arXiv subjects

Zhenlin Zhao

Publications and source records attributed to Zhenlin Zhao.

6 recordsLinked to original sources

arXiv2026

Experimental demonstration of phase-conjugated twin-wave coherent reception over 100 km with injection locking assisted phase stabilization

Recovering complex optical signals with high sensitivity is central to coherent communication and sensing. Beyond conventional homodyne and heterodyne reception, phase-conjugate twin-wave encoding and phase-sensitive joint detection offer an alternative strategy with enhanced sensitivity. Extending this approach to long-distance transmission of high-order-modulated signals is challenging because optical phase coherence must be maintained at the receiver. Here we experimentally demonstrate injection-locking-assisted phase-conjugated twin-wave reception of 200-MBaud signals with modulation formats up to 256-QAM over 100 km of optical fiber. Optical injection locking regenerates the local oscillator (LO) from a reference transmitted through the 16 dB-loss link, while pilot-assisted optical feedback stabilizes the relative signal-LO phase, enabling coherent combining of the phase-conjugated twin waves at a single balanced photodetector. At equal received signal-photon number per quadrature in each data-carrying optical mode, the measured PCTW signal-to-noise ratio (SNR) exceeds the ideal single-mode homodyne standard quantum limit (SQL) benchmark. The SNR gains over the measured homodyne and conventional heterodyne results are about 3 dB and 6 dB, respectively. Constellation and bit-error-rate measurements show that this gain improves high-order data recovery relative to heterodyne reception. An ideal Gaussian-noise model predicts a photon information efficiency of 1.54 bits per received signal photon for uniform 16-QAM. These results establish an injection-locking-assisted coherent receiver architecture that implements joint detection of phase-conjugated twin waves over 100 km of fiber with enhanced sensitivity, providing a practical route for photon-efficient coherent communication.

physics.optics↗

arXiv2026

Delta-sigma noise shaping for low-resolution quantization in Gaussian-modulated continuous-variable quantum key distribution

Low-resolution digital-to-analog conversion can simplify continuous-variable quantum key distribution (CVQKD) transmitters, but coarse quantization increases excess noise and can generate data-correlated spectral replicas. We demonstrate digital delta--sigma modulation (DSM) for low-resolution waveform generation in Gaussian-modulated CVQKD. In a 500-MBaud CVQKD system over 20~km of fiber, 3-bit second-order DSM achieves an estimated asymptotic secret key rate of 46.38~Mbps at an oversampling ratio of 12, whereas uniform quantization yields no positive rate. With 4-bit second-order DSM, the rate reaches 50.11~Mbps, approximately 97\% of the 12-bit reference. A noise model combining the DSM noise-transfer function with matched filtering guides the sampling-rate and signal-band design for effective noise suppression. We also identify a phase-conjugate spectral replica arising from third-order intermodulation between the pilot tone and quantum signal, and evaluate its key-rate penalty using a linear-replica security model. At 3-bit resolution and an oversampling ratio of 24, first-order DSM reduces the experimentally estimated leakage-mode strength by approximately four orders of magnitude. Together, our findings establish the security boundaries and operating conditions of DSM, providing design guidance for low-resolution CVQKD transmitters.

quant-ph↗

arXiv2026

RamanPFN: learning from Raman spectral structure with a tabular foundation model

Raman spectroscopy enables label-free molecular characterization across materials science, analytical chemistry, biomedicine, and industrial process monitoring. However, machine learning for high-dimensional spectroscopy remains constrained by limited labelled data and a mismatch between the physical organization of spectra and feature-agnostic models. Channel coverage alone does not ensure that related bands share a common inference context. Here we present RamanPFN, a general-purpose spectral foundation framework that enables unified in-context inference through physics-guided spectral learning. It captures full-spectrum compositional covariation via Global Compositional Unmixing (GCU), which decomposes distributed, multi-band mixture signatures into shared non-negative latent bases. Simultaneously, it resolves local vibrational structure through Local Vibrational Subspace Encoding (LVSE), which preserves fine-grained peak morphology, intensity fluctuations, and peak shifts within contiguous spectral neighborhoods. Extensive evaluation across 74 diverse public Raman datasets covered 129 regression targets and was further extended to 21 classification tasks. RamanPFN achieved state-of-the-art performance across all reported aggregate metrics against 28 independently reproduced methods spanning chemometrics, spectral neural networks, deep tabular learners and tabular foundation models. RamanPFN establishes a physics-guided paradigm for scientific spectroscopy, enabling data-efficient predictive learning across diverse chemical systems.

cs.LG↗

arXiv2026

Deterministic Minimum-Leakage Continuous-Variable Quantum Key Distribution with Phase-Conjugated Twin Beams

Minimum-leakage continuous-variable quantum key distribution suppresses Eve's Holevo information by engineering the signal ensemble during state preparation. The existing symmetric realization relies on Alice-side heralding. Here we propose an alternative two-mode symmetric realization that is deterministic without heralding. The minimum-leakage condition of the new protocol requires transmitting two phase-conjugated twin beams (PCTB). We show that the PCTB protocol is related to the heralding protocol through a common entanglement-based source but corresponds to a different prepare-and-measure decomposition. The two protocols attain the same secret key rate per transmitted optical mode in the very-large-squeezing limit. For finite squeezing, however, the PCTB protocol requires approximately 3 dB less squeezing to achieve the same performance. We further optimize mode-symmetric Gaussian two-mode entangling-cloner attacks and find that correlated ancillary modes provide only a limited advantage over independent attacks under the minimum-leakage condition. These results establish phase-conjugated twin beams as a deterministic and experimentally appealing route to symmetric minimum-leakage CV-QKD.

quant-ph↗

arXiv2026

Self-Evolving Skills via Surrogate-Guided Solve-and-Reproduce

Agent skills are portable packages of instructions and resources an agent consults at deployment. Self-evolving them fails in two ways today. First, skills evolved from scratch underperform human-curated ones and, on a weak model, using no skill at all. Second, an evolution-time pass records one lucky trajectory that a fresh stochastic agent often fails to reproduce at deployment. We present reSolve, a per-task, oracle-in-the-loop framework built on three components. It decouples interactive solving from a self-contained deliverable that is independently re-executed in a fresh container, a protocol we call solve-and-reproduce. It enhances the sparse reward signal with a surrogate verifier that cannot access hidden tests or reference answers. It then runs verifier-guided beam search over a solution-construction graph. Within a fixed harness, a cheap model self-evolves skills that reach $74.9\%$ mean-of-3, $+14.8$ points over the $60.1\%$ human-curated baseline, exceeding the strongest official curated-skill result ($67.3\%$, GPT-5.5/OpenHands). We also report observed failure cases and domain-level results, including performance on the 14 Natural Science tasks, to clarify when the approach does and does not help.

cs.AI↗

arXiv2026

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development. However, existing benchmarks often suffer from limited task diversity, fragmented datasets, and inconsistent evaluation protocols, making it challenging to systematically assess the reliability and generalization of AI models. In this work, we introduce Chem World, a comprehensive benchmark for chemical property prediction that integrates 17 diverse chemical datasets with over 800,000 molecular samples, covering various properties including density, electrical conductivity, solubility, and other molecular characteristics. Chem World provides a unified platform for evaluating AI models across multiple property prediction tasks. Furthermore, we propose Mixture-PINN, a physics-informed neural network based prediction framework that incorporates chemical prior knowledge into data-driven learning, improving the accuracy, robustness, and reliability of chemical property prediction. Extensive experiments on Chem World demonstrate the effectiveness of our approach compared with existing methods. By combining large-scale standardized evaluation with physics-informed learning, Chem World establishes a foundation for developing trustworthy AI systems for computational chemistry and advancing AI-driven scientific discovery.

cs.LG↗