arXiv subjects

Yuegang Li

Publications and source records attributed to Yuegang Li.

4 recordsLinked to original sources

arXiv2026

Converting Quantum Sensing Noise into Erasures

Erasures are more favorable for quantum sensing than unflagged errors such as Pauli errors. However, realistic sensing noise does not usually appear as erasures; it often acts within the same sensing Hilbert space as the signal, making it difficult to identify and mitigate. For such noise, we establish a noise-model-agnostic necessary and sufficient condition for erasure conversion, identifying the noise components that can be converted into erasures and removed without damaging the signal. For components satisfying the condition, conversion can be realized by a passive dimension-lifted scheme requiring neither detailed noise knowledge nor active control. Theoretically, the protocol remains effective over a broad range of noise strengths and approaches the corresponding precision limit. Experimentally, in single-photon phase sensing, we recover standard-quantum-limit precision in a Pauli-noise channel with erasure-convertible weight 0.5, using orbital angular momentum as the ancilla. These results provide a practical route to robust quantum sensing under realistic noise.

quant-ph↗

arXiv2026

Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm

Spatial photonic Ising machines (SPIMs) based on spatial light modulators (SLMs) have emerged as highly effective solvers for many tasks, including combinatorial optimization problems and spin-glass simulations. However, traditional SPIMs relying solely on the simulated annealing algorithm require a large number of measurement-feedback iterations to find a relatively optimal solution in complex energy landscapes, suffering from slow convergence and high time cost. Here, we propose an optical genetic-simulated annealing hybrid algorithm to accelerate the ground-state search of SPIMs. GA conducts a global coarse-grained search in the early iteration stage, while SA performs fine-grained local refinement in the late stage. Numerical simulations show that our method enables a higher solution quality of full-rank Max-Cut problems than pure GA or SA at different scales. We also experimentally demonstrate its superiority over conventional algorithms on a gauge-transformation time-division multiplexing SPIM for high-rank optimization problems under the same iteration budget. Our approach can be further developed with other advanced metaheuristic algorithms toward intelligent optical Ising computing systems.

physics.optics↗

arXiv2026

Incorporating rank-free coupling and external field via an incoherent modulated spatial photonic Ising machine

Spatial photonic Ising machines offer a novel optical platform for optimization and spin-model simulation, but existing diffraction-based schemes rely on auxiliary spins or multiplexing to encode high-rank couplings and external fields, reducing either speed or spin count. We demonstrate an amplitude-only, rank-free spatial photonic Ising machine in which arbitrary Ising Hamiltonians are encoded as Hadamard products on aligned amplitude and binary spatial modulators and read out by a single-pixel intensity measurement. The machine directly programs fully connected 797-spin Ising models with external fields at nearly 9-bit precision and operates at a constant iteration rate of ~200 Hz. By removing zero-valued product terms, the same architecture scales to sparse problems and experimentally solves a Max-Cut instance on a 424,108-vertex Mobius ladder graph. We also observe the phase transition of the Sherrington-Kirkpatrick model, demonstrating programmable optical simulation beyond low-rank couplings. These results establish amplitude modulation as a scalable route to programmable photonic Ising machines.

physics.optics↗

arXiv2025

Real-time imaging through dynamic scattering media enabled by fixed optical modulations

Dynamic scattering remains a significant challenge to the practical deployment of anti-scattering imaging. Existing methods, such as transmission matrix measurements, iterative wavefront shaping, and optical phase conjugation, depend on a quasi-static assumption, requiring the object and scattering medium to remain stable during a single imaging process to enable one-to-one compensation. However, image reconstruction becomes unattainable when this assumption is violated. Here, we propose a novel imaging strategy that counteracts time-dependent scattering perturbations through a fixed modulation module. This one-to-many compensation mechanism is realized via optical diffraction neural networks (ODNNs) trained on simulated datasets. For the first time, we reveal that its feasibility stems from the optical shower-curtain effect, and its effectiveness typically within 1-2 transport mean free paths. Our approach is not only immune to speckle decorrelation but also leverages it to enhance image quality. ODNNs generalize effectively to real-world scattering medium scenarios via multi-simulated scattering media learning and reconstruct images in real time with light-speed processing. We achieve 80 Hz imaging of moving objects in dynamic scattering media with decorrelation times < 1 ms, demonstrating applicability in large-field-of-view imaging and incoherent illumination scenarios. This work lays the foundation for high-speed, intelligent optical imaging, advancing practical anti-dynamic scattering techniques.

physics.optics↗