arXiv subjects

Quntao Zhuang

Publications and source records attributed to Quntao Zhuang.

At least 19 recordsLinked to original sources

arXiv2026

Hierarchy of discriminative power and complexity in learning quantum ensembles

Distance metrics are central to machine learning, yet distances between ensembles of quantum states remain poorly understood due to fundamental quantum measurement constraints. We introduce a hierarchy of integral probability metrics, termed MMD-$k$, which generalizes the maximum mean discrepancy to quantum ensembles and exhibits a strict trade-off between discriminative power and statistical efficiency as the moment order $k$ increases. For pure-state ensembles of size $N$, estimating MMD-$k$ with arbitrary measurement schemes requires $Θ(N^{1-1/k})$ samples for constant $k$. At the same time, we prove that any stable distance metric with full discriminative power admits an $O(N\log N)$ upper bound and an $Ω(N)$ instance-gap lower bound. For quantum Wasserstein distance, with sufficiently large fixed state dimension, we establish a nearly linear lower bound in the ensemble size at constant additive accuracy, together with an $O(N\log N)$ upper bound. These results provide principled guidance for the design of loss functions in quantum machine learning, as we illustrate in training quantum denoising diffusion probabilistic models.

quant-ph↗

arXiv2026

Emergence of chaos with exceptional points in reset-driven Floquet dynamics

We investigate the spectral structure of reset-driven Floquet quantum channels generated by the Hamiltonian evolution of a many-body system followed by periodic resetting of a bath. By tuning a perturbation parameter in the interacting Aubry-André-Harper(AAH) model, we find that exceptional points could emerge in the spectral transition from a symmetry-constrained ergodic regime to a fully chaotic regime. Across this transition, increasing the perturbation causes the real eigenvalues of the channel to drift, coalesce at exceptional points, and bifurcate into complex-conjugate pairs, reflecting the progressive breaking of symmetry constraints in operator space. We further show that the channel spectrum sharply distinguishes chaotic, ergodic, many-body localized, and scarred dynamical regimes. Finally, we connect the leading channel eigenvalues to experimentally accessible probes based on quantum mutual information, establishing a link between the spectral organization of reset-driven quantum channels and observable relaxation dynamics. We implement the protocol on the IBM Quantum Platform and verify that the dynamical differences are reflected in the decay of mutual information.

quant-ph↗

arXiv2026

Sample-Efficient Tomography of a Class of Mixed States with Extensive Entanglement and Magic

Full tomography of a generic many-qubit quantum state requires exponentially many copies, while suitable structural constraints can make reconstruction sample-efficient. Existing approaches exploit, for example, limited entanglement structure, low magic, or constrained state-preparation circuits. Here we consider a class of mixed states that can simultaneously exhibit extensive entanglement and extensive magic. Specifically, we introduce Clifford-encoded block-product (CEBP) states, obtained by applying an unknown global Clifford unitary to a tensor product of arbitrary mixed states supported on unknown blocks of bounded size. We show that, for a fixed block size, CEBP states can be reconstructed using polynomially many copies by exploiting Clifford-preserved Pauli correlations to recover the latent structure and reduce the remaining problem to local tomography. Our result demonstrates that sample-efficient tomography can arise from bounded complexity in a latent frame even when the physical state is highly entangled, highly magical, and mixed, and suggests complexity modulo structured transformations as a broader organizing principle for quantum-state learnability.

quant-ph↗

arXiv2026

Hypothesis testing between quantum ensembles

Quantum state ensembles are important in quantum information processing. For example, quantum $t$-designs model highly entangled states in complex systems, while projected ensembles appear in generative quantum machine learning and studies of thermalization. With their sample state accompanied by a classical label, these ensembles contain operational information beyond their average density operators. Yet an ensemble differs from a classical-quantum state because it is invariant under permutations of labels. We formulate binary hypothesis testing between finite quantum ensembles and derive fundamental limits on error probability. Given an observed label pattern, we show that the joint sampled state can be described by power-weighted ensemble moments. This yields the Bayes-optimal measurement and exact finite-sample error, revealing that discrimination is governed by the full moment hierarchy up to the number of samples. In the many-sample limit, we derive Chernoff bounds and obtain exact error exponents for finite uniform pure-state ensembles. We apply these results to optical communication and $t$-designs. For finite uniform pure-state $t$-designs with large $t$, the maximal discrimination exponent scales sharply as $\sim t^{-2}$, while equal-prior fixed-error testing requires $\sim t^2$ samples.

quant-ph↗

arXiv2026

Efficient learning of bosonic unitaries beyond the Gaussian class

Multimode quantum processes are generally difficult to learn, due to the large dimensionality and complex entanglement structure beyond the Gaussian class. Here, we show that the fundamental obstruction is not non-Gaussianity itself, but the buildup of irreducible multimode non-Gaussian correlations. We establish a tractability frontier for bosonic unitary learning: a general $m$-mode unitary with input energy at most $E$ per mode requires at least $Ω(E^{2m})$ channel uses, whereas two broad non-Gaussian families---$t$-doped Gaussian unitaries and Gaussian-entanglable unitaries---can be learned with resources polynomial in $m$. The latter can exhibit both extensive non-Gaussianity and strong multimode entanglement. Our forward-only protocols use coherent-state probes, Gaussian operations, local heterodyne detection, and classical post-processing to identify the global Gaussian mixing and reduce the remaining task to single- or few-mode learning. The analysis also yields a multimode quantum Darmois--Skitovich theorem showing that mode-spreading passive networks preserve product structure only for Gaussian input states, an almost-sure activation theorem for non-Gaussian processes showing that non-Gaussian unitaries yield non-Gaussian outputs for almost all coherent input states, and a method for learning unitaries from uncalibrated coherent probes. Our results identify that complexity of learning arises from irreducible mixing of non-Gaussianity and entanglement, rather than either resource alone.

quant-ph↗

arXiv2026

Measurement-induced overconcentration in quantum generative models

Quantum measurement is a key resource for quantum generative learning, providing intrinsic stochasticity for generating diverse quantum samples. However, in measurement-assisted state-ensemble resampling, repeated measurements can also induce overconcentration: under a fixed measurement trajectory, distinct input states progressively converge toward similar output states, suppressing input-dependent diversity. To diagnose this effect, we introduce three complementary metrics: accuracy, generative power, and input sensitivity. For Haar-random monitored circuits, we prove that one-step models retain input sensitivity up to dimension-suppressed corrections, whereas sequential monitored circuits exhibit a depth-dependent loss of input sensitivity. Motivated by this diagnosis, we propose a truncated quantum denoising diffusion probabilistic model (QuDDPM), which restricts the temporal depth of both the forward diffusion and reverse denoising processes. Numerical benchmarks show that truncated QuDDPM preserves stronger input sensitivity while maintaining accuracy and generative power comparable to the original model. These results identify measurement-induced overconcentration as a dynamical limitation of deep monitored quantum generative models and establish temporal depth as a design parameter for balancing measurement-induced randomness with input-dependent diversity.

quant-ph↗

arXiv2026

When AI meets quantum information: A comprehensive review

Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.

quant-ph↗

arXiv2026

18-dB on-chip vacuum squeezing in an adaptively poled lithium niobate waveguide

Quantum squeezed states of light can enhance measurement sensitivity beyond classical limits and enable quantum information processing, but scalable low-loss sources remain challenging. We demonstrate continuous-wave quantum squeezing on a chip, achieving 18 dB of squeezing and 20 dB of anti-squeezing at 1570 nm in a 1.6-cm traveling-wave adaptively poled thin-film lithium niobate waveguide. A distributed model independently determines facet losses, phase noise, and nonlinear interaction strength without prior assumptions, enabling rigorous inference of on-chip performance. We estimate a 95% confidence interval of [-18.96, -17.25] dB squeezing and [19.96, 21.35] dB anti-squeezing. These values represent the highest squeezing reported for any integrated photonic platform and the first assumption-free statistical validation of integrated squeezing performance. Our results establish thin-film lithium niobate as a high-performance, scalable platform for continuous-variable quantum sensing, communications, and photonic computing.

physics.optics↗

arXiv2026

Theory of quantum comb enhanced interferometry

Optical frequency combs, named for their comb-like peaks in the spectrum, are essential for various sensing applications. As the technology develops, its performance has reached the standard quantum limit dictated by the quantum fluctuations of coherent light field. Quantum combs, with their quantum fluctuation engineered via squeezing and entanglement, are the necessary ingredient for overcoming such limits. We develop the theory for designing and analyzing quantum combs, focusing on dual-comb interferometric measurement. Our analyses cover both squeezed and entangled quantum combs with division receivers and heterodyne receivers, leading to four protocols with quantum advantages scalable with squeezing/entanglement strength. In the spectroscopy of a single absorption line, the division receiver with the squeezed comb suffers from entanglement-mismatching-induced amplified noise, while the other three protocols demonstrate a surprising robustness to loss at a few comb lines. Such a unique loss-robustness of a scalable quantum advantage has not been found in any traditional quantum sensing protocols.

quant-ph↗

arXiv2026

Resource-Efficient Quantum-Enhanced Compressive Imaging via Quantum Classical co-Design

Quantum sensing can enhance imaging performance by reducing measurement noise below the classical limit, thereby improving the signal-to-noise ratio (SNR) of acquired data. In conventional quantum imaging schemes, squeezing is applied independently to each pixel or spatial mode, leading to a quantum resource cost that scales linearly with image dimension. This approach implicitly separates quantum enhancement from classical post-processing, treating them as independent layers. In this work, we demonstrate that integrating quantum resource allocation with the guidance from classical compressive imaging, via co-design between the quantum hardware layer and the classical software layer, substantially reduces the required quantum resources. We employ principal component analysis (PCA) to identify a low-dimensional principal component subspace for measurement and apply squeezing selectively to the most informative spatial modes corresponding to these principal components. Our numerical experiments show that high-accuracy image classification and high-fidelity image reconstruction can be achieved with significantly fewer squeezed modes compared to pixel-wise squeezing. Our results establish a joint quantum classical co-design framework for resource-efficient quantum-enhanced imaging.

quant-ph↗

arXiv2026

Anticoncentrated $n$-bit distribution from $\log(n)$ qubits

Random circuit sampling (RCS) is a leading approach to demonstrate quantum advantage, with its believed classical hardness rooted in anticoncentration of output distributions and average-case hardness of probability estimation. Here we show that this association is not fundamental. We introduce holographic random circuit sampling (HRCS), a spatiotemporal protocol that interleaves random unitary evolution with mid-circuit measurements. We prove that $n$ classical bits exhibiting $ε$-approximate anticoncentration of Haar random states can be generated using only $\mathcal{O}(\log n)$ physical qubits and linear depth, establishing a precise space-time trade-off and indicating efficient classical simulation. Our analyses is built upon exact formulas for collision probability and higher-order power sums. Our experimental validation on IBM Quantum devices demonstrates sampling up to 200 classical bits using only 20 qubits.

quant-ph↗

arXiv2026

Loss-robust crossband entanglement generation beyond the direct-transduction limit

Entanglement across distant frequency bands is a crucial resource in quantum networking. However, directly entangling crossband photons, e.g., microwave and optical, is challenging. Furthermore, distributing crossband entanglement via direct quantum transduction is fundamentally limited, regardless of input engineering with unconstrained source brightness. We propose to utilize intraband entanglement to overcome such direct-transduction limits by a factor that increases with the input intraband entanglement brightness in the ideal case. In the presence of experimental loss 5% and assuming 10dB of squeezing in both optical and microwave bands, we show that our protocol can generate a violation of the separability criterion equivalent to 2.38 ebits, compared with the baseline protocol limited to 0.082 ebits. The proposed protocols rely only on off-the-shelf components and provide advantages robust to a substantial amount of loss.

quant-ph↗

arXiv2026

Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics

Quantum information is typically fragile under measurements and environmental coupling. Remarkably, we find that its lifetime can scale exponentially with system size when the environment is continuously monitored via mid-circuit measurements -- regardless of bath size. Starting from a maximally entangled state with a reference, we analytically prove this exponential scaling for typical Haar random unitaries and confirm it through numerical simulations in both random unitary circuits and chaotic Hamiltonian systems. In the absence of bath monitoring, the lifetime exhibits a markedly different scaling: it grows at most linearly -- or remains constant -- with system size and decays inversely with the bath size. We further extend our findings numerically to a broad class of initial states. In the intermediate regime of partial monitoring, we identify and prove a two-scale transition, where the QMI decays logarithmically at microscopic time scales but linearly at macroscopic time scales.} We discuss implications for {monitored quantum circuits in the weak measurement limit, quantum algorithms such as quantum diffusion models and quantum reservoir computing, and quantum communication. Finally, we experimentally verify the gap of persisted information on IBM Quantum hardwares.

quant-ph↗

arXiv2026

Variational Quantum Transduction

Quantum transducers are critical for quantum interconnect, enabling coherent signal transfer across disparate frequency domains. Beyond material and device advances, protocol design has become a powerful means to improve transduction. We introduce a variational quantum transduction (VQT) framework that employs variational tools from near-term quantum computing to systematically optimize protocol performance. As a variational quantum circuit framework, VQT is not plagued by known training issues such as barren plateau, because a small-scale problem is sufficient for substantial advantage and training only needs to be done once to configure a VQT system. Maximizing the quantum information rate within this framework yields protocols that surpass all known schemes in their respective classes. For non-adaptive protocols, VQT exceeds the performance envelopes of Gottesman-Kitaev-Preskill (GKP)-based and entanglement-assisted approaches. In the adaptive setting, VQT provides only a marginal improvement over Gaussian feedforward strategies, indicating that Gaussian adaptive transduction is already close to optimal. With increasingly universal quantum control, VQT provides a systematic path toward optimal quantum transduction.

quant-ph↗

arXiv2026

Quantum squeezing in an all-resonant periodically poled lithium niobate microresonator

Quantum noise limits the sensitivity of optical measurements, but squeezed states of light enable quantum-enhanced metrology, sensing, and information processing. Most on-chip squeezed-light sources rely on Kerr ($χ^{(3)}$) nonlinearities, remain limited by pump power and excess loss constraints. Quadratic ($χ^{(2)}$) platforms instead provide stronger parametric interactions, lower pump power requirements, and greater spectral engineering flexibility. Here, we demonstrate strong, broadband squeezed-light generation on a thin-film lithium niobate (TFLN) photonic chip using a dual-resonant optical parametric amplifier implemented in a single periodically poled LN (PPLN) microresonator. Near-full-depth domain inversion is achieved simultaneously with highly over-coupled resonances, exhibiting escape efficiencies exceeding 90% and intrinsic quality factors above 2.5 million in a 0.6 mm$^2$ X-cut TF-PPLN resonator, enabling efficient squeezing at 1587 nm when pumped at 793.5 nm. Operating in the continuous-wave regime, we directly measure -0.81 dB of squeezing below the shot-noise limit with a pump power of 27 mW, together with +4.29 dB of anti-squeezing. From these measurements, we infer an on-chip squeezing level of -7.52 dB $\pm$ 0.22 dB (95% confidence interval: [-7.96,-7.10] dB), and an on-chip anti-squeezing level of +9.62 dB $\pm$ 0.25 dB. We demonstrate single-mode squeezing at degeneracy with a squeezed-light spectrum exceeding 10.3 THz. This work reports the highest squeezing ratio among integrated $χ^{(2)}$ cavity platforms and the first quasi-phase matched, fully resonant $χ^{(2)}$ cavity squeezer on chip, establishing a scalable route to fully integrated power-efficient squeezed-light sources for quantum-enhanced sensing and metrology.

physics.optics↗

arXiv2026

Correlated Noise Estimation with Quantum Sensor Networks

We address the metrological problem of estimating collective stochastic properties imprinted on a network of quantum sensors. Canonical examples include center-of-mass quadrature fluctuations in a system of bosonic modes and correlated dephasing in an ensemble of qubits (e.g., spins), bosons, or fermions. We develop a theoretical framework to determine the limits of correlated (weak) noise estimation with quantum sensor networks and reveal the requirements for entanglement advantage. Notably, an advantage emerges from the synergistic interplay between quantum correlations of the sensors and ``classical'' correlations of the noises. We determine optimal entangled probe states and identify a sensing protocol -- reminiscent of a many-body echo -- that achieves the fundamental limits of measurement sensitivity for a broad class of problems, unveiling a route towards entanglement-enhanced metrology of correlated many-body phenomena.

quant-ph↗

arXiv2026

Generative quantum machine learning via denoising diffusion probabilistic models

Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-quality samples in many computer vision tasks, as well as to incorporate flexible model architectures and a relatively simple training scheme. Quantum generative models, empowered by entanglement and superposition, have brought new insight to learning classical and quantum data. Inspired by the classical counterpart, we propose the quantum denoising diffusion probabilistic model (QuDDPM) to enable efficiently trainable generative learning of quantum data. QuDDPM adopts sufficient layers of circuits to guarantee expressivity, while it introduces multiple intermediate training tasks as interpolation between the target distribution and noise to avoid barren plateau and guarantee efficient training. We provide bounds on the learning error and demonstrate QuDDPM's capability in learning correlated quantum noise model, quantum many-body phases, and topological structure of quantum data. The results provide a paradigm for versatile and efficient quantum generative learning.

quant-ph↗

arXiv2026

Theory Framework of Multiplexed Photon-Number-Resolving Detectors

Photon counting is a fundamental component in quantum optics and quantum information. However, implementing ideal photon-number-resolving (PNR) detectors remains experimentally challenging. Multiplexed PNR detection offers a scalable and practical alternative by distributing photons across multiple modes and detecting their presence using simple ON-OFF detectors, thereby enabling approximate photon-number resolution. In this work, we establish a theoretical model for such detectors and prove that the estimation error in terms of photon number moments decreases inverse proportionally to the number of detectors. Thanks to the enhanced PNR capability, multiplexed PNR detector provides an advantage in cat-state breeding protocols. Assuming a two-photon subtraction case, $7$dB of squeezing, and an array of 20 detectors of efficiency $95\%$, our calculation predicts fidelity $\sim0.88$ with a success probability $\sim 3.8\%$, representing orders-of-magnitude improvement over previous works. Similar enhancement also extends to cat-state generation with the generalized photon number subtraction. With experimentally feasible parameters, our results suggest that megahertz-rate cat-state generation is achievable using an on-chip array of \emph{tens} of ON-OFF detectors.

quant-ph↗