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Jens Eisert

@jenseisert.bsky.social did:plc:vf7a7kmygi3yy7l7khmoba2t

Scientist, professor of quantum physics at Freie Universität Berlin and affiliated with Helmholtz Center Berlin and the Fraunhofer Heinrich Hertz Institute. ERC Fellow.

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Jens Eisert (@jenseisert.bsky.social) es un usuario de Bluesky que se unió el 10 octubre 2023. Tiene 1.810 seguidores, sigue a 1.141 cuentas y ha publicado 304 posts.

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  • Antonio Anna Mele@antonioannamele.bsky.social

    We then realized that the same idea could be pushed further to other interesting applications, giving sample-optimal tolerant single-copy testing, optimal testing with limited quantum memory, and improved learning of states beyond stabilizers (e.g. Clifford+T states). ✨

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  • Antonio Anna Mele@antonioannamele.bsky.social

    Then GPT-6 came out. We asked essentially the same thing, and it gave us a surprising answer: "What you want to prove is false -- in fact, the opposite is true: there is an optimal algorithm using adaptive single-copy measurements." 💀😅 We checked the argument carefully and.. it worked!

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  • Antonio Anna Mele@antonioannamele.bsky.social

    So our first thought was: okay, then we just need to generalize the lower bound to adaptive strategies. We also tried prompting GPT-5.6 with something like "please prove an Omega(n^2) adaptive lower bound for this problem" -- but it couldn't do it.

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  • Antonio Anna Mele@antonioannamele.bsky.social

    We believed that this could not be done optimally, because a previous paper proved an Omega(n^2) lower bound. Earlier this year, however, we noticed that this proof actually applied only to non-adaptive single-copy measurements.

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  • Antonio Anna Mele@antonioannamele.bsky.social

    Story: It was known that one can optimally learn an n-qubit stabilizer state using O(n) two-copy Bell measurements. But what about single-copy measurements, which are much more practical?

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