Learning Local Fermionic Lindbladians under Parity Superselection
quant-ph, cs.IT, math.IT, math.ST, stat.TH
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- Near-Optimal Learning of Local Lindbladians
- Efficiently learning fermionic unitaries with few non-Gaussian gates
- Demonstrating and Benchmarking Classical Shadows for Lindblad Tomography
- Learning Coulomb Potentials and Beyond with Free Fermions in Continuous Space
- Fermionic Averaged Circuit Eigenvalue Sampling
- Optimal tomography of bosonic and fermionic Gaussian states
- Learning Arbitrary Lindbladians from Time Evolution
- Learning fermionic linear optics with Heisenberg scaling and physical operations
- Learning Gaussian Operations and the Matchgate Hierarchy
- Certifying fermionic Gaussian states (and a little more) with optimal precision dependence
- Gauge freedom and efficient algorithms for Lindbladian learning
- Learning and certification of local time-dependent quantum dynamics and noise
- Lindbladian Learning with Neural Differential Equations
- Ansatz-Free Learning of Lindbladian Dynamics In Situ
- Mildly-Interacting Fermionic Unitaries are Efficiently Learnable
- Learning the structure of open quantum systems
- Classical shadows of fermions with particle number symmetry
- Robust Structure Learning of k-local Lindbladians
- Learning and simulating bosonic systems via finite-energy locality
- Heisenberg-limited Hamiltonian learning continuous variable systems via engineered dissipation
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