Quantum algorithms to hunt dark matter at upgraded LHC — SkimNews
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- Large Hadron Collider shut down in late June for a major overhaul and will restart in 2030 as the High-Luminosity LHC, producing roughly 10 times more collisions than before
- Sarah Alam Malik, a particle physicist at University College London, is developing quantum algorithms to spot anomalous patterns in collision data after more than a decade searching for dark matter at the LHC
- Physicists have hunted WIMPs since 2010 by smashing protons and looking for missing energy from invisible particles escaping detectors, but no dark matter particles have been found
- The field is shifting from theory-driven WIMP searches to model-agnostic quantum anomaly detection — training algorithms on standard model processes to flag deviations without presupposing what dark matter looks like
- LHC collisions are inherently quantum, but classical detectors collapse particles into classical data and discard most of the underlying quantum information; quantum algorithms could analyze less-processed data to separate event types
- Researchers are also exploring quantum sensors that could extract information from particle collisions without fully collapsing quantum states — an approach Malik calls 'very much in its infancy'
- Modified gravity scenarios remain a competing hypothesis to particle-based dark matter, and even a promising collider detection would still need to behave correctly over billions of years to explain cosmological dark matter
Why it matters: With the LHC's 2030 restart producing roughly 10x more collision data, the bottleneck shifts from raw detection to computational analysis. Malik's quantum anomaly-detection approach is designed to find patterns classical systems miss, addressing two decades of failed WIMP hunts — though the method remains exploratory and modified gravity remains a competing hypothesis.
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