For those who missed it, the Quantumzeitgeist post about our work quantumzeitgeist.com/quantum-walk...

Quantum Walks Speed Up Markov Chain Simulations

Szegedy’s quantum walk, offering a potentially quadratic acceleration to Metropolis-Hastings simulations, now benefits from a novel implementation.

quantumzeitgeist.com

Jean-Philip Piquemal@jppiquem.bsky.social · 2w ago

Now in final form and #openaccess. "Quantum circuits for the Metropolis–Hastings algorithm" #quantumcomputing iopscience.iop.org/article/10.1...

Now in final form and #openaccess. "Quantum circuits for the Metropolis–Hastings algorithm" #quantumcomputing iopscience.iop.org/article/10.1...

Jean-Philip Piquemal@jppiquem.bsky.social · 4w ago

Finally out in J. Phys. A: Math. Theor.: "Quantum Circuits for the Metropolis-Hastings Algorithm" With these quantum walks, the end-to-end quadratic speedup holds for MH Markov Chain Monte-Carlo simulations. Stellar work by B. Claudon. #quantumcomputing #compchem iopscience.iop.org/article/10.1...

Finally out in J. Phys. A: Math. Theor.: "Quantum Circuits for the Metropolis-Hastings Algorithm" With these quantum walks, the end-to-end quadratic speedup holds for MH Markov Chain Monte-Carlo simulations. Stellar work by B. Claudon. #quantumcomputing #compchem iopscience.iop.org/article/10.1...

Quantum circuits for the Metropolis-Hastings algorithm

Quantum circuits for the Metropolis-Hastings algorithm, Claudon, Baptiste, Rodenas Ruiz, Pablo, Piquemal, Jean-Philip, Monmarché, Pierre

iopscience.iop.org

Final workshop of our Extreme-scale Mathematically-based Computational Chemistry (EMC2) @erc.europa.eu Synergy project in Roscoff. #ERCSyG #compchem #compchemsky #appliedmathematics #HPC #supercomputing #machinelearning #quantumcomputing Stay tuned for the next steps!

I am deeply honored & thrilled to have been elected Vice President (& future 2028 President) of the 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐨𝐜𝐢𝐞𝐭𝐲 𝐨𝐟 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐁𝐢𝐨𝐥𝐨𝐠𝐲 𝐚𝐧𝐝 𝐏𝐡𝐚𝐫𝐦𝐚𝐜𝐨𝐥𝐨𝐠𝐲 #ISQPB I look forward to serving this vibrant community of computational scientists #compchem #compbio #machinelearning #biophysics isqbp.org

The International Society of Quantum Biology and Pharmacology – Organization of computational chemists and biophysicists.

The International Society of Quantum Biology and Pharmacology The ISQBP is a society founded in 1970 to provide a forum for chemists, pharmacologists, and biologists to discuss and extend the impact...

isqbp.org

On my way to the 2026 International Society of Quantum Biology and Pharmacology (ISQBP) President's meeting in Cluj, Romania to present our latest results on the #FeNNix-Bio1 foundation #machinelearning model. #compchem #compbio isqbp2026.com

2026 ISQBP President’s Meeting

breaking barriers with the computational microscope

isqbp2026.com

VTX 2026 beta is out, previewed last week at the Tinker Developers meeting in @sorbonne-universite.fr features Real-time analytical SES, new GUI and Python bindings check it out at github.com/VTX-Molecula... @sguionni.bsky.social @maximemaria.bsky.social @jppiquem.bsky.social

Releases · VTX-Molecular-Visualization/VTX

High-performance molecular visualization software. Contribute to VTX-Molecular-Visualization/VTX development by creating an account on GitHub.

github.com

#compchemsky #machinelearning #neuralnetwork #moleculardynamics

Jean-Philip Piquemal@jppiquem.bsky.social · 2mo ago

#compchem #compbio New group paper in JCTC: "Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Nonconservative Forces". No fine-tuning, easier to implement, high accuracy & maximum efficiency. @qubit-pharma.bsky.social pubs.acs.org/doi/10.1021/...

#compchem DMTS-NC delivers a 15% 𝐭𝐨 30% 𝐬𝐩𝐞𝐞𝐝𝐮𝐩 𝐨𝐯𝐞𝐫 𝐩𝐫𝐞𝐯𝐢𝐨𝐮𝐬 𝐜𝐨𝐧𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐯𝐞 𝐌𝐓𝐒 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐚𝐧𝐝 𝐮𝐩 𝐭𝐨 𝐚 5.6𝐱 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐯𝐞𝐫 𝐬𝐢𝐧𝐠𝐥𝐞-𝐭𝐢𝐦𝐞-𝐬𝐭𝐞𝐩 𝐦𝐞𝐭𝐡𝐨𝐝𝐬.

#compchem #compbio New group paper in JCTC: "Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Nonconservative Forces". No fine-tuning, easier to implement, high accuracy & maximum efficiency. @qubit-pharma.bsky.social pubs.acs.org/doi/10.1021/...

That's a wrap! The 2026 Tinker Developer Meeting returned to Paris, hosted at @sorbonne-universite.fr . Following the tradition of our previous scientific gatherings, this year's meeting highlighted exciting advances in #moleculardynamics & #machinelearning. wiki.lct.jussieu.fr/workshop/ind...

#compchem #compbio Yesterday was the first day of the 8th Tinker developer meeting. First talk by Sameer Varma (Univ. of South Florida) : "Advances and challenges in force fields for describing charged species in solution " wiki.lct.jussieu.fr/workshop/ind...

Congrats to Baptiste Claudon who succesfully defended his PhD Thesis: "Quantum Algorithms and Circuits for Nonreversible Markov Chain Mixing". #quantumcomputing #compchem

Just out: Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations

Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations

Communications Chemistry, Published online: 08 May 2026; doi:10.1038/s42004-026-02022-6Relative Binding Free Energy (RBFE) calculations are essential for drug discovery but are often hindered by high computational costs and limited reliability for complex transformations. Here, the authors introduce the Dual-LAO framework integrating Lambda-ABF-OPES, dual topology, and dual-DBC restraints, that accelerates RBFE calculations by 15 to 30 times, maintains high accuracy and tackles previously prohibitive molecular changes.

dlvr.it

New paper with the @qubit-pharma.bsky.social team led by Narjes Ansari, just published @commschem.nature.com : "Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations". #compchem #compchemsky #compbio www.nature.com/articles/s42...

Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations - Communications Chemistry

Relative Binding Free Energy (RBFE) calculations are essential for drug discovery but are often hindered by high computational costs and limited reliability for complex transformations. Here, the auth...

nature.com

A fantastic partnership between @qubit-pharma.bsky.social and the Centre for Quantum Technologies in Singapore @quantumlah.bsky.social ! Excited to be part of this journey and looking forward to the breakthroughs ahead.

Qubit Pharmaceuticals@qubit-pharma.bsky.social · 3mo ago

Qubit Pharmaceuticals @qubit-pharma.bsky.social to collaborate with the Centre for Quantum Technologies @quantumlah.bsky.social in Singapore to Advance Quantum Algorithms for Drug Discovery. #quantumcomputing #compchem #compchemsky #compbio thequantuminsider.com/2026/04/30/q...

New group preprint: "Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces". Great work by N. Gouraud. arxiv.org/abs/2602.14975

Will Quantum Computing actually transform #drug discovery? In the age of AI, why are we still betting on Quantum & GPU-accelerated HPC? We’ve just released our whitepaper detailing how the synergy of #QuantumComputing #MachineLearning & #HPC enables quantum-accurate simulations. #compbio #compchem

Jean-Philip Piquemal@jppiquem.bsky.social · 5mo ago

#compchem #machinelearning #quantumcomputing #compbio New preprint: "The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery". @qubit-pharma.bsky.social arxiv.org/abs/2603.17790

#compchem #machinelearning #quantumcomputing #compbio New preprint: "The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery". @qubit-pharma.bsky.social arxiv.org/abs/2603.17790

New #quantumcomputing group preprint in collaboration with @qubit-pharma.bsky.social and CERFACS: "Logarithmic-depth quantum state preparation of polynomials" arxiv.org/abs/2603.16527

Logarithmic-depth quantum state preparation of polynomials

Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This wor...

arxiv.org

#quantumcomputing New preprint: "Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer". We experimentally encoded & accurately ran a quantum MCMC on the H2 & Helios quantum computers. @qubit-pharma.bsky.social @quantumlah.bsky.social arxiv.org/abs/2603.08395

#compchem #compchemsky Our paper in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation" made it to one of the covers! pubs.acs.org/doi/full/10....

Jean-Philip Piquemal@jppiquem.bsky.social · 7mo ago

#compchem #machinelearning 1st of the year in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation". pubs.acs.org/doi/full/10.... (see also the updated preprint: arxiv.org/abs/2510.06562)

🚀 Game-changing speed for drug discovery simulations without trading accuracy for Relative Binding Free Energy (RBFE) calculations. Dual-LAO delivers 15–30× faster simulations while maintaining industry-leading accuracy (~0.5–0.6 kcal/mol). #compchem t.co/dDLVqXKvZm

🚀First paper published! We introduce DMTS, a multi-time-step method for ML force fields ✔️×4 speed-up ✔️Accuracy preserved ✔️Generalizable to any ML potential 📄Link: pubs.acs.org/doi/full/10.... The preprint: arxiv.org/abs/2510.06562 @jppiquem.bsky.social #MolecularDynamics #MachineLearning

🤩 New year, new publication using the FeNNix-Bio1 foundation model ! 🚀« Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation» published in the Journal of Physical Chemistry Letters #compchemsky #biosky #machinelearning

Jean-Philip Piquemal@jppiquem.bsky.social · 7mo ago

#compchem #machinelearning 1st of the year in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation". pubs.acs.org/doi/full/10.... (see also the updated preprint: arxiv.org/abs/2510.06562)

#compchemsky #biosky

Jean-Philip Piquemal@jppiquem.bsky.social · 7mo ago

#compchem #machinelearning 1st of the year in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation". pubs.acs.org/doi/full/10.... (see also the updated preprint: arxiv.org/abs/2510.06562)

#compchem #machinelearning 1st of the year in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation". pubs.acs.org/doi/full/10.... (see also the updated preprint: arxiv.org/abs/2510.06562)

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models Using Multiple Time Steps and Distillation

We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network, where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. The 3.5 Å cutoff distilled model is sufficient to capture the fast-varying forces, i.e., mainly bonded interactions, from the accurate potential, allowing its use in a reversible reference system propagator algorithm (RESPA)-like formalism. The approach conserves accuracy, preserving both static and dynamic properties, while enabling us to evaluate the costly model only every 3 to 6 fs depending on the system. Consequently, large simulation speedups over standard 1 fs integration are observed: nearly 4-fold in homogeneous systems and 3-fold in large solvated proteins through leveraging active learning for enhanced stability. Such a strategy is applicable to any neural network potential and reduces the performance gap with classical force fields.

pubs.acs.org