Using IBM Quantum, they ran a 120-qubit simulation in about two minutes versus more than 100 hours for a tuned classical TDVP benchmark.
Q-CTRL said runtime error suppression enabled more than 10,000 two-qubit operations and up to 90 Trotter steps, with results matching classical tensor-network simulations within 1% RMSE.
A 62-qubit experiment captured spin-charge separation, and the companies said the approach could support materials and energy research before being added to IBM's platform as a Qiskit Function.
Can classical computing still catch up, or is this new quantum advantage here to stay?
Beyond lab simulations, which industries will first see a real return from this quantum leap?
Now that materials can be simulated, could this breakthrough accelerate the design of new medicines?
Achieving Practical Quantum Advantage: 3,000-Fold Faster Fermi-Hubbard Simulation with Q-CTRL and IBM
Overview
In early 2026, Q-CTRL and IBM achieved a groundbreaking 3,000-fold speedup by simulating the complex Fermi-Hubbard model on IBM's 120-qubit quantum processor, using over 10,000 two-qubit operations and 90 Trotter steps. This success was made possible by Q-CTRL's innovative runtime error suppression software, which actively reduced errors during computation, allowing the hardware to run at full speed with high accuracy. The simulation not only delivered fast and precise results but also observed the important spin-charge separation phenomenon. This breakthrough opens the door to designing advanced materials like room-temperature superconductors and is driving broader access through software integration into IBM's Quantum Platform, enabling scalable quantum workflows for materials science and drug discovery.