Quantum Machine Learning in Laboratory Medicine: A Leap into the Future?

Recent discussion around quantum computing often oscillates between excitement and skepticism. In an interview at the 2025 World Governments Summit in Dubai, Sundar Pichai, CEO of Alphabet and Google, remarked that practical quantum computers are likely still 5 to 10 years away, drawing a parallel between today’s quantum landscape and the early stages of artificial intelligence (AI) in the 2010s. At that time, initiatives such as Google Brain had begun to demonstrate potential, yet their broader impact remained uncertain and far from transformative (1). While a decade-long horizon may seem daunting, recent AI history offers an important perspective: beginning around 2010, foundational breakthroughs such as ImageNet (2), AlexNet (3), and later generative adversarial networks (4), emerged in rapid succession. Skepticism toward such projections is understandable, given past experience with long-anticipated technologies, such as fusion energy. However, the trajectory of AI illustrates how a field can advance rapidly once key conceptual and technical components converge.