Director of Strategic Content @ Resonance | Quantum, AI, & Space Market Intelligence | Founder @ Universum Labs | Collective Knowledge, Connected
Pick the wrong orbital, and your excited-state simulation can miss the physics entirely, meaning a cancer drug candidate that looks promising on paper could fail in practice. A new workflow, AEGISS, systematically identifies the right orbitals, keeping quantum models both accurate and reliable. Every week, I track the quantum research that’s intended for real-world performance, resilience, and utility. These are early steps, but they point toward where quantum may prove its worth. ⚇ AEGISS for quantum chemistry: Researchers from Algorithmiq, Cleveland Clinic, and other collaborators present AEGISS, a Python-based workflow for selecting active orbital spaces. By combining orbital entropy analysis with atomic orbital projections it helps map only the most chemically relevant orbitals onto qubits, making high-accuracy excited-state simulations more systematic and scalable. ⚇ QROCODILE hunts dark matter: The University of Zurich leads the first sub-MeV dark matter search using superconducting nanowire single-photon detectors. With thresholds down to 0.11 eV, QROCODILE sets new global limits on light dark matter interactions, exploring regions of parameter space unreachable by prior experiments. ⚇ Quantum vision for enzymes: Purdue University and North Carolina State University researchers developed a multimodal quantum vision transformer that predicts enzyme function with 85.1% top-1 accuracy. By fusing quantum-derived electronic descriptors with sequence, graph, and image data, the model outperforms prior QML architectures in one of biology’s hardest classification problems. If you want these kinds of insights in your inbox every morning, subscribe to the Daily Qubit and never miss a qubit -- link in the comments. #quantumcomputing #quantumsensing #quantumchemistry
AI & EdTech Innovator | Neuroscience-Based Learning Tools | Prototyping with Hugging Face, GitHub & Automation
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