Papers

We propose a quantum-resistant quantum teleportation (QRQT) framework protected by post-quantum cryptography (PQC) to secure the classical correction channel, which is vulnerable to quantum adversaries. By applying PQC to the classical control bits, QRQT eliminates the classical attack surface of quantum teleportation. Our analysis reveals that quantum memory is a hidden bottleneck linking physical and computational security: its finite coherence time simultaneously limits communication distance, constrains tolerable PQC overhead, and restricts the adversary attack window. Under realistic parameters (1 ms coherence, fiber-optic propagation), the maximum secure teleportation distance ranges from 191 km (FrodoKEM-1344) to 199 km (Kyber512). We show that the joint classical-quantum attack probability exhibits a non-monotonic, Bell-shaped profile due to the opposing time dependencies of classical cryptanalysis and quantum decoherence, establishing a bounded optimal attack window beyond which adversarial success decays exponentially. We further analyze how leakage of classical correction bits affects teleportation security under four stochastic leakage models: independent exponential, sequential, burst, and correlated leakage, also accounting for amplitude damping on the shared Bell pair. For each scenario, we derive closed-form expressions for the average Holevo quantity and teleportation fidelity as functions of time, providing measurement-independent upper bounds on extractable information and guiding the design of leakage-resilient quantum communication protocols.

Distance-bounding (DB) protocols let a verifier upper-bound a prover's physical distance by timing rapid challenge-response exchanges. Quantum communication promises simpler DB protocols with stronger security guarantees, yet existing quantum distance-bounding (QDB) proposals are analysed in ad-hoc models and, to the best of our knowledge, lack a common game-based treatment of standard fraud attacks. We contribute (i) a reusable security framework for QDB that fixes system and timing assumptions, specifies a quantum-capable adversary model, formalises distance-, mafia-, and terrorist-fraud experiments, and includes a simple i.i.d. depolarizing noise model; and (ii) an application of this framework to a published QDB protocol. For this protocol we characterise the honest per-round acceptance probability under noise and lift it to the multi-round setting, yielding explicit completeness guarantees as a function of the number of fast rounds, the acceptance threshold, and the noise parameter. For active adversaries we bound the per-round success probability of distance-fraud attacks and analyse the best known mafia-fraud strategy, deriving corresponding multi-round soundness bounds. We also show that the protocol is inherently insecure against terrorist-fraud in our model. The framework cleanly separates protocol-independent definitions from protocol-specific analysis and can be used to evaluate existing and future QDB protocols on a common basis.

We point out that the method presented in a recent arXiv article by Jia et al. (arXiv:2512.21848) for constructing local hidden-state models closely follows the framework we developed in N. von Selzam & F. Marquardt (PRX Quantum, 2025) for constructing local hidden-variable models. While Jia et al. cite our work, the extent of the methodological overlap and the degree of textual similarity are not adequately reflected by the attribution given. We document this overlap in detail.

This paper develops a MacWilliams-type enumerator theory and semidefinite programming bounds for intrinsic quantum codes. An intrinsic code is a subspace $\mathcal{C}\subset V$ of a finite-dimensional unitary representation $V$ of a group $G$, with errors organized into the isotypic components of $\mathscr{L}(V)$ under conjugation. The Knill-Laflamme conditions persist under every $G$-equivariant realization of $V$, aallowing intrinsic bounds to apply across diverse physical encodings. Without a group action, we first construct projector and twirl quadratic enumerators for an arbitrary orthogonal decomposition of $\mathscr{L}(V)$, analogous respectively to the $A$ and $B$ enumerators of Shor-Laflamme theory. These enumerators satisfy positivity, normalization, and Knill-Laflamme inequalities, but the two superoperator families need not span the same space and therefore admit no canonical duality. A group action places their generalized versions in the same equivariant intertwiner algebra, and the change of basis between them defines the intrinsic MacWilliams transform. When the conjugation representation has multiplicities, this algebra is noncommutative and the enumerators are matrix-valued; positivity and detection are encoded by Loewner-order constraints, making the natural programming bound a semidefinite program. The multiplicity-free specialization recovers the known scalar MacWilliams transform and linear-programming framework. For symmetric powers, intrinsic depth equals ordinary qudit distance, yielding extremality certificates for permutation-invariant qudit codes. Finally, for the $27$-dimensional $\mathrm{SU}(3)$ representation $V=(2,2)$, an exact dual certificate proves the sharp bound $K\leq5$ for codes detecting the full adjoint isotypic component. An explicit $5$-dimensional code attains the bound and, under the canonical embedding, gives a four-qutrit distance-$2$ code.

Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent advances in variational autoencoders to demonstrate that such models can learn physically meaningful and interpretable representations from a broad class of unlabeled quantum datasets. From raw measurement data alone, the learned representation reveals rich information about the underlying structure of quantum phase diagrams. We further augment the learning pipeline with symbolic methods, enabling the discovery of compact analytical descriptors that serve as order parameters for the distinct regimes emerging in the learned representations. We demonstrate the framework on experimental Rydberg-atom snapshots, classical shadows of the cluster Ising model, and hybrid discrete-continuous fermionic data, revealing previously unreported phenomena such as a corner-ordering pattern in the Rydberg arrays. These results establish a general framework, which constitutes an important step toward the autonomous discovery of physical descriptors from diverse quantum datasets. All methods are available through qdisc, an open-source Python library designed to make these tools accessible to the broader community.

Quantum technologies promise a radically new way to solve classically intractable computing problems. Superconducting circuits as a platform are at the forefront of this field. The cryogenic operation temperatures of superconducting circuits however impose challenges for the further scaling to many connected quantum information processing units into a local area or global network. In this work, we present a hardware solution for connecting quantum devices operating at microwave frequencies into local area networks, which enable the exchange of quantum information between spatially separated parties. Specifically, we demonstrate a modular system spanning distances of 5, 10 and 30 meters operated at cryogenic temperatures and connecting two superconducting circuit systems, located in individual dilution refrigerators, through a quantum communication channel. We develop a thermal model to evaluate the heat transfer processes in the setup, optimize the design and select appropriate materials for its construction. The assembled 30-meter-long system achieves operating temperatures of below 50 mK after a cooldown time of about six and a half days. This link enables the execution of distributed quantum computing and communication algorithms. It also adds the resource of non-locality, certified by a loophole-free Bell test, to the field of quantum science and technology with superconducting circuits.

While externally driven information engines are well understood, the thermodynamic constraints of their autonomous counterparts remain an open question. Here, we investigate the finite-time operation of an autonomous machine functioning as both an information eraser and a refrigerator, revealing that its irreversibility is bounded by the transient information geometry. Beyond steady-state boundaries, we map the landscape of optimal operation times across both functional modes, uncovering a unique synergistic regime where erasure power $P$ and efficiency $\eta$ increase simultaneously. Fundamentally, this performance is governed by a trade-off relation, $v(1-\eta)P/\eta \le D$, where $v$ is the operational speed and $D$ denotes an information-geometric distance. Our findings pave the way for optimizing fast autonomous information-energy conversion.

Technological progress towards next-generation electronics critically relies on achieving faster switching with reduced energy consumption. Because device operation speeds are fundamentally constrained by the intrinsic properties of constituent materials, identifying systems with inherently superior switching capabilities is essential. Here, we propose that semimetallic systems characterized by non-trivial quantum geometry, including quadratic band-touching semimetals and singular flat bands, can serve as a promising platform for ultrafast switching at voltages compatible with modern electronics. We show that, in such quantum geometric semimetals, an electric current is generated instantaneously upon application of a moderate external electric field, reaching its steady-state value. As a consequence, the current exhibits rapid and stable on-off switching behaviour under periodic optical pulse trains, demonstrating robustness under experimentally feasible conditions. In terms of switching speed, this quantum geometric semimetal outperforms conventional metals, semiconductors, and graphene. We identify the microscopic origin of this behaviour as interband coupling governed by the Hilbert-Schmidt quantum distance, together with a finite density of states at the band-touching point. This mechanism further leads to a universal classification of conductivity for both gapless and gapped quantum geometric semimetals. Finally, first-principles calculations suggest realistic material platforms, including bilayer graphene, cyclic graphene, monolayer bismuth and V3F8-in which the predicted instantaneous current switching can be directly realized, further supported by time-dependent density functional theory simulations performed for representative systems.

We present two general methods to implement quantum circuits for the direct measuring of local unitary invariants on quantum computers. We work these out for important three-qubit invariants, and also demonstrate these on the IBM Quantum Platform for important entanglement measures of three qubits.

Rydberg atoms, which possess exceptionally large electric dipole moments, offer a promising route for electric field sensing as well as metrology traceable to the International System of Units (SI); however, current research predominantly focuses on the microwave (MW) regime, leaving the quasi-direct current (quasi-DC) and low-frequency bands, ubiquitous in power systems, largely unexplored. In this paper, we present a theoretical investigation into low-frequency electric field detection. To this end, we establish a comprehensive modeling framework incorporating Fisher information (FI) and the Cramér-Rao lower bound (CRLB) to quantify the fundamental precision limits of electromagnetically induced transparency (EIT) readouts. Building upon this framework, we propose a linearized sensing strategy utilizing a DC-biased two-point differential measurement. Numerical validations demonstrate that this approach effectively mitigates the weak-field insensitivity for both DC and AC fields, achieving a CRLB-limited sensitivity bound of approximately $1\times 10^{-4}$ V/m/$\sqrt{\text{Hz}}$. Furthermore, to surpass the single-pass sensitivity limit, we introduce a Fabry-Pérot (FP) cavity-enhanced configuration. This architecture leverages intracavity phase modulation to significantly steepen the transmission slope, boosting the FI by over two orders of magnitude compared to standard free-space configurations. This work provides a rigorous theoretical basis and design guidance for the high-precision quantum monitoring of electromagnetic environments in smart grids.

Flux-tunable superconducting qubits rely on fast flux control pulses to implement two-qubit entangling quantum gates, a key building block for quantum algorithms. However, distortion effects introduced by non-ideal control electronics, parasitic components, and the cryogenic quantum chip response can all degrade the gate fidelity. We present a digital predistortion (DPD) framework for characterizing and then compensating for these distortions using a combination of infinite impulse response (IIR) and finite impulse response (FIR) filters. Experiments on a flux-tunable quantum processing unit (QPU) demonstrate a successful correction of step-response distortions on the flux-control line, with a compensated control signal showing only sub-percent deviations from the ideal target linear behavior. The demonstrated method enables automated rapid calibration of flux control channels for superconducting QPUs.

Neural-network variational Monte Carlo (NNVMC) has emerged as a powerful tool for solving quantum many-body problems, yet systematic pathways for improving its accuracy remain largely heuristic. Here, we introduce a physically motivated basis transformation for NNVMC that enhances variational expressivity without increasing the complexity of the neural-network ansatz itself. By formulating the many-body wave function in a Gaussian basis, we introduce a single learnable locality parameter, $\alpha$, that reshapes the target ground state into a more learnable representation. This approach introduces minimal computational overhead and can be readily combined with existing neural-network architectures. Using the three-dimensional homogeneous electron gas as a benchmark, we show that the optimized basis transformation consistently lowers the variational energy for both FermiNet and message-passing neural-network architectures. Notably, for the latter, it enables a more precise determination of the Fermi liquid to Wigner crystal phase transition. More broadly, our results highlight basis transformation as a new route to improving NNVMC in continuous space, showing that accuracy can be enhanced not only by refining the ansatz but also by making the target ground state easier to represent.

Grover's algorithm is a cornerstone of quantum algorithms and is strictly optimal in oracle-query complexity. While the full search problem admits no further improvement, one may trade accuracy for speed in the partial search problem, where the task is to identify only the block containing the target item. The best known quantum algorithm for the partial search problem is the Grover-Radhakrishnan-Korepin (GRK) algorithm, whose optimality has long been conjectured but not proved. In this work, we prove the optimality of GRK in the large-block limit. We formulate partial search as a time-optimal control problem and apply the Pontryagin maximum principle to derive the switching-function dynamics, establish the bang-bang structure of regular extremals, and exclude non-optimal switching patterns. As a result, we show that the optimal regular extremal has the global-local-global form, which yields a control-theoretic proof of the asymptotic optimality of the GRK algorithm in oracle-query complexity.

The Hadamard test is a standard quantum primitive for estimating inner products and expectation values, but in data-processing settings its practical utility is often limited by the cost of preparing amplitude-encoded quantum states. In this study, we investigate an angle-encoding variant of the Hadamard test for estimating cosine similarity between normalized real-valued vectors. The proposed method decomposes the similarity computation into elementwise two-qubit Hadamard-test circuits that can, in principle, be executed in parallel, resulting in constant circuit depth with respect to the vector dimension at the expense of a larger qubit footprint and classical post-processing. Because the resulting estimator is approximate, we analyze the induced bias and show that it is non-negative under the approximation used in our derivation. Numerical experiments on random normalized vectors show that, in the tested setting, the estimation error decreases as the vector dimension increases. We further illustrate a possible application to cosine-attention-based Transformer models. These results suggest that the angle-encoding Hadamard test may provide a useful design point for near-term similarity estimation when shallow circuit depth is preferred over compact qubit usage.

Weber's conjecture (1886) governs three aspects of lattice-based cryptography: the solvability of the Principal Ideal Problem, the freeness of modules over rings of integers, and the tightness of worst-case-to-average-case reductions in Ring-LWE (R-LWE) and Module-LWE (MLWE). Existing verifications for $k \ge 9$ rely on Generalized Riemann Hypothesis (GRH). In this paper, we present the first unconditional proof for $k \le 12$. Our method combines the Fukuda-Komatsu computational sieve, inductive structure of the cyclotomic $\mathbb{Z}_2$-tower, and Herbrand's theorem.

Semi-quantum signature (SQS) schemes aim to enable quantum signature functionality in scenarios where only a subset of participants possess full quantum capabilities, thereby improving practical deployability while preserving quantum security advantages. Within this framework, we present a practical SQS protocol based on Bell states. The protocol is designed so that only the signer requires full quantum capability, significantly alleviating the quantum burden on the remaining participants. To strengthen security in semi-quantum environments, we incorporate an improved eavesdropping-detection mechanism that more effectively detects tampering. Compared with many existing schemes, which do not explicitly consider tampering of already generated signatures in their unforgeability analyses, the proposed protocol is designed to remain secure in the presence of such tampering.

The Hamiltonian formulation of lattice gauge theories plays a central role in quantum simulations of gauge theories, and understanding their spectrum and other properties is expected to become crucial in the upcoming years. The relevant Hamiltonians in this framework possess local symmetry at each lattice site and may exhibit higher-form symmetries. There are then an exponentially large number of dynamically disconnected symmetry sectors, most of which are not translation-invariant. An exponential number of dynamically disconnected sectors, i.e., Hilbert space fragmentation, can also occur in systems in which no such symmetries have been identified. In this contribution, we describe an emergent gauge symmetry that is valid only in a subset of sectors of the fragmented $S=1$ dipole-conserving spin chain. These non-invertible symmetries can label exponentially many of the model's sectors. Simulating this Hamiltonian, which is not gauge-invariant, yields an exact quantum simulation of a gauge theory.

Enhanced local-excitation retention in atomic arrays allows to exploit cooperative radiative effects to suppress emission and prolong excited-state lifetimes. We consider an impurity-assisted setting involving a single storage atom being initially excited and study the survival of local excitation under neither write nor retrieval fields. Because the corresponding dynamics can involve multiple interfering collective modes, the survival dynamics cannot determined from the smallest collective decay rate alone. Thus, using a biorthogonal eigenmode decomposition of an effective non-Hermitian Hamiltonian, we show that the survival dynamics are jointly governed by the decay rates of the eigenmodes and their overlaps with the initial excitation. Large oscillations occur when multiple long-lived modes have comparable weights. Accordingly, we introduce a physically motivated spectral surrogate objective that favors both small weighted decay rates and an initial-state weight concentrated on a single subradiant mode. As a proof of principle of this spectral design, we apply the surrogate to constrained atom-position optimization under minimum-distance constraints and obtain nontrivial aperiodic configurations with enhanced local-excitation retention. Our findings unveil spectral design principles for local-excitation retention in impurity-assisted atomic arrays and provide a proof of principle for their inverse design.

We compute the efficiency of the reversible Stirling engine, with and without regeneration, for a broad class of working substances including Van der Waals fluids, quantum ideal gases (Bose and Fermi), Bose-Einstein condensates, thermal conformal field theories (CFTs), and holographic CFTs. Regeneration acts as an internal heat recycling mechanism that enhances efficiency by reducing the net heat exchange with external reservoirs. For regenerative Stirling cycles, we identify a general sufficient condition for attaining Carnot efficiency: at fixed conserved charges, the fixed-volume heat capacity is independent of volume. This ensures that, at each temperature along the isochores, the heat released during cooling equals the heat absorbed during heating. An ideal regenerator can therefore store and return this heat reversibly, returning to its initial thermodynamic state with no net change in energy or entropy. While this condition is satisfied for classical ideal gases and Van der Waals fluids, it is violated for quantum ideal gases and CFT working substances. For thermal CFT states dual to AdS-Schwarzschild and AdS-Reissner-Nordstrom black holes, we obtain exact expressions for the Stirling efficiency. In the fixed-potential ensemble, both the regenerative and non-regenerative efficiencies approach the Carnot value in the large-potential limit. Regeneration reduces the leading deviation from the Carnot value, although both efficiencies have corrections of the same asymptotic order.

Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and accurate. To tackle this issue and cater to the learning requirements, we envision using a federated learning framework with a quantum-enhanced model. Specifically, we design a hybrid quantum-classical long-shot-term-memory model (QLSTM) for local training at distributed nodes. It combines the representative power of quantum models in understanding complex relationships within the feature space, and an LSTM-based model to learn necessary correlations across data points. Given the computing limitations and unprecedented cost of current stand-alone noisy-intermediate quantum (NISQ) devices, we propose to use a federated learning setup, where the learning load can be distributed to local servers as per design and data availability. We demonstrate the benefits of such a design on a classification task for the Supersymmetry(SUSY) dataset, having 5M rows. Our experiments indicate that the performance of this design is not only better that some of the existing work using variational quantum circuit (VQC) based quantum machine learning (QML) techniques, but is also comparable ($\Delta \sim \pm 1\%$) to that of classical deep-learning benchmarks. An important observation from this study is that the designed framework has $<$300 parameters and only needs 20K data points to give a comparable performance. Which also turns out to be a 100$\times$ improvement than the compared baseline models. This shows an improved learning capability of the proposed framework with minimal data and resource requirements, due to the joint model with an LSTM based architecture and a quantum enhanced VQC.

Classical simulation of quantum operations is essential for algorithm design, noise characterisation, and benchmarking of quantum hardware. The most general physically realisable operation can be described by a positive linear map acting on a hermitian operator, representing either a density matrix or an observable. Established simulators vectorise the density matrix on an $n$-qubit Hilbert space and reuse state-vector kernels, storing all $2^{2n}$ elements and forgoing the benefits of hermitian symmetry. In this work, I introduce Orkan, a simulation library that uses a tiled memory layout storing only the lower triangle of the hermitian matrix at tile granularity, roughly halving both the memory footprint and the wall time to simulate the evolution of quantum states under generic quantum operations. The implementation treats any hermitian operator uniformly and is agnostic to whether the Schrödinger or Heisenberg picture is used. Dedicated $k$-local conjugation algorithms update all entries of the hermitian matrix in a single pass. Benchmarks against Qiskit Aer, QuEST, and Qulacs show consistent wall-clock speedups of $2$-$4{\times}$ partly attributable to the reduced memory footprint.

A broadband electromagnetic source is important for scientific and technological applications. Quantum vacuum fluctuations, which manifest most prominently in the Casimir effect, provide a fundamentally broadband electromagnetic source. Here we explore a potential consequence of the broadband nature of quantum vacuum fluctuations, by showing that such fluctuations can enable measurement of material permittivity over a broad frequency range. Specifically, we consider the Casimir force in a parallel-plate geometry, with one plate covered by a nanoscopic thin film. Using a machine learning approach, we show that one can infer both the thickness of the film and its permittivity over a broad frequency range, starting from the dependency of the Casimir forces on the spacing between the two plates. Our work highlights the application potential of using vacuum fluctuations as a naturally-existing broadband electromagnetic source for material characterization, and shows that the inverse problem in Casimir force calculation can be solved with machine learning.

We demonstrate the use of two-photon excitation for observing the ground state optically detected magnetic resonance (ODMR) of nitrogen-vacancy centers in diamonds at room temperature. An ultrafast femtosecond laser at 1040 nm was used for excitation, while fluorescence signal read out was achieved through a combination of a PMT and a lock-in amplifier. The imaging capability of two-photon excitation fluorescence (2PEF) was utilized to map the distribution of NV centers in a bulk diamond and micro-sized diamonds. For the first time, ODMR traces of the nitrogen-vacancy center are observed with two-photon excitation, providing a promising tool for fast 3D quantum sensing and imaging.

The geometry of quantum states has profound implications in quantum multiparameter estimation. While the Riemannian structure of quantum state space is well understood, the full understanding of the curvature structure of mixed quantum states is still an open problem. Inspired by the Yang-Mills action in non-Abelian gauge theory, we propose a scalar quantifying the Uhlmann curvature and establish its connection to the measurement incompatibility in quantum multiparameter estimation problems. We show that this curvature measure is gauge invariant, reparametrization invariant, and vanishes if and only if the Uhlmann curvature vanishes. We also explicitly calculate the Uhlmann curvature for the joint estimation of phase and phase diffusion as an example.

To study generator design for parameterized unitaries in quantum machine learning (QML), we propose an observable-guided generator selection algorithm for $ n $-qubit Pauli-string generator pools. The proposed method selects generators based on two criteria: maintaining large first-order sensitivity in the gradients and suppressing second-order interference in the Hessian matrix. Under a restricted setting with Pauli-string observables and candidate generators, the selection problem can be formulated as a binary optimization problem that favors mutually anti-commuting generators. Numerical experiments on a synthetic dataset with a small-scale five-qubit circuit show that the selected generators yield faster training than random generator selection in our setting, while exhibiting similar expressibility. Furthermore, under additional algebraic assumptions, the proposed criteria admit an interpretation in terms of the $ \mathfrak{g} $-purity of the observable: the first-order sensitivity is proportional to the $ \mathfrak{g} $-purity, whereas the second-order interference, namely the off-diagonal elements of the Hessian matrix, is upper-bounded by it. These results suggest that observable-guided generator selection is a promising direction for improving trainability in restricted QML settings.

Hybrid variational quantum algorithms are promising for solving practical problems, such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. However, variational quantum algorithms (derived from randomized hardware-efficient ansatz or adaptive ansatz) become a black box, not trustworthy for model interpretation, and not to mention for application deployment in informing critical decisions. In this paper, we construct the first interpretable quantum regression algorithm, in which the quantum state exactly encodes the classical data table and the variational parameters correspond directly to the regression coefficients, which are real numbers by construction, providing a high degree of model interpretability and minimal cost to optimize due to the right expressiveness. We also exploit the encoded data structure to reduce the gate complexity of computing the regression map. To reduce circuit depth in nonlinear regression, our algorithm can be extended by directly constructing nonlinear features via classical preprocessing, such as independent encoded column vectors. By design, the model performance is determined by the cost function measurement results $\mathcal{C}$ synchronous to the mean squared errors (MSE) for the regression models. We derived the read-out errors induced by one-hot encoding and compact encoding; the required physical qubit resources are exponentially compressed for the compact encoding to be favorable for noisy quantum devices. We also derive the cost function dependent sample complexity $ \in \mathcal{O}\left(\sigma^{2}(\mathcal{C}) \ln (1/\alpha)/\epsilon^{2}\right)$ under the error budget $\epsilon$ and confidence tolerance $\alpha$.

GaAs quantum dots grown by droplet etching epitaxy are high-quality solid-state sources of quantum light. Despite implementation in devices that exploit quantum phenomenon, a comprehensive review on the crystal growth of quantum dots grown by droplet etching epitaxy is absent, unlike for other quantum dot growth techniques such as the related droplet epitaxy method or Stranski-Krastanov growth of InAs quantum dots. This review presents a detailed overview of the droplet etching epitaxy growth technique in the molecular beam epitaxy environment, with emphasis on the growth parameters necessary to realize high-quality quantum dots. We systematically cover the three main phases of droplet etching epitaxy - droplet deposition, droplet etching, and nanohole regrowth - and relate experimental results to theories on crystal growth. The review concludes with an introduction to GaAs quantum dot photoluminescence and the extension of droplet etching epitaxy beyond the AlGaAs/GaAs material system.

We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow. The framework enables systematic evaluation of PINN performance across benchmark problems including 1D hyperbolic conservation laws, incompressible flows, and electromagnetic wave propagation. It supports a range of architectural and training enhancements, including Fourier feature embeddings, random weight factorization, strict boundary condition enforcement, adaptive loss balancing, curriculum training, and second-order optimization strategies, with extensibility to additional methods. We provide a comprehensive benchmark study quantifying the impact of these methods on convergence, accuracy, and computational cost, and analyze distributed data parallel scaling in terms of runtime and memory efficiency. In addition, we extend the framework to hybrid quantum-classical PINNs and derive a formal estimate for circuit-evaluation complexity under parameter-shift differentiation. Results highlight the sensitivity of PINNs to architectural and training choices, confirm their high computational cost relative to classical solvers, and identify regimes where hybrid quantum models offer improved parameter efficiency. QPINNACLE provides a foundation for benchmarking physics-informed learning methods and guiding future developments through quantitative assessment of their trade-offs.

We introduce a Hybrid Quantum Residual Network (HQRN) and establish an exact functional correspondence between its state evolution and the dynamics of classical networks with residual connections. When inputs are restricted to the computational basis, the HQRN reduces to its classical analog, enabling the direct translation of optimized classical weights into quantum unitary operations, effectively inheriting the landscape benefits of classical optimization. Conversely, when processing general mixed states, the HQRN leverages off-diagonal quantum correlations to resolve features inaccessible to its classical analog. We validate this framework through digit recognition and bipartite entanglement classification. Notably, HQRN achieves high classification accuracy even for adversarial separable states that mimic the marginal measurement statistics of entangled pairs. Our results bridge the gap between classical and quantum residual learning, paving a scalable pathway for deep quantum architectures.

We investigate quantum thermal state preparation algorithms based on system-bath interactions and uncover a surprising phenomenon in the weak-coupling regime. We rigorously prove that, if the system-bath interaction is engineered so that the transition part of the approximate Lindbladian generator satisfies the Kubo–Martin–Schwinger (KMS) detailed balance condition, then the unique fixed point of the dynamics can be made arbitrarily close to the Gibbs state in the weak-coupling limit, regardless of the structure of the Lamb shift term. Importantly, this remains true even when the approximate Lindbladian differs substantially from the ideal Davies generator and the Lamb shift term does not commute with the thermal state. Our result shows that the role of the KMS detailed balance condition extends well beyond standard Lindbladian dynamics, serving as a general principle for a broader class of dissipative systems. Furthermore, by combining this with a general perturbation framework, we bound the mixing time of the dynamics and establish an end-to-end complexity of $\mathcal{O}(\varepsilon^{-1})$ for Gibbs state preparation. These guarantees apply to any Hamiltonian whose associated KMS-detailed-balance Lindbladian is known to be fast mixing.