Tesi etd-02162021-104714 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
SEMOLA, RUDY
URN
etd-02162021-104714
Titolo
Quantum Control via Deep Reinforcement Learning using IBMQ platform and Qiskit Pulse
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Bacciu, Davide
relatore Prati, Enrico
relatore Prati, Enrico
Parole chiave
- deep learning
- deep reinforcement learning
- Proximal Policy Optimization
- Qiskit Pulse
- quantum computing
- quantum control
- quantum Information processing
- transmon qubit
Data inizio appello
05/03/2021
Consultabilità
Non consultabile
Data di rilascio
05/03/2091
Riassunto
The work investigates in use of Reinforcement Learning in the domain of Quantum Control using Qiskit Pulse framework and IBMQ Platform for transmon superconducting qubit systems.
It is proposed a quantum control framework applied to an open-loop control optimization through reinforcement learning.
In particular, the thesis focuses on realizing analogue-layer controls that would implement a driven X90 gate and any state transition from the ground state to target quantum state.
It is proposed a quantum control framework applied to an open-loop control optimization through reinforcement learning.
In particular, the thesis focuses on realizing analogue-layer controls that would implement a driven X90 gate and any state transition from the ground state to target quantum state.
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