Comparisons of performance between quantum-enhanced and classical machine learning algorithms on the IBM Quantum Experience
2021
Machine learning is now widely used almost everywhere, primarily for forecasting.
In the broadest sense, the machine learning objective may be summarized as an approximation problem, and the issues solved by various training methods can be reduced to finding the
optimal value of an unknown function or restoring a function. At the moment, we have only experimental samples of quantum computers based on classical-quantum logic, when quantum
gates are used instead of ordinary logic gates, and probabilistic quantum bits are used instead of deterministic bits. Namely, the probabilistic nature problems that provide for the determination of a certain optimal state from a large set of possible ones on which quantum computers can achieve “quantum supremacy” – an extraordinary (by many orders of
magnitude) reduction in the time required to solve the task. The main idea of the work is to identify the possibility of achieving, if not quantum supremacy, then at least a quantum
advantage when solving machine learning problems on a quantum computer.
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
8
References
1
Citations
NaN
KQI