arXiv subjects

Yingying Hong

Publications and source records attributed to Yingying Hong.

4 recordsLinked to original sources

arXiv2026

Measurement-Feedback Quantum Information Engine: Coherence-Transition Interference and Correlated Work Statistics

Measurement and feedback jointly prepare coherence and select finite-time dynamics in quantum information engines. Complementing a companion experimental realization, we develop a mechanism-resolved theory of the resulting work statistics and temporal correlations. The con?ditional work separates into population transfer and a phase-sensitive coherence-transition inter?ference term. Symmetric full counting statistics maps this interference to an equal-and-opposite half-quantum pair in the work quasiprobability, entering odd moments while leaving even moments fixed by endpoint mixing. An outcome-resolved tilted kernel then propagates these statistics through the correlated measurement record, yielding finite-cycle and fixed-time fluctuation corrections. The same memory reduces the reversible record-reset cost from the one-symbol entropy to the entropy rate. Our results link coherent work statistics, feedback memory, and information thermodynamics

quant-ph↗

arXiv2026

Experimental Demonstration of a Measurement-Feedback Quantum Information Engine

Harnessing finite-time nonadiabatic transitions that are conventionally associated with quantum inner friction for useful work extraction remains an open experimental challenge. Here we address this issue by introducing and experimentally realizing an innovative measurement-feedback quantum information engine model in the trapped 40Ca+ ion system, in which the projective measurement replaces the hot reservoir as a nonthermal energy source and the feedback control conditionally steers the system through either unitary compression-expansion strokes or thermalization. We experimentally show that the repeated feedback cycle converges to a stable operating regime with a resolved energetic balance, and, by controlling the measurement angle and stroke duration, measurement-induced coherence and the finite-time nonadiabatic contribution can enhance work extraction and raise the efficiency above the corresponding Otto benchmark. The experimental results further show that, over finite ranges of stroke durations, the efficiency and intrinsic cycle power can increase simultaneously. Our experiment establishes a route toward information-to-work quantum engines that convert finite-time irreversibility into performance-enhancing resources.

quant-ph↗

arXiv2026

Geometric Mode Steering of the Quantum Mpemba Effect

The slowest Liouvillian mode often bottlenecks the relaxation of an open quantum system to its steady state. Standard strategies circumvent this bottleneck by selecting special initial states or engineering the dissipator. Here we show that neither is necessary. We introduce a pre-dissipative geometric steering protocol that reshapes any given pure or mixed state before relaxation begins -- coherent rotations interleaved with nonselective projective measurements -- at fixed Lindblad generator. By steering the state's Bloch direction along geodesic paths, the protocol suppresses its overlap with the slowest Liouvillian modes. The prepared state then starts farther from equilibrium yet relaxes faster, realizing the quantum Mpemba effect, whenever two computable conditions hold: reduced slow-mode overlap and a larger initial distance to stationarity. Our framework treats real and complex spectral gaps uniformly, and we demonstrate robust Mpemba acceleration in driven qubit and multiqubit systems using operations available in trapped-ion and superconducting platforms.

quant-ph↗

arXiv2025

PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning

Existing tool-augmented agentic systems are limited in the real world by (i) black-box reasoning steps that undermine trust of decision-making and pose safety risks, (ii) poor multimodal integration, which is inherently critical for healthcare tasks, and (iii) rigid and computationally inefficient agentic pipelines. We introduce PASS (Probabilistic Agentic Supernet Sampling), the first multimodal framework to address these challenges in the context of Chest X-Ray (CXR) reasoning. PASS adaptively samples agentic workflows over a multi-tool graph, yielding decision paths annotated with interpretable probabilities. Given the complex CXR reasoning task with multimodal medical data, PASS leverages its learned task-conditioned distribution over the agentic supernet. Thus, it adaptively selects the most suitable tool at each supernet layer, offering probability-annotated trajectories for post-hoc audits and directly enhancing medical AI safety. PASS also continuously compresses salient findings into an evolving personalized memory, while dynamically deciding whether to deepen its reasoning path or invoke an early exit for efficiency. To optimize a Pareto frontier balancing performance and cost, we design a novel three-stage training procedure, including expert knowledge warm-up, contrastive path-ranking, and cost-aware reinforcement learning. To facilitate rigorous evaluation, we introduce CAB-E, a comprehensive benchmark for multi-step, safety-critical, free-form CXR reasoning. Experiments across various benchmarks validate that PASS significantly outperforms strong baselines in multiple metrics (e.g., accuracy, LLM-Judge, semantic similarity, etc.) while balancing computational costs, pushing a new paradigm shift towards interpretable, adaptive, and multimodal medical agentic systems.

cs.AI↗