Learning to Program Variational Quantum Circuits with Fast Weights
Samuel Yen-Chi Chen
Quantum Machine Learning (QML) has surfaced as a pioneering framework
addressing sequential control tasks and time-series modeling. It has
demonstrated empirical quantum advantages notably within domains such as
Reinforcement Learning (RL) and time-series prediction. A significant
advancement lies in Quantum Recurrent Neural Networks (QRNNs), specifically
tailored for memory-intensive tasks encompassing partially observable
environments and non-linear time-series prediction. Nevertheless, QRNN-based
models encounter challenges, notably prolonged training duration stemming from
the necessity to compute quantum gradients using backpropagation-through-time
(BPTT). This predicament exacerbates when executing the complete model on
quantum devices, primarily due to the substantial demand for circuit evaluation
arising from the parameter-shift rule. This paper introduces the Quantum Fast
Weight Programmers (QFWP) as a solution to the temporal or sequential learning
challenge. The QFWP leverages a classical neural network (referred to as the
'slow programmer') functioning as a quantum programmer to swiftly modify the
parameters of a variational quantum circuit (termed the 'fast programmer').
Instead of completely overwriting the fast programmer at each time-step, the
slow programmer generates parameter changes or updates for the quantum circuit
parameters. This approach enables the fast programmer to incorporate past
observations or information. Notably, the proposed QFWP model achieves learning
of temporal dependencies without necessitating the use of quantum recurrent
neural networks. Numerical simulations conducted in this study showcase the
efficacy of the proposed QFWP model in both time-series prediction and RL
tasks. The model exhibits performance levels either comparable to or surpassing
those achieved by QLSTM-based models.