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Gauge freedom and efficient algorithms for Lindbladian learning
Steven T Flammia, Savar D Sinha, Yu Tong
We study the problem of learning a local Lindbladian in the presence of state-preparation-and-measurement (SPAM) noise. Although recent work has developed scalable learning algorit…
Fermionic Averaged Circuit Eigenvalue Sampling
Adrian Chapman, Steven T. Flammia
Fermionic averaged circuit eigenvalue sampling (FACES) is a protocol to simultaneously learn the averaged error rates of many fermionic linear optical (FLO) gates simultaneously an…
Ansatz-free Hamiltonian learning with Heisenberg-limited scaling
Hong-Ye Hu, Muzhou Ma, Weiyuan Gong +4
Learning the unknown interactions that govern a quantum system is crucial for quantum information processing, device benchmarking, and quantum sensing. The problem, known as Hamilt…
Chasing shadows with Gottesman-Kitaev-Preskill codes
Jonathan Conrad, Jens Eisert, Steven T. Flammia
We consider the task of performing shadow tomography of a logical subsystem defined via the Gottesman-Kitaev-Preskill (GKP) error correcting code. Our protocol does not require the…
Learning -body Hamiltonians via compressed sensing
Muzhou Ma, Steven T. Flammia, John Preskill +1
We study the problem of learning a -body Hamiltonian with unknown Pauli terms that are not necessarily geometrically local. We propose a protocol that learns the Hamiltonian…
Fiber Bundle Fault Tolerance of GKP Codes
Ansgar G. Burchards, Steven T. Flammia, Jonathan Conrad
We investigate multi-mode GKP (Gottesman--Kitaev--Preskill) quantum error-correcting codes from a geometric perspective. First, we construct their moduli space as a quotient of gro…