Quantum Machine Learning Integration: A Novel Approach to Business and Economic Data Analysis
1 Master’s in Commerce, Jagannath University College, Dhaka, Bangladesh.
2 Bachelor of Science in Computer Science and Engineering, Dhaka International University, Bangladesh.
3 Bachelor of Business Administration (BBA in Finance), Northern University, Bangladesh.
4 Bachelor of Arts with Honours in Marketing Management, London School of Management Education, Ilford, England.
5 Bachelor of Arts with Honours in English, National University, Bangladesh.
Research Article
World Journal of Advanced Research and Reviews, 2021, 12(01), 558-566
Publication history:
Received on 19 September 2021; revised on 27 October 2021; accepted on 29 October 2021
Abstract:
Quantum computing, which is based on the principles of quantum mechanics, opens the door to solving problems that are intractable for classical computers through leveraging quantum effects such as superposition, entanglement, and quantum parallelism. When tasks become more challenging in areas such as finance, healthcare, and transportation, then boosting computational power is a must. Solution: Quantum computing should enhance the efficiency and accuracy with which such complex problems are solved, potentially providing transformative opportunities for businesses and researchers. This work investigates the challenges and possibilities of exploiting Quantum Machine Learning (QML) and Quantum Annealing (QA) algorithms for optimising complex problems. We concentrate on problems like supply chain optimisation, financial forecasting, and data analysis in general, industries where other approaches fail to deliver an optimal solution. Our results suggest that quantum algorithms, particularly in optimisation and machine learning problems, are highly competitive for processing large datasets and complex systems. Quantum solutions are faster and more efficient in their computation, leading to better results for business decisions and problem-solving. Quantum computing is still in its infancy, but there’s no denying that the nuances of data analysis and optimisation across multiple industries could be disrupted. Further progress in error correction and multiplier construction is critical to achieve the full potential of this algorithm, to allow its exploitation as a practical tool.
Keywords:
Quantum Computing; Quantum Machine Learning (QML); Quantum Annealing (QA); Optimization; Machine Learning; Supply Chain Optimization; Data Analysis; Computational Efficiency
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