Machine Learning as the Interface Between Quantum and Classical Computation

Due to the exponential scaling in the dimensionality of many-body quantum systems, the study of large and even intermediate sized quantum systems using classical computers can prove intractable. This has ramifications in both the simulation and study of naturally occurring quantum mechanical phenomena and in the development of quantum computing. We have reached the stage where there exist quantum simulators of great enough size that performing classical simulation and analysis of them is unfeasible using standard computational techniques. A promising alternative to these methods has been found in applying machine learning ideas to the study of quantum physics. The hidden/visible layer structure of artificial neural networks provides sufficient representational power to describe quantum states (resulting in a "neural network state") but for generic states this still requires unfeasible computational resources. The size of system at which machine learning techniques can describe a quantum state can in many cases vastly outmatch what is otherwise possible with classical computation and so machine learning could be thought of as the "interface" between classical and quantum computation. The problem of finding an efficient representation for a quantum state has been approached in numerous ways and for some systems there exist highly compressed descriptions of the states (E.G. tensor networks, MPS). Recent work has shown that neural network states trained with measurement data can also provide efficient representations of many physical states, however freedom in the choice of neural network poses several currently open questions. In this way neural network states provide a useful tool for quantum state tomography, allowing one to recreate a quantum state from a set of measurement data. An increasingly popular choice of neural network state is the Restricted Boltzmann Machine (RBM) which has shown great potential in learning many states while also being able to cope with a degree of long-range entanglement. This project aims to investigate the representational power of RBMs for tomography and attempt to determine at what size and level of entanglement of the system in question does the representation cease to be efficient. These machine learning techniques will then be applied in performing tomography on some of IBMs prototype quantum processors to both demonstrate the practical utility of this concept and quantify the performance of these processors.

The main impact of the proposed Hub will be in training quantum engineers with a skillset to understand cutting-edge quantum research and a mindset toward developing this innovation, and the entrepreneurial skills to lead the market. This will grow the UK capacity in quantum technology. Through our programme, we nurture the best possible work force who can start new business in quantum technology. Our programme will provide multi-level skills training in quantum engineering in order to enhance the UK quantum technologies landscape at several stages. Through the training we will produce quantum engineers with training in innovation and entrepreneurship who will go into industry or quantum technology research positions with an understanding of innovation in quantum technology, and will bridge the gap between the quantum physicist and the classical engineer to accelerate quantum technology research and development. Our graduates will have to be entrepreneurial to start new business in quantum technology. By providing late-stage training for current researchers and engineers in industry, we will enhance the current landscape of the quantum technology industry. After the initial training composed of advanced course works, placements and short projects, our students will act as a catalyzer for collaboration among quantum technology researchers, which will accelerate the development of quantum technology in the UK. Our model actively encourages collaboration and partnerships between Imperial and national quantum tehcnology centres and we will continue to maintain the strong ties we have developed through the Centre for Doctoral Training in order to enhance our on-going training provisions. The Hub will also have an emphasis on industrial involvement. Through our new partnerships students will be exposed to a broad spectrum of non-academic research opportunities. An important impact of the Hub is in the research performed by the young researchers, PhD students and junior fellows. They will greatly enhance the research capacity in quantum technology. Imperial College has many leading engineers and quantum scientists. One of the important outcomes we expect through this Hub programme is for these academics to work together to translate the revolutionary ideas in quantum science to engineering and the market place. We also aim to influence industry and policy makers through our outreach programme in order to improve their awareness of this disruptive technology.