Majorana edge modes in number-conserving models with long-range interactions
ORAL
Abstract
Topological superconductors are believed to host exotic quasiparticle excitations known as Majo-
rana zero-modes, with much of the evidence based on BCS mean-field theory. The direct application
of mean-field arguments is tenuous in finite, isolated systems relevant in some experiments. Here,
we numerically study fermion number-conserving models with long-range interactions, which un-
der periodic boundary conditions exhibit robust topological and non-topological superconductivity,
tuned by the strength of interaction [1]. We find evidence that, on the topological side, Majorana
edge modes appear in open chains, manifesting as the vanishing of the energy splitting between
odd- and even-parity ground states with increasing system size. Additionally, off-diagonal two-
point correlation functions show nonlocal, parity-dependent edge effects consistent with Majorana
phenomenology. We develop a correlation-based method revealing the spatial structure of Majorana
modes in this fully interacting many-body setting.
rana zero-modes, with much of the evidence based on BCS mean-field theory. The direct application
of mean-field arguments is tenuous in finite, isolated systems relevant in some experiments. Here,
we numerically study fermion number-conserving models with long-range interactions, which un-
der periodic boundary conditions exhibit robust topological and non-topological superconductivity,
tuned by the strength of interaction [1]. We find evidence that, on the topological side, Majorana
edge modes appear in open chains, manifesting as the vanishing of the energy splitting between
odd- and even-parity ground states with increasing system size. Additionally, off-diagonal two-
point correlation functions show nonlocal, parity-dependent edge effects consistent with Majorana
phenomenology. We develop a correlation-based method revealing the spatial structure of Majorana
modes in this fully interacting many-body setting.
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Publication: https://arxiv.org/html/2509.00158v1
Presenters
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Jaden Thomas-Markarian
- Massachusetts Institute of Technology