The Variational Quantum Eigensolver: a review of methods and best practices
(2021)
Characterisation of spatial charge sensitivity in a multi-mode superconducting qubit
(2021)
High Coherence in a Tileable 3D Integrated Superconducting Circuit Architecture
(2021)
Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits
Physical Review A American Physical Society 101:5 (2020) 52309
Abstract:
Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum devices, parametrized quantum circuits 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. To solve this problem, we give a routine to embed the cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We also demonstrate the ability to evaluate the gradient of the encoded cost function in a quantum state.Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits
(2019)