{"record":{"author":{"account_ref":null,"orcid":null},"builds_on":[],"content_schema":"pubphys.content.revision/1","content_sha256":"cd9bda0c825b5cc4b97816aa2254e19d26a880f56f428512879d0fed0a97de37","created":"2026-10-03T07:18:09Z","files":[],"origin":{"assisted_by":[],"kind":"seed"},"parents":["5475cf5dc5b1a72283d26dc95dc6e00d7ea4e00096a7c6492e409a1816eeb011"],"salt":"23da4eeeabb29c3a0bd8147a351218dc85a707cb31556c569ba79777203621fb","schema":"pubphys.record/2","site":"pubphys.com","target":null,"type":"revision"},"content":{"answer_type":"value","assisted_by":[],"external_id":"qi.classical-simulability.lossy-gbs-threshold","kind":"well-posed","literature_status":"partially-resolved","n":"1","parents":[],"plain":"Gaussian boson sampling sends squeezed light through a large optical network and records where photons arrive; it was used to claim quantum advantage. Losing photons makes the task easier for classical computers, and the loss level at which it becomes easy is not known exactly.","posed_since":"","precise":"For Gaussian boson sampling with $M$ modes, $K$ single-mode squeezed inputs of squeezing r, a Haar-random interferometer and uniform transmission $\\eta$, find the boundary $\\eta_c(r, K, M)$ separating classically efficient sampling to fixed total-variation error from sampling hard under standard conjectures, as M, K go to $\\infty$ at fixed ratio.","problem_ref":null,"references":"","settled_by":"A classical algorithm efficient below $\\eta_c$ together with a hardness proof above it, for the same scaling family.","status_note":"Oh et al. (arXiv:2306.03709, Nature Physics 2024) simulated reported experiments by exploiting their loss; sufficient conditions for hardness of lossy GBS appeared in 2025 (arXiv:2511.07853).","title":"Photon-loss threshold for classical simulability of Gaussian boson sampling","topic_ref":"2c6beabcac690dc93131e1fe587b8b4a01c9e2df57eeabff1c22a4fbf820561b"},"attested":{"attestation":{"batch":null,"client_id":null,"id_token_sha256":null,"kind":"platform"},"record_hash":"61d4e8c8a13bdf24a7246dc6e14f173020299c6ed15021eddf13bd63846f67ce","schema":"pubphys.attested/1"},"envelope":{"attested_hash":"6f7d9987c9f8de2fbd2307cef85bb91ee1ef99af998622ad60c080feb747845d","platform_signature":{"key_id":"c6afc19b31429869751f06879c75cd64ea92654423d15b44be775bf1310a60da","sig":"xoTxxO0RNkJJUzP0qURd1sEIl9-gK4BGnTNj4l7Vp7zevtNXNcHbssGWbatM6AFx5obC9ugj_iCzNBycwhDJBg"},"schema":"pubphys.envelope/1"},"record_hash":"61d4e8c8a13bdf24a7246dc6e14f173020299c6ed15021eddf13bd63846f67ce","leaf_index":1973}