10001969Quantum enhanced control systemsClosedFeasibility StudiesISCFMany industries stand to benefit from the commercialisation of quantum computing, particularly those industries that need high levels of processing power, such as the autonomous vehicle market. Quantum computers can provide a huge increase in processing speed for a number of applications in chemistry, materials science, and general linear algebra operations, and their potential for use within finance and pharmaceuticals is being explored. In this project, we will explore and develop quantum computing solutions for autonomous vehicles, and more specifically driverless cars. The aim of this project is to develop an end-to-end control system deployed in cars, where quantum computers are used to enhance the decision-making process in the control system. Autonomous systems need to repeatedly take decisions as to whether they should take a specific action or not. This is a difficult challenge, particularly when the input from different sensor data is considered. For example, deciding whether a lane change is safe is relatively straight forward for humans, but is difficult for automated control systems. QCs process data in an inherently parallel way, with a possibilistic outcome of the measurements. These can provide complementary information to the control system and hence enhance its decision-making capabilities. In a recent joint research collaboration, Massive Analytic Limited (MAL) and the National Physical Laboratory (NPL) have demonstrated that neural networks implemented on quantum computers, the so-called Quantum Neural Networks, can predict the safety of specific autonomous car manoeuvres. This result was shown on a simplified system as proof of concept. In this project, we will extend this to a real-life scenario, where the decision depends on the positions and velocities of multiple surrounding cars, and integrate the quantum neural networks in MAL's end-to-end commercial APACC control framework of a driverless car. To this aim, we will combine the expertise in control systems of MAL and the quantum software expertise at NPL, and use the autonomous systems dynamics and test facilities at the Centre for Autonomous and Cyber-Physical Systems in Cranfield University. Quantum Neural Networks have been shown to train faster than classical models for certain cases, and hence have the potential to outperform classical machine learning algorithms used in the autonomous vehicle industry. We will systematically assess this in the project. If successful, it will be a disruptive enhancement to MAL's commercial APACC control system, giving it a significant advantage over competitors.