IBM-UChicago Unveils LASSQD for Accurate Molecular Modeling on Noisy Quantum Computers
Updated
Updated · Quantum Zeitgeist · Jul 14
IBM-UChicago Unveils LASSQD for Accurate Molecular Modeling on Noisy Quantum Computers
3 articles · Updated · Quantum Zeitgeist · Jul 14
Summary
LASSQD, published in PNAS, let IBM-UChicago researchers extract accurate molecular insights on current noisy quantum computers without full error correction.
The framework splits complex molecules into smaller fragments, then uses quantum sampling in a hybrid classical-quantum workflow to keep accuracy while working in a smaller dimension space.
Tests on iron-containing systems—including catalytic-center models in metal-organic frameworks and iron porphyrin spin gaps—showed the fragment-based approach avoided a full quantum treatment of the entire molecule.
The team said the method could aid catalysis and energy research now, while further refinements aim to cut computational demands and move toward fully quantum chemical simulations.
With IBM predicting quantum advantage this year, is this new hybrid method the key to finally unlocking it?
Is the future a permanent hybrid of quantum and classical, or are these algorithms just a bridge to perfect quantum computers?
LASSQD Debuts: A Hybrid Quantum-Classical Leap in Computational Chemistry with DOE Backing and AI Integration
Overview
LASSQD is a groundbreaking hybrid method that bridges quantum and classical chemistry, offering a powerful tool for simulating complex molecular systems with high efficiency and accuracy. Developed through a major collaboration and published in December 2025, LASSQD combines quantum mechanics for critical regions with classical approaches for less demanding parts, enabling more scalable and precise simulations. Its debut marks a pivotal moment in computational chemistry, promising to accelerate discoveries in fields like materials science, drug development, and energy research. Supported by leading institutions and the U.S. Department of Energy, LASSQD is set to push the boundaries of what is computationally possible.