Variational Quantum Circuits for Deep Reinforcement Learning

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, a couple of tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum machine learning is hard to simulate classical deep learning models because of the intractability of deep quantum circuits. Thus, it is necessary to design approximated quantum algorithms for quantum machine learning. 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. On the other hand, we use a quantum information encoding scheme to reduce the number of model parameters as small as the scale of $poly(\log{} N)$ in contrast to $poly(N)$ in a standard configuration. Besides, our variational quantum circuits can be deployed in many near-term noisy intermediate quantum machines.