Volume 126, Number 6, June 2019
|Number of page(s)||7|
|Published online||22 July 2019|
Deep reinforcement learning for quantum gate control
1 Institute of Physics, Beijing National Laboratory for Condensed Matter Physics, Chinese Academy of Sciences Beijing 100190, China
2 School of Physical Sciences, University of Chinese Academy of Sciences - Beijing 100049, China
3 Collaborative Innovation Center of Quantum Matter - Beijing 100190, China
4 Songshan Lake Materials Laboratory, Dongguan - Guangdong 523808, China
Received: 22 March 2019
Accepted: 20 June 2019
How to implement multi-qubit gates efficiently with high precision is essential for realizing universal fault-tolerant computing. For a physical system with some external controllable parameters, it is a great challenge to control the time dependence of these parameters to achieve a target multi-qubit gate efficiently and precisely. Here we construct a dueling double deep Q-learning neural network (DDDQN) to find out the optimized time dependence of controllable parameters to implement two typical quantum gates: a single-qubit Hadamard gate and a two-qubit CNOT gate. Compared with traditional optimal control methods, this deep reinforcement learning method can realize efficient and precise gate control without requiring any gradient information during the learning process. This work attempts to pave the way to investigate more quantum control problems with deep reinforcement learning techniques.
PACS: 03.67.Ac – Quantum algorithms, protocols, and simulations / 03.67.Lx – Quantum computation architectures and implementations / 07.05.Mh – Neural networks, fuzzy logic, artificial intelligence
© EPLA, 2019
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