About the project
This PhD project investigates improving the robustness of variational quantum algorithms on noisy quantum computers. Using simulated quantum systems, it explores error-correction-inspired approaches and learned redundancy within hybrid classical-quantum frameworks. The research can be theoretical or computational, aiming to enable larger, more reliable quantum computations despite hardware noise and decoherence.
By utilizing quantum entanglement and parallelism, quantum computers are, in theory, able to quickly solve many problems that cannot be efficiently solved on traditional computers. However, in the near term, system noise and decoherence significantly limit their utility as they limit the number of operations that can be performed before system noise destroys computational results.
To overcome these limitations, variational quantum algorithms have been proposed, which have some inherent robustness to noise. These hybrid classical-quantum methods utilize the advantages of noisy near-term quantum computers whilst using well established classical optimization methods to adapt a small number of quantum operations.
Beyond these current developments, a key mid-term goal in quantum computation is the push for ever larger quantum computers that will allow the use of many more computational steps. The hope is that in addition to hardware improvements, noise robustness can be achieved utilizing error correction methods.
Your work will explore the development of methods using simulated quantum computers that incorporate error correction ideas and in particular the use of learned redundancy within the variational computational framework. The goal is to increase the robustness of these methods to noise and to thus allow for the use of more complex computational structures.
This is a field where there is significant scope that allows you to develop and follow your own interests and to pursuit different directions, whether these are theoretical (by looking at the mathematical properties of the methods) or computational (by experimentally exploring the practical performance of methods).
