Quantum Simulation Enabled by Photonic Tensor Processing

Objective

Current quantum simulation and computing algorithm research still relies on classical digital architectures, most notably GPU-accelerated tensor processing, since practical quantum computers remain in their early stages. However, with Moore’s Law approaching its limits, digital platforms face mounting challenges in power consumption and scalability, making large-scale quantum simulations increasingly costly and inefficient. Photonic computing has recently emerged as a promising alternative, offering ultra-high bandwidth, massive parallelism, and low energy consumption. Yet, existing photonic paradigms remain limited: they either excel only at narrow tasks or deliver performance comparable to GPUs in general tensor operations, leaving them unsuitable for large-scale quantum simulation. To address this gap, this project introduces a new paradigm of high-order photonic tensor processing that directly bridges classical optical computing with quantum simulation. The proposed platform unites flexibility with massive parallelism, enabling efficient, scalable simulations that surpass conventional digital approaches and accelerate the development of next-generation quantum technologies. The key goals of the project are: (1) To design a high-order photonic tensor computing platform for the simulation of quantum computing algorithms and quantum machine learning frameworks; (2) To provide comprehensive training to Dr. Yufeng Zhang, a skilled and talented researcher, to establish him as a leading scientist in the rapidly evolving field of photonic computing and quantum technology. This multidisciplinary project has great implications on photonic computing, computer science, and quantum technology such as quantum simulation, algorithms, and quantum machine learning, and will involve both fundamental and applied research.