  We explore the efficacy of the novel use of parametrised quantum circuits
(PQCs) as quantum neural networks (QNNs) for forecasting time series signals
with simulated quantum forward propagation. The temporal signals consist of
several sinusoidal components (deterministic signal), blended together with
trends and additive noise. The performance of the PQCs is compared against that
of classical bidirectional long short-term memory (BiLSTM) neural networks. Our
results show that for time series signals consisting of small amplitude noise
variations (up to 40 per cent of the amplitude of the deterministic signal)
PQCs, with only a few parameters, perform similar to classical BiLSTM networks,
with thousands of parameters, and outperform them for signals with higher
amplitude noise variations. Thus, QNNs can be used effectively to model time
series having, at the same time, the significant advantage of being trained
significantly faster than a classical machine learning model in a quantum
computer.
