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.
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Tech VC | MD @ KIP | AI-Quantum-Blockchain-Energy
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