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Probing the Berezinskii-Kosterlitz-Thouless vortex unbinding transition in two-dimensional superconductors using local noise magnetometry
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The melting of quasi-long-range superconductivity in two spatial dimensions occurs through the proliferation and unbinding of vortex-antivortex pairs -- a phenomenon known as the Berezinskii-Kosterlitz-Thouless (BKT) transition. Although signatures of this transition have been observed in bulk measurements, these experiments are often complicated, ambiguous, and unable to resolve the rich physics of the vortex unbinding transition. Here we show that local noise magnetometry is a sensitive, noninvasive probe that can provide direct information about the scale-dependent vortex dynamics. In particular, by resolving the distance and temperature dependence of the magnetic noise, it may be possible to experimentally study the renormalization group flow equations of the vortex gas and track the onset of vortex unbinding in situ. Specifically, we predict i) a nonmonotonic dependence of the noise on temperature and ii) the local noise is almost independent of the sample-probe distance at the BKT transition. We also show that noise magnetometry can distinguish Gaussian superconducting order-parameter fluctuations from topological vortex fluctuations and can detect the emergence of unbound vortices. The weak distance dependence at the BKT transition can also be used to distinguish it from quasiparticle background noise. Our predictions may be within experimental reach for a number of unconventional superconductors.
Forward citations
Cited by 2 Pith papers
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Non-Gaussian Noise Magnetometry Using Local Spin Qubits
A single NV spin qubit and two-qubit coincidence or Bell-state echoes can isolate fourth-order magnetic noise cumulants, demonstrated on telegraph-noise and critical Ising models.
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Multi-qubit nanoscale sensing with entanglement as a resource
Super-resolution covariance magnetometry is demonstrated with unresolved NV pairs, including an entanglement-based protocol with sensitivity that scales linearly with readout noise.
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