  The state-of-the-art machine learning approaches are based on classical von
Neumann computing architectures and have been widely used in many industrial
and academic domains. With the recent development of quantum computing,
researchers and tech-giants have attempted new quantum circuits for machine
learning tasks. However, the existing quantum computing platforms are hard to
simulate classical deep learning models or problems because of the
intractability of deep quantum circuits. Thus, it is necessary to design
feasible quantum algorithms for quantum machine learning for noisy intermediate
scale quantum (NISQ) devices. This work explores variational quantum circuits
for deep reinforcement learning. Specifically, we reshape classical deep
reinforcement learning algorithms like experience replay and target network
into a representation of variational quantum circuits. Moreover, we use a
quantum information encoding scheme to reduce the number of model parameters
compared to classical neural networks. To the best of our knowledge, this work
is the first proof-of-principle demonstration of variational quantum circuits
to approximate the deep $Q$-value function for decision-making and
policy-selection reinforcement learning with experience replay and target
network. Besides, our variational quantum circuits can be deployed in many
near-term NISQ machines.

Machine Learning;Artificial Intelligence;Quantum Physics;Machine Learning