Search arXivSearch

arXiv · 2210.05275

Topological superconductivity from doping a triplet quantum spin liquid in a flat band system

Abstract

We explore superconductivity in strongly interacting electrons on a decorated honeycomb lattice (DHL). An easy-plane ferromagnetic interaction arises from spin-orbit coupling in the Mott insulating phase, which favors a triplet resonance valence bond spin liquid state. Hole doping leads to partial occupation of a flat band and to triplet superconductivity. The order parameter is highly sensitive to the doping level and the interaction parameters, with $p+ip$, $f$ and $p+f$ superconductivity found, as the flat band leads to instabilities in multiple channels. Typically, first order transitions separate different superconducting phases, but a second order transition separates two time reversal symmetry breaking $p+ip$ phases with different Chern numbers ($ν=0$ and 1). The Majorana edge modes in the topological ($ν=1$) superconductor are almost localized due to the strong electronic correlations in a system with a flat band at the Fermi level. This suggests that these modes could be useful for topological quantum computing. The `hybrid' $p+f$ state does not require two phase transitions as temperature is lowered. This is because the symmetry of the model is lowered in the $p$-wave phase, allowing arbitrary admixtures of $f$-wave basis functions as overtones. We show that the multiple sites per unit cell of the DHL, and hence multiple bands near the Fermi energy, lead to very different nodal structures in real and reciprocal space. We emphasize that this should be a generic feature of multi-site/multi-band superconductors.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Manuel Fernández López, Ben J. Powell, Jaime Merino. 2022-10-11. Topological superconductivity from doping a triplet quantum spin liquid in a flat band system. https://doi.org/10.1103/physrevb.106.235129

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Collective excitation-mediated transport in nanoscale Josephson junctions that exhibit quantum confinement

Quantum confinement can strongly modify transport through Josephson junctions. Here, we study local tunneling transport through nanoscale Josephson junction stacks in the Coulomb blockade regime, where the metallic layers exhibit strong vertical quantum confinement. We find that quasiparticle transport is strongly enhanced by a collective excitation mode intrinsic to the junction and localized in the isolated metallic overlayer. We quantify both the collective-mode energy and the Coulomb gap and show that both exhibit strong layer-dependent modulation, consistent with the modulation of the underlying quantum well states. We further investigate how the collective excitation responds to various perturbations, including mechanical motion and an applied magnetic field. Our results suggest that this collective mode is sensitive to quasiparticles near the Fermi level and may therefore provide an indirect probe of the superconducting state.

cond-mat.supr-con

Universal Dzyaloshinski-Moriya interaction dictates pairing in unconventional superconductor families

The collinear-antiferromagnetic spin-fluctuation paradigm has long guided unconventional superconductivity research, yet fails to reconcile the noncollinear spin phenomena observed across cuprates, iron-based superconductors, and nickelates. Using extensive first-principles calculations and unbiased large-scale DMRG simulations, we show that Dzyaloshinski-Moriya interaction (DMI)-arising from local inversion-symmetry breaking-is a common ingredient across these families. This DMI unifies hallmark observations in parent compounds-incommensurate orders, spin-wave gaps, and noncollinear textures. Under hole doping, strong DMI drives spin vortices to merge with pi-shifted hole stripes, forming hybrid vortex-hole stripe phases. These phases stabilize charge order while supporting, not suppressing, superconductivity. By contrast, under electron doping, these vortices pin holes and suppress long-range superconductivity. Our results establish DMI as a unifying link between noncollinear magnetism and superconductivity, identifying hole-strip-vortex coupling as a microscopic pairing engine. Given that DMI is common across major superconductor families, these findings challenge the prevailing pairing mechanism and offer an experimentally testable roadmap for materials optimization.

cond-mat.supr-con

Digital-analog concept for superconducting perceptron-like neural networks

A promising route to superconducting artificial neural networks is a hybrid digital-Analog architecture that combines digital single-flux-quantum (SFQ) communication with compact Analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-Analog-to-digital (DAD) converter, in which the role of the Analog cell was performed by a $Σ$-neuron with a nonlinear transfer function -- the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an Analog-to-digital converter (ADC), and re-encodes the Analog $Σ$-neuron waveforms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $Σ$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterised a redesigned $Σ$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $Σ$-neuron transfer characteristic, which reached classification accuracies of $97.0\%$ and $91.9\%$, respectively, on the MNIST handwritten digit dataset.

cond-mat.supr-con