  High-fidelity quantum gate design is important for various quantum
technologies, such as quantum computation and quantum communication. Numerous
control policies for quantum gate design have been proposed given a dynamical
model of the quantum system of interest. However, a quantum system is often
highly sensitive to noise, and obtaining its accurate modeling can be difficult
for many practical applications. Thus, the control policy based on a quantum
system model may be unpractical for quantum gate design. Also, quantum
measurements collapse quantum states, which makes it challenging to obtain
information through measurements during the control process. In this paper, we
propose a novel training framework using deep reinforcement learning for
model-free quantum control. The proposed framework relies only on the
measurement at the end of the control process and offers the ability to find
the optimal control policy without access to quantum systems during the
learning process. The effectiveness of the proposed technique is numerically
demonstrated for model-free quantum gate design and quantum gate calibration
using off-policy reinforcement learning algorithms.
