Research Area

Noisy Intermediate-Scale Quantum (NISQ) era hybrid quantum/classical algorithms for quantum chemical simulations.

Objective and Background

Near-term quantum computing holds the promise of enabling substantial improvements in quantum chemistry simulations. Whilst there are obvious reasons to expect so (such as having access to controllable entanglement between qubits on demand), developing a fully functional quantum chemistry simulation strategy compatible with the main limitations of current and future hardware requires answering several fundamental questions. In particular, near-term devices can only run “shallow circuits” (few operations per iteration of whichever algorithm under use) and rely on repeated measurements.

There is rich body of research already done on classical algorithms for quantum chemistry simulations, however, all the currently known methods (be it CASSCF, DMRG etc) eventually fail to capture the strongly interacting behaviour of larger, more complex systems due to their inability to deal adequately with an exponentially growing Hilbert space. On the other hand, quantum computation comes with the limitations of high gate errors and limited availability of shots. Further, one cannot always perform full quantum state tomography on these devices due to similar issues with an exponential number of measurements required.

Many of these options can be circumvented using Informationally Complete Positive Operator Value Measures (IC-POVMs) to reconstruct expectation values of interest without the need for full quantum state tomography. Hence, a rational chemistry simulation paradigm must be able to naturally incorporate the main techniques developed for classical computational chemistry in such a way that they can benefit from quantum advantage. The proposed research will be conducted within this framework.

On the one hand, I will develop techniques and tools to incorporate paradigmatic methods for embedding (such as DMET), short-range DFT, and FMO, amongst others, on near-term devices by exploiting the skilful use of IC POVMs, which enable extracting multiple physico-chemical properties of the system under study simultaneously. On the other hand, I will explore the possibility of using quantum computing to enhance state-of-the-art machine learning methods for computational chemistry as well as for the exploration of chemical compound space, which can potentially result in important improvements in drug discovery and design.

Expected Results

I hope that upon the completion of this project, hybrid quantum/classical algorithms will successfully be employed to boost already existing classical algorithms. Furthermore, I hope that progress will be made in realising quantum advantage for quantum chemistry simulations. I expect that many of the bottlenecks associated with today's simulation methods will be alleviated and that the methods developed in this project will be already fit for purpose in the drug discovery industry. 

Research References

https://arxiv.org/abs/2212.11405

https://arxiv.org/abs/2212.09719