Quantum Software for a Digital Universe

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics


Major progress in science is marked by the bringing into its scope aspects of the world that had previously been considered to be fixed and absolute, but are revealed instead to be dynamical and contingent. Darwin's discovery of the origin of the species by the process of evolution by natural selection is a prime example, as is Einstein's discovery that spacetime itself has its own laws of motion that couple its behaviour to the matter within it. Such discoveries always open the door to further questions -- unthinkable before the shift in world-view -- and in the case of spacetime, we are confronted today with questions of a profound and cosmological nature involving the interaction and ultimate relationship between spacetime and quantum matter. This project will develop new quantum software, namely quantum algorithms, to address fundamental physical questions about quantum field theory (QFT) in the early universe, particle astrophysics and black holes on the assumption that spacetime is, at some level, digital or discrete and also respects Lorentz invariance.

The fundamental physics aims of this project are: i) discover the effect of discreteness on perturbations of an interacting quantum scalar field theory such as the inflaton in the early universe and, alternatively, model initial density perturbations arising from pre-Big Bang dynamics of an evolving discrete cosmos; ii) account for the entropy of a black hole by a state counting method where the states are discrete states of the horizon; iii) discover phenomenology of quantum particles of astrophysical or cosmological origin in a digital spacetime background. Heeding the stringent bounds on violation of Lorentz invariance, we will use a discrete dataset that can underpin a continuum spacetime approximation and also be Lorentz invariant. A random, discrete partial order or causal set is such a dataset. Such a mathematical object is difficult to deal with analytically and numerical calculations are crucial for progress, especially for obtaining testable predictions from phenomenological models. The numerical methods, however, also have their limitations since classical algorithms require extensive computational power and time, especially in the phenomenologically relevant case of 4-dimensional spacetime.

Our approach is to develop numerical methods for those physical questions, that use quantum algorithms, at least for expensive subroutines, and we will prioritise quantum algorithms that can be implemented on Noisy Intermediate Scale Quantum devices. The techniques we will use include: the variational quantum eigensolver, the quantum approximate optimisation algorithm, quantum annealing and adiabatic quantum computing, as well as quantum walks. The ambition is that these quantum algorithms will offer advantage in comparison with classical numerical methods for these novel investigations of the physics of a digital universe, and be an important tool for investigating and testing further discrete models for fundamental physics in the near-future. These quantum algorithms will first be tested in an emulation environment using High Performance Computing and specifically the Archer2 national leading facility. The effects of noise on the performance will be considered and we will extrapolate to understand the scales on which these algorithms will outperform classical numerical methods. Eventually, we aim to run these quantum algorithms on physical devices -- digital quantum computers -- once our confidence in the potential advantage is founded and access to suitable hardware is obtained. We will seek to obtain such access reaching out to hardware developed within the UK National Quantum Technology Programme: via our committed engagement with the Quantum Computing and Simulation Hub, or by approaching the National Quantum Computing Centre and quantum hardware companies involved in the UK National Quantum Technology Programme, such as Rigetti and IBM.


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