Qclairvoyance Quantum Labs Achieves Chemically Accurate Molecular Simulations on IQM Quantum Hardware
Summarize this article with:
Insider Brief An international team led by Qclairvoyance Quantum Labs demonstrated chemically accurate quantum simulations of molecular systems on superconducting hardware using a hybrid quantum–classical workflow. Using IQM’s 24-qubit Sirius processor, the researchers generated a full 2D potential energy surface for water and simulated the drug Amantadine, extending quantum simulations beyond small benchmark molecules. The study employs techniques such as Sample-based Quantum Diagonalization and Density Matrix Embedding Theory to enable scalable, near-term applications in drug discovery and materials science. PRESS RELEASE — An international research team led by Qclairvoyance Quantum Labs has demonstrated chemically accurate quantum simulations of complex molecular systems using superconducting quantum hardware, marking a significant step forward for practical quantum chemistry. Working on IQM’s 24-qubit Sirius processor, the team successfully simulated molecular systems using up to 16 qubits and introduced a hybrid quantum–high performance computing (HPC) workflow that enables scalable and accurate results on near-term quantum devices. Among the key achievements, the researchers experimentally generated a full two-dimensional potential energy surface (2D-PES) for a water molecule directly on quantum hardware—one of the first demonstrations of its kind.
The team also simulated the FDA-approved drug Amantadine, showcasing the ability to handle pharmacologically relevant molecules beyond small benchmark systems. “This work demonstrates that hybrid quantum-classical approaches can deliver chemically accurate results on today’s quantum hardware,” said Sai Shankar P, CEO at Qclairvoyance Quantum Labs. “It provides a clear roadmap for scaling quantum simulations toward real-world applications in drug discovery and materials science.” To overcome the limitations of current quantum processors, the team used a hybrid approach known as Sample-based Quantum Diagonalization (SQD). In this method, the quantum processor is used for sampling key electronic configurations, while computationally intensive calculations are offloaded to classical supercomputers. For larger molecules like Amantadine, the researchers combined SQD with Density Matrix Embedding Theory (DMET), enabling them to break down complex systems into smaller, quantum-computable fragments while preserving chemical accuracy. The study also explores different quantum circuit strategies, providing insights into balancing computational cost and accuracy—an important consideration for near-term quantum applications. The research was conducted in collaboration with scientists from the National University of Singapore, Ahmedabad University, the University of Arizona, and BITS Pilani, and is detailed in a recent arXiv preprint. The full research findings are available in the arXiv preprint: arXiv:2604.01983. The results highlight the growing potential of hybrid quantum-classical workflows as a practical pathway toward solving classically intractable problems in chemistry, with future applications ranging from catalyst design to pharmaceutical development.
Matt Swayne LinkedIn With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. matt@thequantuminsider.com Share this article: