The matter blotter is back :) This week we revisit the springs and sticks model—one of the projects I have enjoyed the most working on during my PhD! A universal regression model that learns using thermodynamics, and let’s us compute the minimum energy it requires to learn a function

Excited to share our new article: “Quantum algorithm for vibronic dynamics: case study on singlet fission solar cell design”, now published in Quantum Science and Technology! 📜 Paper: doi.org/10.1088/2058... 📝 Blog post overview: pennylane.ai/blog/2025/02... [1/8]

🎉Excited to share our new perspective: “Connecting the concepts of quantum state tomography and molecular representations for machine learning” — now on ChemRxiv! Link: doi.org/10.26434/che... [1/5]

With all the recent interest in physical machine learning, @realmantilla.bsky.social and I wondered what would be a mechanical machine learning system? We devised a simple springs and sticks model that leads to very interesting thermodynamics learning limits. #chemsky #physicssky #machinelearning

Excited to share our most recent work: “Learning with springs and sticks” — now on arXiv! Paper link: arxiv.org/abs/2508.19015 Project page (with animations & code): bestquark.github.io/springs-and-... Authors: @realmantilla.bsky.social, @aspuru.bsky.social [1/4]

⚛️ Curious about getting into quantum computing for quantum chemistry? Check out our new primer published in ACS InFocus. 📙 [1/4]

👾Excited to share our most recent work: “Quantum circuits as a game: A reinforcement-learning compiler for reconfigurable neutral-atom arrays” — now live on arXiv! We explore how reconfigurable neutral-atom arrays can be controlled more efficiently using reinforcement learning. [1/5]

⚛️🖥️Excited to share our work on simulating Hamiltonian evolution on quantum computers. Led by our group member Mohsen Bagherimehrab, we introduce Corrected Product Formulas (CPFs), which significantly improve the performance of standard product formulas. [1/4]