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Chao-Yang Zhang

Publications and source records attributed to Chao-Yang Zhang.

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A Chip-scale Space-time Multiplexed Gaussian Boson Sampling Processor Beyond 10,000 Photons

Gaussian boson sampling (GBS) has emerged as a leading photonic paradigmfor demonstrating quantum computational advantage. Nevertheless, state-ofthe-art GBS setups face practical barriers including stringent optical alignment, phase instability, and limited programmability, which impede scalable engineering deployment. The chip-scale space-time multiplexed architecturepromises to resolve these constraints, yet it strongly demands wafer-scale chipcapabilities to simultaneously satisfy stringent requirements on low loss, highprecision and high-speed modulation. Here we report the first chip-scale spacetime multiplexed GBS system, monolithically integrating high-speed electrooptic modulators, on-chip delay lines, and a time-space multiplexed interferometric network on a thin-film lithium niobate chip, operating at a 4-GHz clockrate with detection events of up to 11,059 photons within 1 millisecond. Beyond benchmarking quantum advantage, we further reconfigure the photonichardware into a GBS-powered world model for modelling physical dynamics,which achieves lower prediction error with fewer trainable readout parameters compared with a classical echo state network (ESN) baseline. Our resultsvalidate the feasibility of our endeavor towards scalable photonic quantumhardware, and pave the way for the versatile programmable applications offuture GBS quantum systems.

quant-ph

Formula-Guided Machine Learning for Ground Vibration Propagation and Attenuation Modeling

Understanding the propagation and attenuation patterns of ground vibrations is critical for evaluating the impact of environmental disturbances on large-scale scientific facilities. However, complex site conditions often result in intricate vibration behaviors, limiting the accuracy of traditional predictive methods. This study proposes a hybrid iterative fitting method that integrates machine learning with the Bornitz formula through an intelligent formula generation model. The method enables the automatic derivation of high-precision, interpretable ground vibration attenuation formulas from experimental data. A case study was conducted at the High Energy Photon Source in Beijing, where field tests were performed to collect vibration data. Using the proposed approach, an attenuation formula describing ground vibration propagation was derived. The physical validity of the model was further verified via finite element simulations. A probabilistic analysis was then employed to estimate computational errors. Comparative evaluations with black-box machine learning models and empirical formulas from previous studies demonstrate that the proposed method offers significant advantages in both interpretability and accuracy. These findings provide a valuable framework for vibration impact assessment and mitigation in other large-scale scientific infrastructure projects.

physics.app-ph