Quantum enhanced neural networks

This project will contribute to QCS WP9 Algorithms with strong links to WP8 Architectures, Control and Emulation. The research in WP9 investigates how next generation NISQ devices could be used to solve optimization problems and how they can be interfaced with classical data sources. More specifically, this project is concerned with the application of quantum computers to accelerate the performance of neural networks. Recently, it has been shown that quantum algorithms can take advantage of the exponentially large quantum Hilbert space to obtain an enhanced solution when it comes to classifying data [Havlicek et al., Nature 567, 7747 (2019)]. In this project, the DPhil student will implement novel variational quantum circuits on a quantum computer to solve industrially relevant problems in machine learning and artificial intelligence. Based on current timetables for the future availability of quantum hardware (e.g. through our partnership with IBM and through collaboration with WP1 through WP6), we expect our quantum algorithms to outperform classical computers within the next 3 - 5 years, exploring classically intractable feature spaces. Since neural networks are ubiquitous in all areas of science and technology, our method has the potential to leverage quantum-enhanced algorithms in a broad range of real-world applications