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CQT and Qubit Pharmaceuticals Partner to Advance Quantum Drug Discovery

Quantum Computing Report
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⚡ Quantum Brief
A two-year collaboration between Singapore’s Centre for Quantum Technologies and Qubit Pharmaceuticals will develop quantum algorithms to accelerate drug discovery by tackling computational bottlenecks in molecular sampling and property prediction. The partnership achieved the first experimental implementation of a quantum Markov Chain Monte Carlo (qMCMC) algorithm on physical quantum hardware, demonstrating potential quadratic speedups over classical methods for molecular simulations. Using Quantinuum’s H2 and Helios trapped-ion systems via Singapore’s National Quantum Computing Hub, the team validated qMCMC’s feasibility on NISQ devices, with results published on arXiv (arXiv:2603.08395). Beyond qMCMC, researchers are testing variational quantum eigensolvers (VQE) and quantum phase estimation (QPE) to enhance chemical modeling fidelity, aiming to integrate quantum tools into pharmaceutical workflows. Led by Sergi Ramos-Calderer and Jean-Philip Piquemal, the project focuses on generating real molecular data to improve early-stage drug development decisions.
CQT and Qubit Pharmaceuticals Partner to Advance Quantum Drug Discovery

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CQT and Qubit Pharmaceuticals Partner to Advance Quantum Drug Discovery The Centre for Quantum Technologies (CQT) in Singapore and Qubit Pharmaceuticals have launched a two-year strategic research collaboration to develop and implement quantum algorithms for molecular discovery. The partnership aims to address critical computational bottlenecks in drug discovery—such as the accurate prediction of drug properties and efficient molecular sampling—by combining Qubit Pharmaceuticals’ expertise in quantum chemistry with CQT’s capabilities in circuit design and hardware implementation. A primary technical milestone of the collaboration is the first-ever experimental realization of a quantum Markov Chain Monte Carlo (qMCMC) algorithm on physical quantum hardware. While classical drug discovery often relies on Markov chains to sample probability distributions for molecular simulations, quantum versions of these algorithms offer potential quadratic speedups.

The team successfully deployed the qMCMC algorithm using Quantinuum’s H2 and Helios trapped-ion systems via Singapore’s National Quantum Computing Hub. The results, which demonstrate the feasibility of running accurate sampling tasks on Noisy Intermediate-Scale Quantum (NISQ) devices, have been published to the physics preprint server arXiv (arXiv:2603.08395). In addition to sampling, the researchers are designing and testing other advanced methods, including variational quantum eigensolvers (VQE) and quantum phase estimation (QPE). The collaboration, led by Sergi Ramos-Calderer (CQT) and Jean-Philip Piquemal (Qubit Pharmaceuticals), focuses on moving beyond abstract benchmarks to produce real molecular simulation data. By modeling chemistry with higher fidelity on gate-based quantum machines, the partners intend to integrate quantum-enhanced capabilities directly into pharmaceutical research workflows to improve early-stage decision-making in the drug development pipeline. You can find the official announcement from the Centre for Quantum Technologies here and the technical study regarding the qMCMC realization on arXiv here. May 2, 2026 Mohamed Abdel-Kareem2026-05-02T14:39:52-07:00 Leave A Comment Cancel replyComment Type in the text displayed above Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.

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drug-discovery
quantum-finance
quantum-machine-learning
quantum-chemistry
quantum-computing
quantum-algorithms
quantum-hardware
quantinuum
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Source: Quantum Computing Report