arXiv subjects

Li Gao

Publications and source records attributed to Li Gao.

At least 19 recordsLinked to original sources

arXiv2026

Complete entropy contraction from conditional hyperboundedness

For finite-dimensional quantum channels compatible with a faithful conditional expectation, we show that conditional hyperboundedness and strict GNS contraction together yield a relative entropy contraction. For KMS-symmetric quantum Markov semigroups, a conditional $2\to q$ hyperboundedness at time $t_0>0$ combined with a GNS gap $λ>0$ implies a modified logarithmic Sobolev constant \[ α=\frac{λ(1-2/q)} {12(λt_0+\log M+1-2/q)},\] where $M$ is the hyperboundedness constant. As a consequence, completely bounded hyperboundedness over auxiliary systems gives complete versions of entropy contraction with the same rates.

quant-ph↗

arXiv2026

In situ enhanced detectability of axions' responses in a microwave cavity

Microwave cavities in strong magnetic fields are among the most sensitive detectors for axion dark matter detection. Its sensitivity, however, is fundamentally limited by the second order nature of axion photon energy conversion, which yields an extremely weak signal. Here we propose a feasible approach to break this limitation, by pre-exciting the target cavity mode to generate a first order power response signal in situ. The resulting signal amplitude scales linearly with the axion-photon coupling parameter, which is in sharp contrast to the quadratic scaling of conventional schemes, and is detectable via standard quadrature balanced homodyne detection system, even though its phase remains stochastic. Numerical simulations demonstrate that this pre-excited seed field can effectively suppress thermal noise, and thus the detection sensitivity of the system can approach the lower quantum noise limit. Consequently, the sensitivities predicted by the KSVZ and DFSZ models could be surpassed. This proposal might offer a pathway to a major leap in axion dark matter searches, as it is within reach of current technology.

hep-ph↗

arXiv2026

Cross-Visibility-Gap Identity Persistence for 4D Scene Graphs

4D scene graphs require persistent object identity when an object disappears inside an opaque carrier and later reappears as a fresh detection. When appearance and target-centric motion are unavailable, identity recovery must rely on relational history. We formulate cross-visibility-gap identity persistence as historical-node recovery through a remembered target--carrier binding. We introduce CVG-SGM, a perception-frontend-compatible transfer--maintain--read memory. Geometry-weighted cross-node interaction transfers target-specific evidence into carrier states, a shared per-node GRU maintains this information during transport, and a two-time readout combines stored carrier--identity compatibility with current detection--carrier geometry to recover the historical identity. In the controlled one-identity-per-carrier setting, CVG-SGM achieves 1.000 re-link accuracy, compared with 0.570 for both no-memory and parameter-count-matched no-write controls. It also generalizes to scene-disjoint, unseen longer visibility gaps. On real RGB-D/SAM2 feature-level episodes, it correctly re-links 36/36 held-out detections, compared with 28/36 for the strongest fixed carrier--identity control. Counterfactual carrier-memory swaps redirect predictions according to the swapped bindings, supporting the role of stored relational information in identity recovery. The current approach remains limited by shared-carrier identity ambiguity and does not provide end-to-end raw-video re-identification.

cs.CV↗

arXiv2026

Complete Modified Logarithmic Sobolev Inequalities for one-dimensional quantum Spin Chain

We prove a complete modified logarithmic Sobolev inequality with a system-size-independent constant for Chen--Kastoryano--Gilyén Gibbs samplers on one-dimensional spin chain at every fixed positive temperature. The result holds for Hamiltonians with bounded finite-range interactions, including noncommuting and spatially nonuniform interactions. It yields trace and diamond mixing times of order $O(\log N+\log(1/ε))$. The proof establishes complete spatial entropy factorization and absorbs quasi-local errors as conditional relative entropy at the original joint state and reference. The static estimate also yields a system-size-independent CMLSI for the single-site Petz heat-bath and regularised heat-bath dynamics.

quant-ph↗

arXiv2026

Noncommutative $L_p$-Poincaré inequalities

We derive $L_p$-Poincaré inequalities for quantum Markov semigroups on finite von Neumann algebras, from a spectral gap (PI) or from a modified logarithmic Sobolev inequality (MLSI). For $p\ge2$, the corresponding coefficients have optimal order $O(p)$ and $O(\sqrt p)$, respectively. Both estimates follow from Beckner inequalities by integration along semigroups. We also establish a non-tracial extension to quantum Markov semigroups satisfying GNS detailed balance. As a direct byproduct, we apply $L_p$-Poincaré inequalities to Schatten-valued functions on graphs in order to obtain the Schatten distortion of expanders.

math.OA↗

arXiv2026

Emulation Capacity between Idempotent Channels

We study the optimal rates of emulation (also called interconversion) between quantum channels, in three regimes: when the encoder and the decoder are unassisted, when they share classical randomness, and when they share an unlimited amount of entanglement. When the source and the target channels are idempotent, we completely characterise these three regimes, in terms of the shape vectors of the two channels, which describe the structure of their ranges. In the unassisted and shared randomness regimes, we give a single-letter expression for the emulation capacity, show that it coincides with the zero-error emulation capacity, and prove a finite-blocklength converse which implies a strong converse. In these two regimes, shared randomness does not increase the emulation capacity, and the emulation of idempotent channels is not reversible. Moreover, we provide an efficient algorithm to approximate this capacity to arbitrary precision. When an unlimited amount of shared entanglement is allowed, we determine the minimal emulation error exactly at every finite blocklength. It is governed by a single quantity, the $\ell_2$-norm of the shape vector, so that the emulation becomes reversible, and exact emulation is achievable with finitely many maximally entangled states. Altogether, these results draw a comprehensive picture of the task of emulating idempotent channels with each other.

quant-ph↗

arXiv2026

A solution to 2-copy distillability of Werner states

Entanglement distillation is a fundamental task in quantum information theory. In this work, we prove that Werner states in arbitrary dimension are $2$-copy distillable if and only if they are $1$-copy distillable. This answers the longstanding open question of the $2$-copy distillability of Werner states. This is an important step on determining whether every non-positive partial transpose (NPT) state is distillable, which remains one of the central open problems in the field of entanglement distillation.

quant-ph↗

arXiv2026

Quantum Entropy Contraction and Factorization from Hypercontractivity

We prove that hypercontractivity implies entropy contraction for a single quantum channel, without a detailed balance condition. For primitive quantum Markov semigroups that are KMS-symmetric with respect to a faithful invariant state $σ$, we obtain the modified Log-Sobolev bound $α_1\geq \fracλ{(2+\log\|σ^{-1}\|_\infty)}$ where $λ$ is the spectral gap. This removes the assumption of $L_p$-regularity for the comparison through the log-Sobolev constant. As an application, we show that the hypercontractivity of an average of two conditional expectations implies the approximate tensorization of relative entropy.

quant-ph↗

arXiv2026

A Generalized quantum Stein lemma on von Neumann algebras

We prove a generalized quantum Stein lemma for i.i.d. normal states against convex, tensor-stable families on arbitrary von Neumann algebras. Assuming the existence of an alternative state with finite relative entropy from the null state, we show that, at every type-I error tolerance $\varepsilon\in (0,1)$, the worst case type-II error exponent is achieved with the regularized relative entropy with a strong converse. The proof combines the integral representation of relative entropy by hockey-stick divergences, a modular testing bound, and convex minimax argument.

quant-ph↗

arXiv2026

Conditioning on Subalgebras - Entropy Duality and Generalized Quantum Stein's Lemma in von Neumann Algebras

We establish duality relations for conditional entropies relative to subalgebras of arbitrary von Neumann algebras, including the Umegaki relative entropy as well as the sandwiched- and Petz-Rényi divergences. Even in finite dimensions, these results extend the familiar dualities for conditional entropies from tensor-product subsystems to general subalgebras, while also lifting the framework to the fully infinite-dimensional setting. As an application, we establish an infinite-dimensional extension of quantum Stein's lemma when the alternative hypothesis is the state space of a finite-index subalgebra, which gives an operational interpretation of the relative entropy to a subalgebra as the optimal asymptotic error exponent in composite quantum hypothesis testing-the first beyond finite dimensions.

quant-ph↗

arXiv2026

Tight entropy contraction beyond detailed balance

We establish quantitative entropy contraction bounds for finite-dimensional quantum channels beyond detailed balance. For channels compatible with a faithful conditional expectation, we show that GNS (Gelfand--Naimark--Segal) norm contraction yields relative entropy contraction with only a logarithmic loss in the dimensional constant. For quantum Markov semigroups with a faithful asymptotic conditional expectation, this gives a lower bound on the complete modified logarithmic Sobolev constant in terms of the GNS spectral gap. The bounds allow initial entanglement with an external environment and are independent of its system size. Applications to alternating reservoir pulses and continuously coupled energy ladders give bounds with optimal size dependence.

quant-ph↗

arXiv2026

Relative Entropy Decay via BKM coercivity for Quantum Markov Semigroups

We introduce the notion of the BKM coercivity constant and show that the positivity of this constant governs the exponential relative entropy decay of the semigroup. Indeed, we prove that the modified log-Sobolev constant is equivalent to the BKM coercivity constant up to a constant depending on the asymptotic conditional expectation. We also discover that the BKM coercivity constants for matrix amplifications are already attained with a qubit auxiliary system, which also coincides with the GNS spectral gap of the generating Lindbladian. As a corollary, we establish a criterion that the complete modified log-Sobolev inequality holds if and only if the GNS gap of the semigroup is strictly positive. All the discussion above applies to quantum Markov semigroups that admit a faithful asymptotic conditional expectation as long-time equilibration, with no detailed balance condition assumed. As an application, we show the finite-dimensional Chen-Kastoryano-Gilyén Gibbs samplers always satisfy the complete modified log-Sobolev inequality.

quant-ph↗

arXiv2026

Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors

Quadruped robots have achieved remarkable locomotion, yet their behavioral repertoire remains confined to a few gaits--far from the expressive, companion-like presence long envisioned for them. Attempts to import the humanoid recipe of large-scale motion data have inherited one tacit assumption: that robot motion must first pass through an animal body, making data collection dependent on cooperative animals, reconstruction fragile across species, and retargeting ill-posed across incompatible morphologies. We propose Uni-Mo, a fully automated pipeline that removes the animal from the loop by reframing data scarcity as a generation problem: an LLM proposes motion prompts, a video diffusion model synthesizes the corresponding robot behaviors, and the generated videos are lifted into 3D reference trajectories used to train tracking policies deployed on a real Unitree Go2. To make naively-drifting generations reliably extractable, we introduce an Identity Consistency Loss that enforces appearance coherence across frames. We release Quad-Imaginarium at https://github.com/Amap-Robotics/Quad-Imaginarium, the resulting open-source dataset of 7,488 language-annotated quadruped motions (18.5 hours) spanning acrobatic and performative behaviors. We validate 392 randomly sampled motions on a real Unitree Go2 with a 96.7% deployment success rate, complemented by a 97.6% success rate across the full dataset in simulation.

cs.RO↗

arXiv2026

Minimax games for quantum channel discrimination

Quantum channel discrimination is a primitive task for identifying, verifying, and benchmarking quantum dynamics. Previous studies have primarily considered either the best-case tester-input setting or the worst-case jammer-input setting. Here, we introduce a game-theoretic framework in which both the tester and jammer control separate inputs. Combining three input structures, characterized by whether the tester and jammer use entangled inputs or IID inputs across channel uses, with four information patterns, determined by the visibility of the jammer's strategy and its knowledge of the true hypothesis, yields twelve game models. We provide exact finite blocklength hypothesis testing characterizations of all twelve models in terms of nine minimax hypothesis testing divergences and derive their asymptotic Stein exponents. Notably, for entangled jammers, neither the visibility of the jammer's strategy nor its knowledge of the true hypothesis affects the asymptotic Stein exponent, whereas the information pattern remains consequential for IID jammers. As an example, we study the discrimination of a general channel from a replacer channel and show that all asymptotic Stein exponents coincide with the same additive, single letter quantity. We further develop a general argument that upgrades achievability results to strong converse results, thereby establishing strong converse properties for several game models, resolving an open problem in composite hypothesis testing posed by Berta et al. [Commun. Math. Phys. 385, 55 (2021)], and strengthening several recent results of Lami [arXiv:2510.06340]. The framework and techniques developed here may support future studies of quantum information tasks involving competing roles.

quant-ph↗

arXiv2026

Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection

As the misuse of AI-generated images grows, generalizable image detection techniques are urgently needed. Recent state-of-the-art (SOTA) methods adopt aligned training datasets to reduce content, size, and format biases, empowering models to capture robust forgery cues. A common strategy employs reconstruction techniques, e.g., VAE and DDIM, to construct aligned synthetic negatives. However, relying on a single reconstruction process yields a narrow and homogeneous artifact distribution, leaving forensic traces from other artifact-forming mechanisms underrepresented. To broaden artifact coverage without sacrificing alignment, we construct adversarial restoration-based negatives with SRGAN, whose learned upsampling and texture restoration yield traces complementary to VAE reconstruction while preserving content, size, and format. Directly mixing the two aligned fake domains is nontrivial because their artifact manifolds and optimization directions can conflict. We therefore propose Artifact-Complementary Expert Fusion (ACEF), a two-stage framework for robust AIGC detection. ACEF first constructs two artifact-specific experts via LoRA adaptation on a frozen foundation backbone. It then freezes them and introduces Layer-wise Artifact-Complementary Fusion (LACF) to integrate multi-layer source-specific and cross-artifact evidence through an adaptive gate. Extensive experiments on 13 diverse benchmarks demonstrate the effectiveness of our method.

cs.CV↗

arXiv2026

Tight Entropy Contraction of Generalized Quantum Depolarization

We establish upper and lower bounds for relative entropy contraction of generalized quantum depolarizing channels and semigroups. Our bound provides tight first order asymptotic of the contraction rate in terms of the dimension constant. One side estimate are based on sharp reverse ratio and convexity of relative entropy of two states, which can be derived from the recently introduced Hockey-Stick quantum $f$-divergence, and also independently, Bogoliubov--Kubo--Mori quantum Fisher information metric. The other side follows from the existence of index achieving pure state with respect to a general conditional expectation. Our results extend to the complete entropy contraction rate, tensor stable estimates for product dynamics. Examples include quantum depolarization, dephasing, and compact group symmetrization, with consequences for the decay rate of coherence and asymmetry.

quant-ph↗

arXiv2026

The 1st AI Children Challenge

The First AI Children Challenge aims to advance real-world applications of computer vision and AI in child healthcare, child education, and pediatrics. The 2026 CV4CHL edition featured the first track in this domain: Children Gait Visual Analysis. The main goal of Children Gait Visual Analysis is the fine-grained analysis of children's gait behaviors from keypoint sequences. This is still a big challenge for human action recognition. Experienced medical doctors can distinguish these subtle nuances, but none of the people test AI models in this domain. To bridge this gap, we introduce thousands of 2D children keypoint sequences walking around videos across various age groups of children (3-16 years old). There is a significant opportunity for batch analysis of these videos to provide clinically relevant insights into medical diagnosis. The Challenge will be launched with two problem tracks: Edinburgh Visual Gait Score (EVGS) Scoring and Classification of Gait Patterns in Bilateral Spastic Cerebral Palsy. Each track is chosen in consultation with board-certified pediatricians based on the value of potential solutions. With the first available dataset for such tasks and ground truth for each track, the challenge enabled participants to evaluate their solutions. Final rankings will be revealed after the competition concludes, fostering reproducibility and mitigating overfitting.

cs.CV↗

arXiv2026

ForgeDrive: Bidirectional Cross-Conditioning for Unified Visual-Action Generation in Autonomous Driving

World-model-based autonomous driving endows the model with the ability to understand scene evolution. Yet this promise is undermined by the prevailing imagine-then-act paradigm, which allows errors from the more challenging visual generation stage to cascade into action planning. We introduce ForgeDrive, a unified autoregressive diffusion framework with visual-action cross-conditioning that closes this gap through act-then-imagine paradigm. ForgeDrive factorizes the future as a sequence of per-timestep frame-action pairs, intertwining each action with its corresponding visual observation. During training, we decouple the diffusion timesteps of the two modalities and introduce a UniDiffuser-style noise scheduler to get the ability to infer either modality from its counterpart and deepen understanding of relationships between images and actions. At inference, we propose a novel act-then-imagine inference paradigm, and find that at each step, action generation is a capability internalized during training, requiring no clean future frame as a prerequisite at inference time; instead, the generated action can improve the accuracy of future frame generation, which in turn enhances the quality of the next action. Additionally, we augment each step with future ego-status prediction, further sharpening planning ability. Extensive experiments on NAVSIM demonstrate that ForgeDrive not only unifies driving simulation, planning, and visual odometry into a single model, but also outperforms existing strong planners without any post-training strategy.

cs.CV↗