2016
DOI: 10.1063/1.4962337
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Local gap threshold for frustration-free spin systems

Abstract: We improve Knabe's spectral gap bound for frustration-free translation-invariant local Hamiltonians in 1D. The bound is based on a relationship between global and local gaps. The global gap is the spectral gap of a size-m chain with periodic boundary conditions, while the local gap is that of a subchain of size n < m with open boundary conditions. Knabe proved that if the local gap is larger than the threshold value 1/(n − 1) for some n > 2, then the global gap is lower bounded by a positive constant in the th… Show more

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Cited by 47 publications
(93 citation statements)
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“…Comparison to prior work: As already mentioned, our tools significantly differ from those employed in [Kna88,GM16,LM18,Lem19a]. Similar to us, the work [KL18] employs the detectability lemma and its converse to obtain the local gap threshold.…”
Section: Proof Outlinementioning
confidence: 99%
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“…Comparison to prior work: As already mentioned, our tools significantly differ from those employed in [Kna88,GM16,LM18,Lem19a]. Similar to us, the work [KL18] employs the detectability lemma and its converse to obtain the local gap threshold.…”
Section: Proof Outlinementioning
confidence: 99%
“…The dependence on min q t 2 q cannot be improved; although the dependence on D might not be optimal. To show this, we provide the following example adapted from [GM16]. We consider the heisenberg ferromagnet, which is a one dimensional chain of qubits with frustrationfree local hamiltonian defined by nearest-neighbour interaction 1 2 (|01 − |10 ) ( 01| − 10|).…”
Section: Local Verses Global Spectral Gap On D Dimensional Latticesmentioning
confidence: 99%
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“…Our proof is based on verifying a tailor-made finite-size criterion in the spirit of Knabe [10] by an explicit computer calculation. Recently, finitesize criteria have been further developed in related contexts [6,11,12] and here we show that this technique does say something (but not everything one wants) about the hexagonal AKLT model that had originally motivated Knabe. The present work is the first instance where such a finite-size criterion can be verified by an exact computation of the eigenvalues of a subsystem with genuine 2D features.…”
Section: Motivationmentioning
confidence: 84%
“…To prove the criterion, we follow the combinatorial approach pioneered by Knabe [26], strengthened by using interaction weights as in Refs. [33,34]. In step 2, we combine the rigorous analytical insight from step 1 by numerically verifying the finitesize criterion via a high-precision density-matrix renormalization group (DMRG) calculation (see also Ref.…”
mentioning
confidence: 99%