Measurement-Based Adaptation Protocol with Quantum Reinforcement Learning in a Rigetti Quantum Computer
We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiauton...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/97953 |
| Acesso em linha: | https://hdl.handle.net/11441/97953 https://doi.org/10.3390/quantum2020019 |
| Access Level: | acceso abierto |
| Palavra-chave: | Quantum artificial intelligence Quantum machine learning Quantum reinforcement learning Cloud quantum computer Rigetti quantum computer State estimation |
| Resumo: | We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits. |
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