Quantum-enhanced machine learning using phosphorus-doped silicon quantum dots

Recently, quantum machine learning has garnered intense interest due to the promise for improvements over classical ML and its applicability on near-term hardware. In particular, recent theoretical work has shown that quantum kernel functions admit a provable quantum advantage over classical kernels. Quantum kernels are often used as part of variational hybrid quantum-classical machine learning algorithms that are within the reach of NISQ computers. Typically, the optimisation of ansatz circuits used in this variational technique is costly, requiring significant time and often running into barriers such as the barren plateau problem. Randomisation can be used as an alternative to optimisation in this context. By creating an enhanced feature space using randomness, comparable results to kernel functions can be achieved in a shorter time. In this talk, I will show how we can leverage atomically precise manufacturing of phosphorus dopants in silicon to realise a quantum system for generating features for use in a random quantum feature algorithm. We experimentally demonstrate the algorithm on various datasets and find the performance is competitive with equivalent classical methods. The results indicate that this is a promising approach for achieving near-term quantum advantage.