aiqbench analysis
This research advances quantum computing by demonstrating efficient learning algorithms for fermionic magic states, a critical resource for achieving quantum advantage in simulation tasks. The study leverages the spectral properties of reduced density matrices to reconstruct complex state structures using only polynomial resources and single-copy measurements. These findings provide essential theoretical bounds on how much non-Gaussian complexity can be efficiently characterized within free-fermion evolution frameworks.
Efficient Learning of Fermionic Magic States under Free-Fermion Evolution
arXiv:2609.39372v1 Announce Type: new Abstract: We establish efficient learning for a family of fermionic magic states under unknown number-conserving free-fermion evolution. Each input block has a definite particle number and is a superposition of Fock states, with occupied mode sets disjoint both within and across blocks. The key idea is to exploit the spectral structure of particle reduced density matrices (RDMs) to separate contributions from individual blocks from those involving several blocks, allowing us to reconstruct the hidden block structure. For a fixed upper bound on the particle number per block, our algorithm uses single-copy measurements and polynomial sample and classical computational complexity to recover a compact description of the state with prescribed fidelity and high probability, without prior knowledge of the block decomposition or the evolution. RDMs up to this upper bound suffice for reconstruction. We further show that this RDM order is necessary in general: two orthogonal states in the family can have identical RDMs at every lower order. These results show that an extensive number of non-Gaussian blocks can be compatible with efficient state learning.
Abstract:We establish efficient learning for a family of fermionic magic states under unknown number-conserving free-fermion evolution. Each input block has a definite particle number and is a superposition of Fock states, with occupied mode sets disjoint both within and across blocks. The key idea is to exploit the spectral structure of particle reduced density matrices (RDMs) to separate contributions from individual blocks from those involving several blocks, allowing us to reconstruct the hidden block structure. For a fixed upper bound on the particle number per block, our algorithm uses single-copy measurements and polynomial sample and classical computational complexity to recover a compact description of the state with prescribed fidelity and high probability, without prior knowledge of the block decomposition or the evolution. RDMs up to this upper bound suffice for reconstruction. We further show that this RDM order is necessary in general: two orthogonal states in the family can have identical RDMs at every lower order. These results show that an extensive number of non-Gaussian blocks can be compatible with efficient state learning.
arXiv:2609.39372 [quant-ph]
(or arXiv:2609.39372v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.39372 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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