This paper is a survey on the use of fully-quantum digital recurrent neural networks (QRNNs) for time-series forecasting with real-world data. It discusses precedent QRNN papers, their methodology and evaluation strategies, and highlights that, despite their intuitive applicability to the problem, QRNNs haven't been rigorously evaluated with forecasting tasks. Finally, it presents some real-data forecasting experiment results and a couple examples of how experiment setup can affect the outcomes. These results serve to inform and motivate future work, with the goal of useful QRNN prediction.
This paper is a survey on the use of fully-quantum digital recurrent neural networks (QRNNs) for time-series forecasting with real-world data. It discusses precedent QRNN papers, their methodology and evaluation strategies, and highlights that, despite their intuitive applicability to the problem, QRNNs haven't been rigorously evaluated with forecasting tasks. Finally, it presents some real-data forecasting experiment results and a couple examples of how experiment setup can affect the outcomes. These results serve to inform and motivate future work, with the goal of useful QRNN prediction.
The analysis of historical data allows the execution of predictive tasks such as weather and stock price forecasting. To achieve these goals, Recurrent Neural Networks are implemented in classical computers and, in recent years, quantum methods have also emerged to perform prediction tasks based on the analysis of historical series, which have been called Quantum Recurrent Neural Network (QRNN). The objective of this work is to identify and review the main QRNNs discussed in the literature. A literature search in google scholar resulted in eight relevant papers that were reviewed. In general, the QRNNs show better training accuracy and stability compared to classical methods. It is not possible to speak of a training time advantage with the noisy and low-scale quantum computers currently available.
Classical models for time series forecasting, despite their success, often face challenges related to training, generalization, energy consumption and interpretability. Unconventional computing paradigms, such as quantum computing, offer a promising avenue to address these limitations. Quantum Recurrent Neural Networks (QRNN) emerge as a powerful approach for multivariate time series prediction. Due to some features, the QRNN model we study is not supported in many quantum computers. We develop a specific-purpose emulator to address this barrier, and then we benchmark the model against datasets of varying complexities, involving realistic features such as noisy outputs. The results show significant performance compared to other well-known classical approaches for time series prediction.
Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.
Time series forecasting remains a significant challenge in various domains, including finance, weather prediction, and industrial process control. Traditional neural networks, particularly recurrent neural networks (RNNs), often struggle to effectively capture the intricate temporal dependencies inherent in time series data. This paper proposes a novel approach leveraging quantum-inspired algorithms to optimize RNNs for improved time series forecasting performance. Specifically, we explore the utilization of quantum annealing techniques to adjust the weights within an RNN architecture. This approach mimics key quantum principles, such as superposition and interference, to potentially enhance the network's ability to represent and predict complex temporal patterns. The core claim of this work is that classical neural networks, particularly RNNs, are limited in their ability to model temporal dependencies. We introduce a mechanism where quantum-inspired algorithms, particularly quantum annealing, are employed to optimize the weights of RNNs. The underlying principles of superposition and interference are leveraged to capture more complex temporal patterns. The results demonstrate the potential of this quantum-inspired approach to achieving superior forecasting accuracy compared to conventional RNNs.
Quantum neural network (QNN) is one of the promising areas in which noisy medium-scale quantum devices (MCD) can find advantageous applications in the short term compared to classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but there is still no canonical model of a quantum recurrent neural network (QRNN), which certainly limits research in the field of quantum deep learning. In the present paper, a new type of QRNS is proposed that would be a good candidate as a canonical QRNN model, where quantum recurrent blocks (QRB) are built in a hardware-efficient way, and QRNN are constructed by stacking QRB in a checkerboard pattern, which can significantly reduce the algorithm’s requirements with respect to coherent time of quantum devices. That is, QRNN is much more accessible on MCD devices. Numerical experiments show that our QRNN provides much better performance in prediction (classification) accuracy compared to classical RNN and state-of-the-art QNN models for sequential training and can predict changing details of time series data.
Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent blocks (QRBs) are constructed in the hardware-efficient way, and the QRNN is built by stacking the QRBs in a staggered way that can greatly reduce the algorithm's requirement with regard to the coherent time of quantum devices. That is, our QRNN is much more accessible on NISQ devices. Furthermore, the performance of the present QRNN model is verified concretely using three different kinds of classical sequential data, i.e., meteorological indicators, stock price, and text categorization. The numerical experiments show that our QRNN achieves much better performance in prediction (classification) accuracy against the classical RNN and state-of-the-art QNN models for sequential learning, and can predict the changing details of temporal sequence data. The practical circuit structure and superior performance indicate that the present QRNN is a promising learning model to find quantum advantageous applications in the near term.