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

Understand

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.