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Lecture notes in computer science

Performance of Qubit Neural Network in Chaotic Time Series Forecasting

Taisei Ueguchi, Nobuyuki Matsui, Teijiro Isokawa

Published Jan 1, 2016• 6 citations
Publisher
DOI
10.1007/978-3-319-46675-0_28

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A qubit neural network (QNN) is a neural network that incorporates the quantum computing and representation. QNN is constructed from a set of qubit neuron model, of which internal state is a coherent superposition of qubit states. This paper evaluates the performance of QNN through a prediction of well-known Lorentz attractor, which produces chaotic time series by three dynamical systems. The experimental results show that QNN can predict time series more precisely, compared with conventional (real-valued) neural networks.
Continuous Variable-based Quantum Computing (CVQC) has been developing at speed with a lot of promise in the field of quantum machine learning. It provides a direct and natural way to accommodate continuous values into the quantum computing framework. We carried out experiments on quantum simulators where we compared the results of time series forecasting on a type of continuous-variable quantum variational circuit, called CV-Quantum Neural Networks (CVQNN) for different types of time series like Energy Consumption data and stock price data. We compared their performance with a discrete variable-based variational quantum algorithm as well as with a classical Neural Network. Experiments showed that CVQNN can function just like a neural network but with a lesser number of parameters while tackling the two obstacles that are faced in qubit-based computing, which are, tackling continuous values and introducing controlled non-linearity into the circuits. We used the circuit for multi-step forecasting that performed better for a larger prediction window than one-step forecasting done iteratively on the predicted data. The resembling architecture of CVQNN with that of a neural network offers the flexibility of using a similar structure for both one-step and multi-step forecasting.