arXiv subjects

Qipeng Qian

Publications and source records attributed to Qipeng Qian.

11 recordsLinked to original sources

arXiv2026

Shapley Valuation of Finite-Copy Quantum Data Depends on Physical Access

Data valuation asks how learning utility should be attributed to training data contributors. Most classical formulations begin after data have become reusable records, so the physical readout of the data is effectively fixed. Finite-copy quantum data are different: unknown states are consumable physical systems, and the same supplied states and downstream task can yield different Shapley values under different physical access models. Our framework makes this dependence explicit by treating physical access as a component of quantum data valuation itself. We establish an exact connection between physical-access advantage and contributor-level data valuation. For nested access models, we prove that the maximal downstream utility gain enabled by richer physical access exactly determines the largest symmetric Shapley ranking-reversal margin. More generally, for arbitrary access-model pairs, including non-nested ones, we derive an exact geometric characterization of the possible shifts of the full Shapley attribution vector. For fixed learning pipelines, we further obtain an operational Shapley-observable representation for finite-copy valuation. Numerical experiments demonstrate that identical quantum samples can receive different values and rankings when only the physical access model is changed. These results establish that quantum data value is not an intrinsic property of the underlying states alone, but emerges from the interaction between quantum states, physical access, and the downstream learning task.

quant-ph↗

arXiv2026

Information-Calibrated Quantum Diffusion: Aligning Forward Noise with Reverse Recoverability

Quantum diffusion models typically parameterize forward corruption by raw channel strength, even though this parameter does not directly quantify how much ensemble information is erased or how difficult the corresponding reverse problem is. We introduce the classical--quantum information decrement $\Delta_t=I(X{:}Q_{t-1})-I(X{:}Q_t)$ as an intrinsic diffusion coordinate that links forward noise allocation to reverse recoverability. Along depolarization, equalizing $\Delta_t$ yields the unique minimax discretization of the forward information loss, while universal recoverability gives the same quantity an operational interpretation as a physically attainable local recovery budget. We further show that such local calibration is not sufficient for stochastic generation: models can satisfy the same recovery criterion while producing substantially different state distributions. This motivates a stochastic learner that combines information-calibrated recovery constraints with distribution matching. We establish finite-sample calibration and compositional trace-Wasserstein control for the resulting learner. Controlled quantum experiments validate the predicted information--recovery alignment, show that the recovery constraints improve local inversion, and confirm the complementary role of distribution matching in endpoint generation. The resulting framework also achieves stronger endpoint trace-Wasserstein performance than an official QuDDPM implementation with fewer trainable parameters. Overall, our work provides a unified information-theoretic principle for designing forward schedules, calibrating reverse steps, and separating physical recovery from generative coverage in quantum diffusion.

quant-ph↗

arXiv2026

When Does Forecasting Reveal Temporal Structure? A Stability Analysis of Time-Series Structural Selection

Forecast accuracy is often used as a proxy for temporal structure discovery, but predictive performance and structural identifiability are not equivalent. Different temporal mechanisms can achieve similar forecast errors, while small forecast differences may still contain sufficient information for recovery. In this work, we study when forecast-only structural selection can be trusted. We show that a vanishing forecast margin does not necessarily imply structural ambiguity, and establish a stability perspective that evaluates structural separation relative to uncertainty in the selection objective. This perspective provides both a sufficient condition for reliable selection and a continuous measure of selection difficulty. Experiments across controlled and end-to-end settings demonstrate that forecast margin alone is insufficient, while the proposed stability measure better characterizes when forecast-based structural selection succeeds or fails. Our results suggest that predictive accuracy should be treated as evidence for structure discovery only when its separation is sufficiently robust.

cs.LG↗

arXiv2026

An Hybrid Quantum-Classical Diffusion Model for Image Generation

Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simulating large density operators. We propose a scalable hybrid generative pipeline that combines a classical autoencoder for dimensionality reduction with a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) operating in the learned latent space. The autoencoder compresses data into compact latent codes that can be embedded into a small-qubit Hilbert space, after which the quantum diffusion model learns a generative distribution over latent density operators and decodes samples back to the original domain. Algorithmically, we simplify the reverse dynamics by predicting an estimate of the clean state $\rho_0$ at timestep $t$ and computing the one-step reverse update via an analytic backward propagation rule, rather than learning an explicit predictor for $\rho_{t-1}$. We demonstrate the proposed approach on MNIST image generation and discuss how mixed-state quantum diffusion can serve as a practical backbone for hybrid quantum--classical generative modeling under realistic qubit budgets.

cs.LG↗

arXiv2026

Covert Block-Activity Information Transmission over Thermal-Loss Bosonic Channels: Latency--Payload Limits and Finite-Key Achievability

We study covert block-activity information transmission over a thermal-loss bosonic channel, where messages are encoded in weight-constrained activity patterns that Bob must recover through block-local decisions by prescribed deadlines. Shared circular Gaussian displacement modulation makes Willie's averaged lost-light state exactly thermal, with a strictly convex relative-entropy cost in signal energy, whereas a fixed Gaussian receiver at Bob yields a Gaussian mean-shift divergence linear in energy. This asymmetry produces an exact finite-block energy--information frontier and a detector-independent latency converse, matched in order by a block-reset cumulative-sum detector. For block covertness budget $\delta_b$ and error target $\epsilon_b$, the required active length scales as $\delta_b^{-1}\log^2(1/\epsilon_b)$ up to an explicit channel--receiver factor. Lifting this local law to communication yields the matching transmission limit $\log M=\Theta(\sqrt n/\log n)$ for the symmetric coordinatewise architecture under a fixed total covertness budget, maximal-message covertness, vanishing maximal error, and local deadlines. A relaxed full-horizon reference with the same modulation family and fixed measurement supports $\Theta(\sqrt n)$, showing that covertness and the selected measurement alone do not impose the extra logarithmic factor. Finally, public quadrature phase-shift keying codebooks selected by $O(\sqrt n)$ secret bits remove ideal continuous shared randomness without changing the payload order.

cs.IT↗

arXiv2026

Optimal Probe State for Phase Estimation Under Covariant Measurement

We study the optimization of input states for phase estimation under covariant measurements. Building on Holevo's framework, which provides the optimal covariant measurement for a fixed input state, we further optimize over the input state itself. For a general even $2\pi$-periodic cost function with non-negative Fourier coefficients, we derive a necessary and sufficient condition for the optimal input state: Its Fock coefficients are determined, up to arbitrary phases, by the eigenvector corresponding to the largest eigenvalue of a Toeplitz matrix defined by the cost function. This characterization yields an explicit expression for the attainable lower bound of the average cost under optimal covariant measurements and shows that this bound asymptotically approaches zero in the infinite-energy limit. For the specific cost function $W(\theta,\tilde{\theta})=4\sin^2[(\theta-\tilde{\theta})/2]$, we obtain the optimal input state and the corresponding minimum average cost in closed form, demonstrating Heisenberg scaling with respect to the mean photon number.

quant-ph↗

arXiv2026

Heralded enhancement in quantum state discrimination

The discrimination of quantum states is a central problem in quantum information science and technology. Meanwhile, partial post-selection has emerged as a valuable tool for quantum state engineering. In this work, we bring these two areas together and ask whether partial measurements can enhance the discrimination performance between two unknown and non-orthogonal pure states. Our framework is general: the two unknown states interact with the same environment--set in a pure state--via an arbitrary unitary transformation. A measurement is then performed on one of the output modes (i.e. a partial measurement), modeled by an arbitrary positive operator-valued measure (POVM). We then allow classical communication to inform the unmeasured mode of the outcome of the partial measurement, which is subsequently measured by a POVM that is optimal in the sense that the discrimination probability of error is minimized. The two POVMs act locally and classical information is exchanged between the two modes, representing a single-round (feed-forward) form of local operations with classical communication. Under these considerations, we first show that, as expected, the minimum error probability, averaged over all possible conditional states, cannot be reduced below the minimum error probability of discriminating the original input states. Then, we devise a generic setup produces specific examples where the conditional discrimination can achieve strictly lower error probabilities than the original optimal measurement, illustrating that while post-selection does not improve the average performance, it can enable better discrimination in certain post-selected ensembles.

quant-ph↗

arXiv2026

Learning Heat-based Equations in Self-similar variables

We study solution learning for heat-based equations in self-similar variables (SSV). We develop an SSV training framework compatible with standard neural-operator training. We instantiate this framework on the two-dimensional incompressible Navier-Stokes equations and the one-dimensional viscous Burgers equation, and perform controlled comparisons between models trained in physical coordinates and in the corresponding self-similar coordinates using two simple fully connected architectures (standard multilayer perceptrons and a factorized fully connected network). Across both systems and both architectures, SSV-trained networks consistently deliver substantially more accurate and stable extrapolation beyond the training window and better capture qualitative long-time trends. These results suggest that self-similar coordinates provide a mathematically motivated inductive bias for learning the long-time dynamics of heat-based equations.

cs.LG↗

arXiv2026

Upper bounds on the purity of Wigner positive quantum states that verify the Wigner entropy conjecture

We present analytical results toward the Wigner entropy conjecture, which posits that among all physical Wigner non-negative states the Wigner entropy is minimized by pure Gaussian states for which it attains the value $1+\ln\pi$. Working under a minimal set of constraints on the Wigner function, namely, non-negativity, normalization, and the pointwise bound $\pi W\le 1$, we construct an explicit hierarchy of lower bounds $B_n$ on $S[W]$ by combining a truncated series lower bound for $-\ln x$ with moment identities of the Wigner function. This yields closed-form sufficient conditions, expressed in terms of the state purity $\mu:=\operatorname{Tr}(\rho^2)=2\pi\int dq\,dp\,W(q,p)^2$, ensuring $S[W]\ge 1+\ln\pi$. In particular, we first prove that all physical Wigner-non-negative states with $\mu\le 4-2\sqrt{3}$ satisfy the Wigner entropy conjecture. We further obtain a systematic purity-only relaxation of the hierarchy, whose limiting sufficient condition is $\mu\le 2/e$. Finally, we show that the threshold $2/e$ is sharp under the relaxed constraints considered here, thereby identifying the need for additional quantum-realizability information in the remaining high-purity regime.

quant-ph↗

arXiv2024

Wigner non-negative states that verify the Wigner entropy conjecture

We present further progress, in the form of analytical results, on the Wigner entropy conjecture set forth in https://link.aps.org/doi/10.1103/PhysRevA.104.042211 and https://iopscience.iop.org/article/10.1088/1751-8121/aa852f/meta. Said conjecture asserts that the differential entropy defined for non-negative, yet physical, Wigner functions is minimized by pure Gaussian states while the minimum entropy is equal to $1+\lnπ$. We prove this conjecture for the qubits formed by Fock states $|0\rangle$ and $|1\rangle$ that correspond to non-negative Wigner functions. In particular, we derive an explicit form of the Wigner entropy for those states lying on the boundary of the set of Wigner non-negative qubits. We then consider general mixed states and derive a sufficient condition for Wigner non-negativity. For states satisfying our condition we verify that the conjecture is true. Lastly, we elaborate on the states of the set which is in accordance with our condition.

quant-ph↗

arXiv2024

Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting

Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. However, these methods do not explicitly model some of the natural characteristics in traffic data, such as the multiscale structure that encompasses spatial and temporal variations at different levels of granularity or scale. To that end, we propose a Wavelet-Inspired Graph Convolutional Recurrent Network (WavGCRN) which combines multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. In WavGCRN, the traffic data is decomposed into time-frequency components with Discrete Wavelet Transformation (DWT), constructing a multi-stream input structure; then Graph Convolutional Recurrent networks (GCRNs) are employed as encoders for each stream, extracting spatiotemporal features in different scales; and finally the learnable Inversed DWT and GCRN are combined as the decoder, fusing the information from all streams for traffic metrics reconstruction and prediction. Furthermore, road-network-informed graphs and data-driven graph learning are combined to accurately capture spatial correlation. The proposed method can offer well-defined interpretability, powerful learning capability, and competitive forecasting performance on real-world traffic data sets.

cs.LG↗