Graph States of Light
Quantum Graph States are a fundamental resource for quantum information processing. Different graph states can be used for universal quantum computing, quantum repeaters and quantum sensing. While the generation of those states has been proved to be remarkably hard, recent developments in the manipulation of so-called Quantum Dots (QD) allow for completely new directions in the generation of photonic graph states. Single QDs can be used to deterministically generate a few types of arbitrarily large graph states, which can be fused together via linear optical circuits to generate larger and arbitrarily complex states. The optimal quantum circuits and codes for creating photonic graph states using linear optics is, however, not known. By optimal we mean the scheme that reduces the engineering cost of its implementation to the minimum. Here, we address this big challenge head-on. Equipped with the new QD resource, this project proposes the development of a novel simulation framework to support the design of linear optical circuits to generate large dimension cluster states based on quantum dots with realistic error models.
Quantum Hybrid quantum-classical simulation and optimization platform for industrials
To address the problems at hand, we are going to start with Theory-Trained Neural networks (TTNs). Shallow theory-trained neural networks have so far been successfully used for learning the solution of highly coupled differential equations in small systems. This project is to scale up both TTNs and Physics-Informed Machine Learning methods for simulating wind dynamics and to build a real-time forecasting platform to optimize wind power generation. Furthermore, we develop even more efficient algorithms by employing theory-trained quantum neural networks, instead of or in addition to the classical neural network. The algorithms we will be employing includes convolutional neural network, Long-Short-Term-Memory network, Variational Quantum Eigensolver, Quantum Monte Carlo, Quantum Approximate Optimization Algorithm. Applying such techniques to large data sets, training deep neural networks, and computational fluid dynamics simulations are computationally heavy and can only be run on HPC in a timely manner.