Speaker
Description
In recent years, quantum machine learning (QML) has emerged as a rapidly expanding field within quantum algorithms and applications. Current noisy quantum devices enable small-scale experiments on existing quantum hardware, while increasingly powerful classical hardware allows for the simulation of quantum algorithms and the execution of robust classical AI applications.
Recently, hybrid quantum-classical approaches have been explored to utilize both high-performance classical and noisy quantum computing resources. Among these hybrid methods, variational quantum algorithms are the most extensively studied.
We examine the application of quantum reinforcement learning (QRL) in a hybrid quantum-classical system, where the quantum agent is represented by a parametric quantum circuit (PQC), optimized via gradient descent by a classical optimizer. However, many reinforcement learning environments possess high-dimensional observation spaces, such as visual observations, with feature vectors in the thousands. This makes it infeasible to encode these feature vectors into currently available quantum devices, which typically have only a few dozen noisy qubits.
To address these limitations, we investigate the use of classical autoencoders (AEs) to reduce the dimensionality of the original feature spaces and encode the latent feature variables into quantum states. While a similar approach has been tested in a fully classical scenario to our knowledge, it has not yet been applied to quantum agents.
We simulate these experiments using state-of-the-art quantum simulators and optimization frameworks. Our preliminary results indicate that this hybrid approach enables the use of quantum agents in reinforcement learning environments, which would not be feasible without the application of autoencoders.
