Quantum Machine Learning in Finance: Time Series Forecasting (2202.00599v1)

Abstract: 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.

• The paper demonstrates that PQC-based quantum neural networks achieve competitive forecasting accuracy versus classical BiLSTM models, especially under high-noise conditions.
• It employs a six-layer, 16-qubit circuit with parameterized XX, ZZ, and YY gates to efficiently process periodic financial signals.
• The findings suggest reduced overfitting and faster training times, highlighting potential breakthroughs for real-time financial applications like portfolio optimization.

Quantum Machine Learning in Finance: Time Series Forecasting

The research paper entitled "Quantum Machine Learning in Finance: Time Series Forecasting" by Dimitrios Emmanoulopoulos and Sofija Dimoska proposes an investigation into the utilization of Parameterized Quantum Circuits (PQCs) within Quantum Neural Networks (QNNs) for forecasting financial time series. This exploration is motivated by the potential of quantum computing to enhance computation speeds and offers an alternative perspective on tackling the complexities inherent in financial forecasting.

Overview

The paper evaluates PQCs applied to time series forecasting, comparing their performance against classical BiLSTM (bidirectional Long Short-Term Memory) neural networks. The deterministic component of the data is derived from stock price adjustments of Apple Inc., while the frameworks are challenged with signals embedded with various levels of noise and trends. Of interest is the comparative efficacy between quantum and classical neural network workflows in handling these noisy data variations, quantified via signal-to-noise ratios.

Technical Analysis

The crux of the paper lies in discovering that PQCs, despite having fewer parameters, perform comparably to classical BiLSTM networks when handling time series with low noise interference (up to 40% of deterministic signal amplitude). Particularly notable is their outperforming capability in high-noise scenarios, where PQCs exhibit less overfitting tendency. This aligns with theoretical underpinnings that suggest trigonometric functions governed by PQCs efficiently align with periodic signal constituents — a harmonic resonance absent in classical sigmoid or polynomial structures.

A significant architecture choice is the quantum circuit's design:

• A six-layer structure with 16 qubits, interconnected through parametrized XX, ZZ, and YY gates.
• The computational model executes quantum forward propagation with contemporary classical backpropagation.

The classical alternative, a complex BiLSTM setup, utilized 175,648 trainable parameters and was executed within the TensorFlow framework. Illustrative results demonstrated the QNN's ability to achieve impressive accuracy with a fraction of the parameters, reflecting the innate efficiency of quantum state superposition.

Implications and Speculations

From a theoretical perspective, the findings reinforce the potential paradigm shift quantum computing introduces to machine learning, particularly in fields necessitating real-time data processing like finance. Reduced training times due to the smaller parameter space and potential quantum advantage (if fully quantum optimization methods mature) could operationalize superior prediction tools against the backdrop of quantum-enhanced models.

Practically, assuming advancements in quantum hardware and algorithms, the financial industry could witness transformative impacts. For instance, volatility prediction, algorithmic trading, and portfolio optimization could leverage quantum-enhanced forecasts for potentially higher yield strategies or risk management techniques.

Future Directions

The evolution of these models requires further exploration into integrating large-scale data volumes compatible with existing quantum computing constraints. Continued development in robust quantum optimization is essential for realizing the promise of fully quantum backpropagation, paving the way for end-to-end quantum machine learning pipelines.

Moreover, other lucrative areas include transactional fraud prediction using quantum anomaly detection strategies, albeit challenges persist in processing voluminous and imbalanced categories of data. These implementations would necessitate collaboration between quantum computing progress and financial analytics, fostering innovations transcending conventional computational limitations.

In summary, this paper delineates a compelling narrative for quantum neural networks in financial time series forecasting, advocating for continued research and development at the intersection of quantum computing and machine learning methodologies. Through demonstrating tangible benefits even with current constraints, it marks a promising step towards harnessing quantum advantages in practical financial applications.