Abstract
The previous chapter looked into strategies of implementing inference algorithms on a quantum computer, or how to compute the prediction of a model using a quantum instead of a classical device. This chapter will be concerned with how to optimise models using quantum computers, a subject targeted by a large share of the quantum machine learning literature.
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Notes
- 1.
An assumption that allows us to omit that the bounds also depend on the norm.
- 2.
The initial state plays the role of the uniform superposition that we started with in the original quantum approximate optimisation algorithm. It can be prepared by starting with the state \( \bigotimes \limits _j \sqrt{2 \cosh (\delta ) \sum _{\pm } \mathrm {e}^{\mp \frac{\beta }{2} | \pm \rangle _j | \pm \rangle _{E_j}}}\) and tracing out the \(E_j\) (‘environment’) register (which has as many qubits as the visible register) [26].
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Schuld, M., Petruccione, F. (2018). Quantum Computing for Training. In: Supervised Learning with Quantum Computers. Quantum Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-96424-9_7
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