Quantum computing (QC) is revolutionizing data analysis by improving the performance of artificial intelligence (AI) algorithms. The convergence of these two paradigms has given rise to quantum machine learning (QML), which is emerging as a major driver of analytical development across various fields of knowledge, including market research. While traditional market analysis involves data collection to estimate very simple models that describe the essence of consumer behavior, QC can simultaneously analyze multiple variables and scenarios to generate a range of outcomes and their associated probabilities of occurrence, thus offering a far more comprehensive view of the playing field to facilitate strategic decision-making. This entry explores the role that QML can play in digital market research and pinpoints four key areas for potential improvement: enhancement of simulation models, optimization of marketing strategies, generation of AI-based solutions, and advancement of quantum cryptography to protect consumer data. Although the digital ecosystem generates an abundance of information, it is mostly unstructured. As market research typically relies on structured and codified data, comprehensive analysis remains a major challenge in this field. In particular, this entry considers qualitative research using natural language processing (NLP). These quantitative algorithms are used to analyze text in order to extract consumers’ ratings, opinions, and decision-making patterns. By applying QML to such practices, market researchers will be able to overcome many of the limitations of traditional methods in the digital ecosystem, and will therefore gain a better understanding of customer behavior and be able to make more accurate strategic decisions.