Classical computers have come a long way over the last few decades, but they’ll always be deficient in a major way — the world they represent is ultimately quantum, which is hard to express and manipulate in classical ways. But this is where quantum computing could be a crucial add-on to conventional computing.
NVIDIA CEO Jensen Huang recently shared his insights into the future of computing. He articulated a vision of quantum computing not as a replacement for classical computing, but as a powerful extension. His words paint a picture of how quantum processing units (QPUs) can unlock new capabilities for existing computer architectures, opening doors to breakthroughs in fields like biology, chemistry, and materials science.
Huang stated: “The idea of a quantum computer is not to build a computer that replaces [classical] computing, but it’s a QPU that is added to a GPU, to a CPU, to extend classical computing to do things that otherwise [it] cannot.”
He went on to elaborate, suggesting several promising areas of application: “There are some domains, some useful domains that we can imagine. For example, in the biology and chemistry applications and materials applications, where we could use a quantum computer to make a classical computer way better, to solve problems [it] otherwise cannot. For example, to create the ground truth for biology, to create the ground truth for atomic physics. And that enables us to use AI as we understand it today, and improving itself at a million times every two-three years to now amplify the capability of AI to be able to use AI to solve the drug discovery, the material science, the biology applications.”
Huang’s perspective is particularly relevant given the recent surge in both quantum computing research and AI development. Quantum computers, leveraging the principles of quantum mechanics, have the potential to tackle problems currently intractable for even the most powerful supercomputers. By generating highly accurate simulations of quantum systems, QPUs can provide the “ground truth” data necessary to train and refine AI models. This synergy between quantum and classical computing could revolutionize scientific discovery.
AI algorithms, supercharged by quantum-derived data, could potentially design new drugs with unprecedented speed and precision. Materials scientists could use AI to predict and synthesize materials with new and exotic properties, tailored for specific applications. Huang’s vision suggests that quantum computing won’t necessarily replace our familiar computers, but will instead empower them to solve problems previously beyond our reach. This hybrid approach, combining the strengths of both classical and quantum computing, could unlock a new era of scientific and technological advancement. As both technologies continue to evolve, their convergence could reshape our world in profound ways.