Quantum Ventures: Accelerating Startups with QML (#106)

Authors

Acuña Acuña, Edwin Gerardo

Abstract

This article analyzes the transformational potential of Quantum Machine Learning (QML) as a strategic facilitator for early-stage entrepreneurs in high-complexity sectors. This work utilizes quantum models, namely QSVM (Quantum Support Vector Machine) and VQC (Variational Quantum Classifier), to evaluate their performance relative to traditional machine learning methods for processing speed and prediction accuracy. Findings indicate that QML significantly decreases training duration and improves decision-making, especially in data-intensive industries such as biotechnology, banking, and logistics. The research offers novel insights by framing QML as both a computational asset and a basis for a new category of quantum-driven entrepreneurs, which draw investment and transform business models centered on cutting-edge technology. This study is pertinent to the domains of artificial intelligence, digital transformation, and deep-tech entrepreneurship. It also examines ethical issues and scalability, fostering responsible and inclusive innovation. The societal effect resides in its capacity to democratize access to modern computers for small enterprises and promote scientific entrepreneurship in developing nations. The work is endorsed for presentation because of its multidisciplinary significance, methodological precision, and practical relevance for accelerators, incubators, and policymakers seeking to foster sustainable, innovation-driven development in the quantum age.