Quantum Approaches to Data Science and Data Analytics

Dipak Shankar Raskar, Rajiv Iyer, Vedprakash Chandrakant Maralapalle

Nana Liu

Rashi Sharma, Anand Sharma

Havana Rika, Aviv Itzhak, Aviv Bertha

In the field of People Intelligence, Emotion Analytics is one of the emerging and growing challenges. A prominent study field is analyzing an individual's emotional state from textual data, as well as recognizing emotions from audio and video recordings. Current Artificial Intelligence approaches for Emotion Analytics based on machine and deep learning and neural networks based on classic data science approaches assume rational people's decision-making process. People's decision-making is irrational. As a result of recent quantum cognition advancements, we show that emotional judgments from one modality may be incompatible with judgments from another, and they cannot be assessed together to produce a final judgment. As a result, the cognitive process exhibits "quantum-like" biases that classical AI approaches based on probability models challenged to analyze. As a result, we offer an emotion analytics approach based on the quantum data science method for predicting people's emotions by a fundamentally novel assessment method.

P. Prasant, Hariharan R, E Sudha +3 more

Koffka Khan

Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.