Shiji Zhang | Searcharxiv

arXiv2026

A Differentiable Physical Framework for Goal-Driven Spin-State Engineering in Magnetic Resonance Spectroscopy

Magnetic Resonance Spectroscopy (MRS) offers a unique non-invasive window into metabolic processes, yet its potential remains strictly constrained by severe spectral congestion and intrinsic insensitivity. Traditional pulse sequence design, tethered to human intuition, predominantly targets simple quantum states, thereby overlooking the vast majority of the exponentially scaling operator space which consists of complex spin superpositions. Here, we introduce a spectrum-driven, end-to-end differentiable physical framework that transcends these heuristic limitations. By integrating physical laws with automatic differentiation algorithm, our approach directly navigates the high-dimensional spin dynamics space, bypassing the intractable inverse problem of state preparation. This enables the discovery of non-intuitive, complex mixed states that simultaneously satisfy the dual objectives of selective excitation and interferometric signal enhancement. We validate this paradigm by achieving the robust separation of Glutamate and Glutamine, which is a longstanding neuroimaging challenge, in the human brain at 3T, demonstrating spectral fidelity superior to conventional methods. By unlocking the "dark" informational content of nuclear spin ensembles, our work establishes a generalizable paradigm for goal-driven quantum state engineering in magnetic resonance and beyond.

quant-ph↗

arXiv2023

Redundancy-free integrated optical convolver for optical neural networks based on arrayed waveguide grating

Optical neural networks (ONNs) have gained significant attention due to their potential for high-speed and energy-efficient computation in artificial intelligence. The implementation of optical convolutions plays a vital role in ONNs, as they are fundamental operations within neural network architectures. However, state-of-the-art convolution architectures often suffer from redundant inputs, leading to substantial resource waste. Here, we propose an integrated optical convolution architecture that leverages the inherent routing principles of arrayed waveguide grating (AWG) to execute the sliding of convolution kernel and summation of results. M*N multiply-accumulate (MAC) operations are facilitated by M+N units within a single clock cycle, thus eliminating the redundancy. In the experiment, we achieved 5-bit precision and 91.9% accuracy in the handwritten digit recognition task confirming the reliability of our approach. Its redundancy-free architecture, low power consumption, high compute density (8.53 teraOP mm^-2 s^-1) and scalability make it a valuable contribution to the field of optical neural networks, thereby paving the way for future advancements in high-performance computing and artificial intelligence applications.

cs.ET↗