Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits

Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally calculated classically from repeated measurement outcomes, such that it is no longer encoded in a quantum state. This prevents the value from being directly manipulated by a quantum computer for algorithms such as gradient estimation using the Hadamard Test. In this talk, we introduce a routine to embed a cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We characterize the utility of such a routine using numerical simulations and introduce proof-of-principle experiments in an optimized superconducting qubit device.