Quantum Simulation Achieves Speed Boost With UTokyo-IBM Algorithm

University of Tokyo associate professor Nobuyuki Yoshioka, alongside IBM Principal Research Scientist Antonio Mezzacapo and their collaborators, have developed a novel algorithm for “Krylov quantum diagonalization” (KQD) to extend the capabilities of quantum computers in simulating condensed matter systems. This algorithm, detailed in a June 2025 publication in Nature Communications, focuses on efficiently finding the ground state—the lowest-energy state—of complex systems. By maximizing the capabilities of existing IBM quantum computers, KQD represents a key step toward achieving quantum advantage, enabling computations beyond the reach of classical supercomputers in fields like chemistry and high-energy physics.

Quantum Algorithm Development for Computational Advantage

Quantum algorithm development is crucial for realizing the potential of quantum computers, as algorithms drive the utility of the hardware. Researchers at the University of Tokyo and IBM have focused on…