SearcharxivSearch

arXiv subjects

Chenhao Zhao

Publications and source records attributed to Chenhao Zhao.

3 recordsLinked to original sources

Quantum simulation of slow analytic time-dependent Hamiltonians

We develop a quantum algorithm for slow analytic Hamiltonians $\widetilde H(t)=H(t/T)$ with $\|H(s)\|\leq\alpha$ that achieves nearly additive query complexity and low gate overhead. Our main technical contribution is a periodic Gevrey extension of $H(s)$, together with Fourier component decay and truncation bounds that enable an efficient finite-dimensional simulation. Combined with Floquet embedding and optimal time-independent Hamiltonian simulation technique, this gives query complexity $\widetilde{\mathcal O}\!\left(\alpha T+\log(1/\varepsilon)\right)$ and additional gate complexity $\widetilde{\mathcal O}\!\left((\alpha T+\log(1/\varepsilon))^2\log(1/\varepsilon)\right)$, assuming coherent access to $H'(s)$ and endpoint derivatives. For slow analytic control Hamiltonians, only block encodings of the time-independent control operators are required, with the same query complexity and lower gate overhead. Our method also extends to Gevrey Hamiltonians and improves the precision dependence for simulating slow analytic semi-dissipative linear differential equations.

quant-ph

Nonlinear atomic tunnelling boosted by bright squeezed vacuum

Nonlinear optical processes, mediated by multiphoton interactions rather than single-photon response, are routinely exploited to enable a range of light-based functionalities in devices and applications. Nonlinear effects are enhanced through higher intensity fields, which is a limiting strategy owing to potential radiation damage. An alternative strategy relies on the fluctuation redistribution typical of quantum light, but experimental demonstrations at the most fundamental level have been limited. Here we report experimental nonlinear tunnelling ionization of isolated atoms, a pivotal nonlinear process that drives high-harmonic generation and forms the basis of attosecond science, boosted by quantum light -- bright squeezed vacuum (BSV). A BSV light with an average pulse energy of 300 nJ achieves an effective intensity equivalent to that of a coherent light with 7.1 {\textmu}J, demonstrating a more than 20-fold quantum boost in nonlinear effect from BSV light. This boost is revealed by matching the peaks of the photoelectron momentum spectra produced by the BSV and coherent light using angular streaking. Furthermore, we demonstrate control of the effective intensity of the BSV by tuning the correlation function at fixed average pulse energy, establishing a robust method to tailor nonlinear processes via quantum statistics rather than classical intensity scaling. These findings may facilitate the development of quantum-controlled strong-field dynamics using tailored quantum light sources.

quant-ph

UT-OSANet: A Multimodal Deep Learning model for Evaluating and Classifying Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder that is associated with increased risks of cardiovascular morbidity and all-cause mortality. While existing diagnostic approaches can roughly classify OSA severity or detect isolated respiratory events, they lack the precision and comprehensiveness required for high resolution, event level diagnosis. Here, we present UT OSANet, a deep learning based model designed as a event level, multi scenario diagnostic tool for OSA. This model facilitates detailed identification of events associated with OSA, including apnea, hypopnea, oxygen desaturation, and arousal. Moreover, the model employs flexibly adjustable input modalities such as electroencephalography (EEG), airflow, and SpO 2. It utilizes a random masked modality combination training strategy, allowing it to comprehend cross-modal relationships while sustaining consistent performance across varying modality conditions. This model was trained and evaluated utilizing 9,021 polysomnography (PSG) recordings from five independent datasets. achieving sensitivities up to 0.93 and macro F1 scores of 0.84, 0.85 across home, clinical, and research scenarios. This model serves as an event-level, multi-scenario diagnostic instrument for real-world applications of OSA, while also establishing itself as a means to deepen the mechanistic comprehension of respiratory processes in sleep disorders and their extensive health implications.

eess.SP