QuantumLeap: Hybrid quantum neural network for financial predictions

Eric Paquet · Farzan Soleymani

87 citations0/0 files storedExpert Systems with Applications

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Stock Market Forecasting Methods Neural Networks and Applications Quantum Computing Algorithms and Architecture

Abstract

This paper introduces a new hybrid deep quantum neural network for financial predictions, the QuantumLeap system. This system consists of an encoder that transforms a partitioned financial time series into a sequence of density matrices; a deep quantum network that predicts the density matrix at a later time; and a classical network that measures, from the output density matrix, the maximum price reached by a security at a later time. The deep quantum network is isomorphic to a deep classical network and is computationally tractable. A hybrid deep network is associated with each time stride, allowing for parallelisation of the learning process. The classical network is a learnable measurement apparatus which infers, from the output density matrix, the maximum price reached by a security for a given time. Experimental results associated with 24 securities clearly demonstrate the accuracy and efficiency of the system in both the regression and extrapolation regimes.

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References

2020Stock markets’ reaction to COVID-19: Cases or fatalities?2014Stock Price Prediction Using the ARIMA Model 2020COVID–19’s Impact on Stock Prices Across Different Sectors—An Event Study Based on the Chinese Stock Market 2019Stock Market Prediction Based on Generative Adversarial Network 2012The stock market crash of 2008 caused the Great Recession: Theory and evidence 2002A discussion on embedding the Black–Scholes option pricing model in a quantum physics setting 1998Forecasting irish inflation using ARIMA models 2009Interest Rates and Coupon Bonds in Quantum Finance

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