Tensor Networks for Quantum Simulation and Computation

Quantum computing promises significant advancements in the simulation of complex quantum systems, with potential applications spanning from material science to drug development. However, the current generation of quantum computers, known as noisy intermediate-scale quantum (NISQ) devices, are constrained by their limited size, both in terms of the number of qubits and the depth that quantum circuits can reach. This limitation restricts the scale of problems that can be effectively computed and brings into question whether there will be any practical advantage using NISQ devices. Conversely, tensor networks provide a powerful method for the classical simulation of quantum systems, although they struggle with highly complex systems that involve many interactions. By combining quantum computing and tensor networks, there is potential to gain new insights into many-body quantum systems.

This PhD will focus on the development of new quantum algorithms for quantum simulation based on tensor network methods. This includes the simulation of open quantum systems, which are found in various physical and chemical processes and are challenging to simulate classically due to their complex interactions with the environment. The quantum algorithms developed will be benchmarked for accuracy and efficiency, and run on state-of-the-art quantum computers, with the goal of improving NISQ quantum algorithms for quantum simulation.