which steering serves as a key resource.

Programmable quantum simulators are beginning to access correlators of increasing complexity, ranging from four-point out-of-time-ordered correlators to even higher-order many-body correlators. The theoretical framework for interpreting such data, however, remains comparatively underdeveloped. Although a variety of higher-order correlators can be constructed straightforwardly, their physical meaning is often difficult to infer. A further complication is that different correlators are generally not independent: some may be mutually redundant, while others may encode genuinely distinct information. These features make it necessary to analyze correlators not as isolated quantities, but as a structured family. In this work, we develop a geometric framework for the collective analysis of higher-order correlator families. By representing correlators as inner products between operator words, we recast each family as a geometry in operator space. The key idea is to introduce conditioning subspaces that separate this geometry into reducible information, already explained by a chosen resolved sector, and irreducible information, encoded in the residual correlator geometry. Focusing on the latter component, we define irreducible volume profiles that quantify how broadly the unexplained part of a correlator family spreads over independent geometric directions. This perspective leads to several complementary forms of conditioning. Canonical conditioning optimally explains a correlator family. Targeted conditioning fixes the resolved sector to isolate a chosen physical feature. Krylov and cross conditioning extend the framework from a single correlator family to comparisons among correlator geometries. Our framework reveals irreducible structures hidden at the level of individual correlator values and establishes correlator geometry as a higher-level description of quantum many-body dynamics.

In this paper we give a systematic and unified treatment and extensions of various results on a new notion of quantum $f$-divergences defined from quantum hockey stick divergences, the theory of which has been developed recently in \cite{BHT_fdiv,HircheTomamichel_integral,LiuHircheCheng2025}. In particular, we consider non-normalized states and hockey stick $f$-divergences defined from more general notions of quantum hockey stick divergences, as well as a somewhat more general form of the integral representation defined in terms of an additional real parameter. We also consider the extension of the theory to general von Neumann algebras, and extend various results from \cite{HircheTomamichel_integral,LiuHircheCheng2025} to this setting. Our main results here are the representation of the hockey stick $f$-divergences in terms of Neyman-Pearson error probabilities, which was given in the finite-dimensional case in \cite{LiuHircheCheng2025}, an extension of Jen\v cová's result \cite{Jencova2023} on the detection of reversibility of a quantum channel on a pair of states in terms of the hockey stick divergences, and an extension of a result in \cite{HircheTomamichel_integral} showing that the regularized hockey stick Rényi $\alpha$-divergences coincide with the Petz-type Rényi divergences for $\alpha\in(0,1)$ and with the sandwiched Rényi divergences for $\alpha>1$. Moreover, we give some partial results on the characterization of when different notions of quantum $f$-divergences give the same value on a pair of quantum states.

Whether a heat engine can approach Carnot efficiency while maintaining finite power is a fundamental question in finite-time thermodynamics. For classical Markovian heat engines with local interactions, the power-efficiency trade-off forbids an asymptotic approach to Carnot efficiency at finite power. In quantum systems, by contrast, degeneracy, symmetry, and collective jumps have been theoretically predicted to enable such an asymptotic attainment by enhancing activity. It has remained open, however, whether this mechanism can be realized in an experimentally implementable heat engine. In this Letter, we propose a superconducting-circuit heat engine that emulates the collective enhancement, thereby enabling an asymptotic approach to Carnot efficiency at finite power. This result demonstrates that, in an implementable model, such an enhanced dissipative mechanism circumvents the power-efficiency trade-off of classical Markovian engines. Our work connects abstract bounds in finite-time thermodynamics to a concrete circuit-QED platform and suggests a route toward quantum-device design based on collectively enhanced dissipative processes.