Quantum Closed-loop Design of Diquats for Use as Electrolytes in Redox Flow Battery Systems
The proposed project aims at a closed-loop discovery of organic redox flow battery electrolytes. In this continuous workflow, properties of organic molecules are predicted using machine learning (ML) models and/or computed using quantum mechanical models, which allows leveraging the costs of the experiments. Then, the lead candidates are synthesized and characterized using automated systems. Finally, the results of characterization are used for adjusting the computational models. We focus on a specific class of organic molecules –diquats– that show high redox reversibility and good chemical stability. A virtual screening pipeline will be developed using proprietary software provided by Kebotix Canada.
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.
Optical Approaches to Modular Quantum Computers
Numerical assessments will be done by simulating quantum circuits under realistic conditions defined by the architecture. The main tool for the simulations will be a python based simulator which is currently being developed by Entangled Networks. The simulated quantum circuits will involve many qubits which are represented by large matrices of order 2^n x 2^n where n is the number of qubits. These need to be stored and manipulated at each time step. Operations on these very large arrays are performed across distributed parallel nodes, with access to non-trivial amounts of memory (RAM), current estimate is 64GB per node and a total of 512GB in total.