| Research Group Website | https://piquemalresearch.com |
| Google Scholar | https://scholar.google.com/citations?user=z0cOrb0AAAAJ&hl=fr |
#compchem #machinelearning #quantumcomputing New preprint: "The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery".
Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials.
Our recent #quantumcomputing work "Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer" has been highlighted by Quantum Zeitgeist.
https://quantumzeitgeist.com/researchers-run-quantum-markov-chains-on-quantinuum-machine/
For years, complex quantum algorithms have struggled to deliver dependable results on existing hardware due to inherent noise. Now, a quantum Markov Chain Monte Carlo algorithm has been successfully implemented on Quantinuum’s devices, demonstrating the feasibility of obtaining accurate results despite these limitations. This work represents a practical step towards utilising quantum computers for simulations in fields like drug discovery and materials science.
#quantumcomputing New group preprint: "Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer"
https://arxiv.org/abs/2603.08395
We experimentally use encodings of Markov chains to prepare quantum states & run a quantum Markov Chain Monte Carlo algorithm (qMCMC) on Quantinuum's H2 & Helios quantum computers. We demonstrate that it is possible to obtain accurate results on current Noisy Intermediate Scale Quantum (NISQ) hardware, operating directly on the physical qubits
