Evaluating hybrid quantum-classical learning for financial forecasting: a comparative study with classical machine learning models on BSE indices.

Rashmi Nair, Pankaj Chandre, Shahin Makubhai, Bhagyashree Shendkar, Disha Deotale, Vijaya Patil

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Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Rashmi NairDepartment of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Pune, India.

Pankaj ChandreDepartment of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Pune, India.

Shahin MakubhaiDepartment of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Pune, India.

Bhagyashree ShendkarDepartment of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Pune, India.

Disha DeotaleDepartment of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Pune, India.

Vijaya PatilDepartment of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Pune, India.

Funding

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6 · The paper itself

Abstract

The forecasting of financial time series has gained more significance in decision making within a dynamic economic setting. Over the last few years, there has been a push in exploring both classical machine learning methods and novel quantum machine learning models with a view to enhance predictive accuracy. This paper suggests a quantum-classical regression framework developed that combines the feature selection in the form of a Random Forest in a hybrid approach, dimensionality reduction in the mode of Principal Component Analysis (PCA), and predicting in the form of a Variational Quantum Circuit (VQC). It is tested in various financial indicators, as BSE ESG, Carbon Exchange, Energy, Green Exchange, Oil and Gas and Power, with the help of RMSE and

Indexed as

financial time series predictionhybrid modelsprincipal component analysisquantum machine learningRandom Forest regressionVariational Quantum Circuit

Identifiers

PMID42751118

DOI10.3389/frai.2026.1902018

PMCPMC13579563

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.