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Shi-Xin Zhang

Publications and source records attributed to Shi-Xin Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Entanglement Embezzlement from Diffusive Hydrodynamics

Entanglement embezzlement asks how much entanglement can be borrowed from a many-body state by local operations and classical communication while returning that state with a small error. We uncover a conservation-law mechanism that redistributes the dominant probability mass of the Schmidt spectrum in the logarithmic Schmidt-rank coordinate and thereby controls this operational resource. For typical random pure states at fixed U(1) charge, we prove a finite-error conversion law: away from half filling, the charge bias converts $O(\sqrt L)$ charge fluctuations into $O(\sqrt L)$ borrowable entanglement, whereas particle--hole symmetry at half filling removes this contribution entirely. We then show how the resource develops dynamically in charge-conserving random circuits. Combining hydrodynamic analysis with large-scale replica tensor-network calculations, we find that diffusion broadens the operationally relevant distribution in the logarithmic Schmidt-rank coordinate on the scale $t^{1/4}$ and increases the amount of entanglement that can be borrowed. Charge transport therefore continues to reorganize the entanglement spectrum and activate embezzlement after the leading volume-law entropy has saturated.

quant-ph↗
arXiv2026

Critical Touching of Temporal Entanglement Transitions

Equilibrium phase transitions are conventionally categorized into first-order and continuous phase transitions. Far from equilibrium, many new transitions emerge during the real-time evolution of quantum systems. One such transition is the temporal entanglement transition (TET) characterized by the nonanalyticity of the entanglement spectrum. So far, all TETs occur through a linear crossing of the leading Schmidt levels in different symmetry sectors, resembling a first-order transition in equilibrium. A natural question is whether a continuous TET, featured by entanglement spectrum touching, is possible. In a periodically driven transverse $J_1$-$J_2$ Ising chain, we show that two such TETs can merge into a critical touching, where the leading levels meet tangentially without exchanging, realizing the temporal analog of a continuous phase transition. Near the critical frequency, the temporal separation of the two TETs vanishes continuously and its derivative with respect to frequency diverges, a nonanalytic signature that is absent in a single first-order TET. This finite-frequency touching arises from the interplay between a weak symmetry-preserving perturbation of the product initial state and Floquet corrections. Further extending the frequency scan reveals a second critical touching at a higher frequency, and the two critical frequencies enclose a finite window with no TET. These features can be understood from a second-order Floquet Hamiltonian and persist across a broad range of coupling ratios, establishing the critical touching as a distinct form of TET.

cond-mat.stat-mech↗
arXiv2026

Entanglement Growth as Transport Across Schmidt Scales

Quantum entanglement growth is commonly summarized by a single entropy, obscuring where correlations reside in the exponentially large Schmidt spectrum and how they form. Here, we introduce Schmidt-scale concentration and dominant Schmidt scale, two coordinates that locate the probability maximum across logarithmic windows in ordered Schmidt-rank space. Applied to quenches of a random-field spin chain, these coordinates distinguish rapid transport of the dominant scale to higher Schmidt rank at weak disorder from strongly suppressed transport despite continued logarithmic entropy growth at strong disorder. The disorder-averaged dynamics exhibit an ordered hierarchy: entropy production peaks first, spectral roughness and exact nonlocal magic peak next, and dominant-Schmidt-scale transport becomes typical only after a substantial delay. Moreover, a solvable head--tail model and controlled numerical experiments reveal the physical origin of this hierarchy: the spectral path determines the order of events, local dynamics on active exchange bonds set their early timing, and intra-subsystem many-body dressing further delays dominant-Schmidt-scale transport. These results establish the Schmidt-scale coordinates as powerful dynamical probes for uncovering fine-grained entanglement structures distinguishing entanglement production, entanglement-spectrum reorganization, and dominant-Schmidt-scale transport beyond entropy alone.

quant-ph↗
arXiv2026

LLM-Driven Cross-Paradigm Design for Quantum Optimal Control

Quantum optimal control (QOC) underpins adiabatic quantum computation, quantum annealing, and quantum state engineering, yet practical deployment is fundamentally bottlenecked by strict hardware constraints and substantial expert effort required to design protocols for each problem instance. To overcome this, we introduce QOC-Workbench, an auditable, large language model (LLM)-driven workflow that acts as an automated quantum co-scientist for cross-paradigm protocol design. Going beyond traditional numerical optimizers that merely tune parameters within a fixed formula, the LLM autonomously parses physics literature, proposes structural hypotheses, and writes code to validate them by direct simulation. This workflow supports cross-paradigm design by accumulating control motifs across tasks. We demonstrate this approach across three distinct settings: Case 1, Rydberg-atom maximum-independent-set arrays; Case 2, interacting XXZ spin chains; and Case 3, random transverse-field Ising models. In Cases 1 and 2, the workflow autonomously discovers hardware-compliant auxiliary controls, target catalysts, and schedule deformations that outperform literature baselines. In Case 3, it addresses the computational bottleneck of variational counterdiabatic driving by escalating from per-instance optimization to an amortized graph-neural-network generator, successfully transferring learned coefficient paths to larger unseen systems. By actively bridging the gap between theoretical algorithms and experimental restrictions across distinct control paradigms and Hamiltonian families, QOC-Workbench establishes a continuously evolving, cross-paradigm methodology for autonomous quantum control.

quant-ph↗
arXiv2026

Revealing Entanglement-Growth Mechanisms through the Magic Barrier

Quantum entanglement and magic are complementary resources underlying quantum computational advantage, yet their dynamical relation in many-body systems remains poorly understood. In this Letter, we show that the mechanism of bipartite entanglement growth is encoded in the relative timescale between the entropy-growth-rate peak and the magic barrier, defined as the transient peak of the anti-flatness of the entanglement spectrum. When entanglement is locally built, the same microscopic process increases the entropy and reshapes the Schmidt spectrum, so the magic-barrier peak occurs in the time window of maximal entropy growth. When entanglement is mainly transported or redistributed, entropy can grow before appreciable spectral non-flatness is generated, naturally separating the two peak times. We demonstrate this distinction in the random-field XXZ chain: the two peaks remain strongly correlated in the thermal regime, while their separation grows systematically across the thermal--MBL crossover. We further validate this theoretical framework by employing Bell-pair initial states alongside a tunable SWAP--Haar random circuit. Our results reveal an intrinsic dynamical connection between entanglement and magic, establishing the magic barrier as a powerful spectral diagnostic of how quantum information is generated, transported, and reshaped.

quant-ph↗
arXiv2026

Entanglement Growth from Entangled States: A Unified Perspective on Entanglement Generation and Transport

Studies of entanglement dynamics in quantum many-body systems have focused largely on initial product states. Here, we investigate the far richer dynamics from initial entangled states, uncovering universal patterns across diverse systems ranging from many-body localization (MBL) to random quantum circuits. Our central finding is that the growth of entanglement entropy can exhibit a counter-intuitive non-monotonic dependence on the initial entanglement in many non-ergodic systems, peaking for moderately entangled initial states. To understand this phenomenon, we introduce a conceptual framework that decomposes entanglement growth into two mechanisms: ``build'' and ``move''. The ``build'' mechanism creates new entanglement, while the ``move'' mechanism redistributes pre-existing entanglement throughout the system. Specifically, we demonstrate that MBL dynamics are ``move-dominated'', exhibiting a quantitative agreement with a random SWAP circuit that serves as a model of pure ``move'' dynamics by uniformly distributing pre-existing entanglement. This implies that MBL acts as a redistributor of a hidden entanglement reservoir quantified by the bipartition-averaged entropy. This ``build-move'' framework offers a unified perspective for classifying diverse physical dynamics, deepening our understanding of entanglement propagation and information processing in quantum many-body systems.

quant-ph↗
arXiv2026

ORBIT-Q: Dual-axis benchmarking of autonomous agents in scientific quantum programming

Autonomous coding agents perform well on many conventional programming tasks, but scientific computing demands a rigorous validation paradigm that extends beyond simple functional test completion: generated code must preserve physical fidelity, differentiable workflows, framework-native semantics, and scalable representations. We introduce Open Research Benchmark for Integrated Tasks in Quantum Computing (ORBIT-Q) to address this gap. At its core, ORBIT-Q contributes a carefully curated suite of complex, research-level quantum workflows that serves as a challenging testbed for modern scientific programming. ORBIT-Q combines a rigorous multi-tier verification pipeline to support two orthogonal comparisons: different agent harness and model configurations at a fixed quantum software framework, and different quantum software frameworks at a fixed agent. In our systematic evaluations, TensorCircuit-NG (TC) exhibits the highest capability and performance efficiency among the evaluated quantum software frameworks under agent-driven programming, and Codex with GPT-5.5 is the strongest tested agent configuration on TC. However, a significant performance and design gap remains between frontier autonomous agents and human expert reference implementations. We further evaluate two efficiency dimensions: agent-side resource use and artifact-side runtime. Together, these results establish ORBIT-Q as a rigorous benchmark for autonomous scientific programming, framework-agent synergy, and quantum software performance.

quant-ph↗
arXiv2026

Absence of poor local minima in matrix product states

Quantum circuits suffer from severe trainability issues: even shallow circuits are swamped with poor local minima. Yet matrix product states (MPS), which can be prepared by sequential circuits, are remarkably trainable in practice -- as demonstrated by decades of successful density matrix renormalization group calculations. In this work, we resolve this apparent paradox by proving that the energy landscapes of MPS are free from poor local minima, under the same setting where brickwork circuits are not. The key insight is that the gauge freedom of MPS creates an effective local overparametrization that causes local minima to concentrate near the global minimum, analogous to overparametrized classical neural networks. We rigorously prove that the local minimum distribution is invariant under moves of the orthogonality center of MPS representations. Numerical experiments further confirm that the optimization of sequential circuits converges to near-optimal solutions even for random Hamiltonians, in stark contrast to brickwork circuits. Our findings establish a theoretical understanding of the trainability of MPS, providing a valuable guide for designing variational quantum circuits and algorithms with better trainability in the future.

quant-ph↗
arXiv2026

Intrinsic preservation of plasticity in continual quantum learning

Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning. However, standard deep learning suffers from a fundamental issue: loss of plasticity, in which networks gradually lose their ability to learn from new data. Here we show that quantum learning models naturally overcome this limitation, preserving plasticity over long timescales. We demonstrate this advantage systematically across a broad spectrum of tasks from multiple learning paradigms, including supervised learning and reinforcement learning, and diverse data modalities, from classical high-dimensional images to quantum-native datasets. Although classical models exhibit performance degradation correlated with unbounded weight and gradient growth, quantum neural networks maintain consistent learning capabilities regardless of the data or task. We identify the origin of the advantage as the intrinsic physical constraints of quantum models. Unlike classical networks where unbounded weight growth leads to landscape ruggedness or saturation, the unitary constraints confine the optimization to a compact manifold. Our results suggest that the utility of quantum computing in machine learning extends beyond potential speedups, offering a robust pathway for building adaptive artificial intelligence and lifelong learners.

quant-ph↗
arXiv2026

Observation and Modulation of the Quantum Mpemba Effect on a Superconducting Quantum Processor

In non-equilibrium quantum systems, the quantum Mpemba effect (QME) emerges as a counterintuitive phenomenon: systems exhibiting greater initial symmetry breaking restore symmetry faster. It has been attracting broad interest in studying QME dynamics and potential applications in quantum information science. While theoretical exploration of QME has surged, experimental studies, specifically on its flexible modulation, remain limited. Here, we report the observation and modulation of QME using a superconducting processor featuring an all-to-all connected, tunable-coupling architecture that enables precise control from short- to long-range interactions. This platform allows independent manipulation of coupling regimes, on-site potentials, and initial states, enabling us to elucidate their roles in QME. To quantify symmetry restoration, we employ entanglement asymmetry (EA), derived from the reconstructed density matrix via quantum state tomography, as a sensitive probe. In strong short-range coupling regimes, EA crossovers during quenches from tilted Néel states confirm the presence of QME. In contrast, in intermediate coupling regimes, synchronized EA and entanglement entropy dynamics reveal the suppression of QME. Remarkably, QME reemerges with the introduction of on-site linear potentials or quenches from tilted ferromagnetic states, the latter proving robust against on-site disorder. Our study demonstrates flexible QME modulation on a superconducting platform with multiple controllable parameters, shedding light on quantum many-body non-equilibrium dynamics and opening avenues for quantum information applications.

quant-ph↗
arXiv2026

Quantum Subliminal Learning

Machine learning models can inherit hidden behavioral traits through innocuous public interfaces, a phenomenon known as subliminal learning. Here we extend this framework to quantum models and study two distillation pathways: an auxiliary channel on random inputs and a restricted task channel in which the student matches a public supervised output while the hidden behavior resides on a disjoint task. Both classical and quantum neural networks (QNNs) exhibit efficient auxiliary-channel subliminal learning, but the task channel shows strong architecture dependence. Classical neural networks transmit little hidden-task information through the public-task interface, whereas QNNs retain most of the hidden-task signal. We show that a unified geometric picture explains both regimes: transmission is controlled by the teacher drift magnitude together with the fraction of hidden-task-relevant drift that remains visible through the public interface. These results identify a concrete security concern for quantum model supply chains and suggest a controlled route for hidden-information transfer in quantum information processing.

quant-ph↗
arXiv2026

Noisy Monitored Quantum Circuits

Noisy monitored quantum circuits have emerged as a versatile and unifying framework connecting quantum many-body physics, quantum information, and quantum computation. In this review, we provide a comprehensive overview of recent advances in understanding the dynamics of such circuits, with an emphasis on their entanglement structure, information-protection capabilities, and noise-induced phase transitions. A central theme is the mapping to classical statistical models, which reveals how quantum noise reshapes dominant spin configurations. This framework elucidates universal scaling behaviors, including the characteristic $q^{-1/3}$ entanglement scaling with noise probability $q$ and distinct timescales for information protection. We further highlight a broad range of constructions and applications inspired by noisy monitored circuits, spanning variational quantum algorithms, classical simulation methods, mixed-state phases of matter, and emerging approaches to quantum error mitigation and quantum error correction. These developments collectively establish noisy monitored circuits as a powerful platform for probing and controlling quantum dynamics in realistic, decohering environments.

quant-ph↗
arXiv2026

A Qudit-native Framework for Discrete Time Crystals

We introduce a qudit-native framework for engineering rich and robust discrete time crystals (DTCs) by leveraging their internal multilevel structure. Unlike in qubit systems, qudit-based DTCs exhibit distinct dynamical mechanisms that arise only in multilevel systems, as supported by a dressed normal-form analysis in the heating-suppression regime. These mechanisms are manifested in representative systems: we show that subspace-selective embedded kicks stabilize higher-order subharmonic responses and suppress thermalization, as demonstrated in spin-1 chains; in spin-3/2 systems, extending embedded kicks to more levels enables different level partitions and reveals that DTC robustness is dictated by the symmetry of the partition; and in spin-2 platforms, we realize concurrent 2T and 3T DTCs under a unified drive. These findings establish a systematic, hardware-efficient methodology for designing stable and multifunctional Floquet phases of matter on modern qudit-based quantum processors.

quant-ph↗
arXiv2026

Quantum Pontus-Mpemba Effects in Real and Imaginary-time Dynamics

The quantum Pontus-Mpemba effect (QPME) is a counterintuitive phenomenon wherein a quantum system relaxes more rapidly through a two-step evolution protocol than through direct evolution under a symmetric Hamiltonian alone. In this protocol, the system first evolves under a symmetry-breaking Hamiltonian and then switches to a symmetric one. We demonstrate that QPME occurs under both real-time and imaginary-time dynamics with respect to $U(1)$-symmetry. Using tilted ferromagnetic initial states, we demonstrate that a transient asymmetric evolution significantly accelerates thermalization or convergence to the ground state for both real-time and imaginary-time evolutions, respectively. The effect is pronounced for small tilt angles, while larger tilts or antiferromagnetic initial states suppress it. Numerical evidence across different system sizes confirms the robustness of QPME, demonstrating its stability in the thermodynamic limit. This work extends the framework of nonequilibrium quantum phenomena to incorporate active state preparation, with direct implications for the implementation of quantum simulation.

quant-ph↗
arXiv2026

Post-Selection-Free Decoding of Measurement-Induced Area-Law Phases via Neural Networks

Monitored quantum circuits host a rich variety of exotic non-equilibrium phases. Among the most representative examples are measurement-induced phase transitions between distinct area-law entangled states. However, because these transitions are characterized by specific entanglement quantities such as mutual information or topological entanglement entropy that are nonlinear functionals of the density matrix, their experimental observation requires multiple identical quantum trajectories via post-selection, which becomes exponentially unfeasible for large systems. Here, we leverage modern machine learning tools to address this challenge. We devise a neural network architecture combining a convolutional neural network with an attention mechanism, and use raw measurement outcomes directly as input to classify trivial, long-range entangled, and symmetry-protected topological phases. We show that the system's relaxation to a steady-state phase manifests as a sharp convergence in the classifier's accuracy, entirely bypassing the need for quantum state reconstruction. We systematically study the performance of our network as a function of sample size, input data, spatial and temporal constraints, and system size scalability. Our results demonstrate that this approach is robust and post-selection free, offering a practical pathway for experimentally probing measurement-induced phases.

quant-ph↗
arXiv2026

Entanglement growth and information capacity in a quasiperiodic system with a single-particle mobility edge

We investigate the quantum dynamics of a one-dimensional quasiperiodic system featuring a single-particle mobility edge (SPME), described by the generalized Aubry-André (GAA) model. This model offers a unique platform to study the consequences of coexisting localized and extended eigenstates, which contrasts sharply with the abrupt localization transition in the standard Aubry-André model. We analyze the system's response to a quantum quench through two complementary probes: entanglement entropy (EE) and subsystem information capacity (SIC). We find that the SPME induces a smooth crossover in all dynamical signatures. The EE saturation value exhibits a persistent volume-law scaling in the mobility-edge phase, with an entropy density that continuously decreases as the number of available extended states decreases. Complementing this, the SIC profile interpolates between the linear ramp characteristic of extended systems and the information trapping behavior of localized ones, directly visualizing the mixed nature of the underlying spectrum. Our results establish unambiguous dynamical fingerprints of a mobility edge, providing a crucial non-interacting benchmark for understanding information and entanglement dynamics in more complex systems with mixed phases.

quant-ph↗
arXiv2026

TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation

We present TensorCircuit-NG, a next-generation quantum software platform designed to bridge the gap between quantum physics, artificial intelligence, and high-performance computing. Moving beyond the scope of traditional circuit simulators, TensorCircuit-NG establishes a unified, tensor-native programming paradigm where quantum circuits, tensor networks, and neural networks fuse into a single, end-to-end differentiable computational graph. Built upon industry-standard machine learning backends (JAX, TensorFlow, PyTorch), the framework introduces comprehensive capabilities for approximate circuit simulation, analog dynamics, fermion Gaussian states, qudit systems, and scalable noise modeling. To tackle the exponential complexity of deep quantum circuits, TensorCircuit-NG implements advanced distributed computing strategies, including automated data parallelism and model-parallel tensor network slicing. We validate these capabilities on GPU clusters, demonstrating a near-linear speedup in distributed variational quantum algorithms. TensorCircuit-NG enables flagship applications, including end-to-end QML for CIFAR-100 computer vision, efficient pipelines from quantum states to neural networks via classical shadows, and differentiable optimization of tensor network states for many-body physics.

quant-ph↗
arXiv2026

The Dual Role of Low-Weight Pauli Propagation: A Flawed Simulator but a Powerful Initializer for Variational Quantum Algorithms

Variational quantum algorithms are often hindered by rugged optimization landscapes. In this Letter, we investigate the low-weight Pauli propagation (LWPP) algorithm and find that it serves as an unreliable energy estimator for variational circuits. However, we reveal a counterintuitive insight: the Pauli-weight truncation acts as a spectral filter, effectively smoothing out high-frequency local minima while preserving the global basin of attraction in the landscape. We identify this mechanism as landscape alignment, where the approximate landscape becomes a superior navigator compared to the rugged exact landscape. Benchmarks across diverse spin models and molecular systems demonstrate that LWPP-initialized optimization yields order-of-magnitude improvements in accuracy, often finding solutions inaccessible to direct exact optimization. This work reframes LWPP from a flawed simulator into a vital pre-optimizer that serves not only as a cheap classical substitute but also as an essential tool for addressing quantum optimization challenges.

quant-ph↗