Connecting quantum computing and machine learning to improve quantum simulation and optimization

Shradha Deshmukh, Bikash K. Behera, Preeti Mulay

Quantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation, the machine learning models are continuously evolving. Hence, the new challenge is to improve the existing complex and critical machine learning training models. Therefore, the healthcare sector is shifting from a classical to a quantum domain to sustain patient-oriented attention to healthcare patrons. This paper presents a hybrid classical-quantum approach for training the unsupervised data models. In order to achieve good performance and optimization of the machine learning algorithms, a quantum k-means (QK-means) clustering problem was deployed on the IBM quantum simulators, i.e.,the IBM QASM simulator. In the first place, the approach was theoretically studied and then implemented to analyze the experimental results. The approach was further tested using small synthetics and cardiovascular datasets on a qsam simulator to obtain the clustering solution. The future direction connecting the dots is the incremental k-means algorithm with the quantum platform, which would open hitherto unimaginable technological doors.

K. Balasubramanian, T. Ganesan, K. Kalaivani +1 more

This paper examines quantum computing’s revolutionary potential to overcome classical computing’s limitations and unleash unprecedented computational power to promote innovation across many industries. Quantum computing might revolutionize AI, cryptography, simulations, and finance by solving scalability, optimization, and real-time processing issues. Traditional computer approaches struggle with complicated difficulties, especially in machine learning (ML), which demands more efficient processing due to enormous datasets and numerous sophisticated models. We introduced Quantum-Inspired Hybrid Machine Learning Models (Q-IHMLM) that combine traditional and quantum techniques. This paradigm aims to bridge this gap. Q-IHMLM uses quantum mechanics to boost computational efficiency, which speeds up data processing, improves forecasts, and improves financial modeling for portfolio optimization, risk assessment, and fraud detection. The suggested technique is adaptable since it is employed in big data analytics, AI model training, optimization, and financial analysis. The abstract should be refined to explicitly highlight the quantum circuit structure used, detailing gate composition and qubit connectivity. It must also describe the encoding mechanisms that map classical data into quantum states. Additionally, the role of algorithmic differentiation in training and optimization should be clarified. These elements will provide a more complete technical foundation for the proposed work. Q-IHMLM speeds up problem-solving while maintaining accuracy and saving processing resources, making real-world applications more efficient and scalable. We found that Q-IHMLM outperforms other approaches in real-time data processing, accuracy, and scalability. This achievement affects the banking industry and other organizations and sets the way for computer technology improvements.

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

T. Shreekumar, K. M. Rashmi, Ravinarayana +1 more

Subrata Paul, Anirban Mitra

Computational technologies drive progress in industry, science, government, and society. While these technologies form the foundation for intelligent systems and enable scientific and business innovation, they are also the limiting factors for progress. Quantum computing promises to overcome these limitations with better and faster solutions for optimization, simulation, and machine learning problems. Quantum computing is broadly applicable to business problems in optimization, machine learning, and simulation, impacting all industries. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Therefore, it is instrumental for industry to seek an active role in this emergent ecosystem. In this chapter, the authors present a brief overview on various applications of quantum computing in machine learning.