Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements

Abstract

We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunnelling-spectroscopy features of a chain with two additional sensor dots added at its ends provides a reliable metric to navigate parameter space and find points where crossed Andreev reflection and elastic cotunneling between neighbouring sites balance in such a way to yield near-zero-energy modes with very high Majorana quality. We simulate tuning of two- and three-site Kitaev chains, where the loss function is found from calculating the low-energy spectrum of a model Hamiltonian that includes Coulomb interactions and finite Zeeman splitting. In both cases, the algorithm consistently converges towards high-quality sweet spots. Since tunnelling spectroscopy provides one global metric for tuning all on-site potentials simultaneously, this presents a promising way towards tuning longer Kitaev chains, which are required for achieving topological protection of the Majorana modes.

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Cited by in corpus (9)

• Fate of poor man's Majoranas in the long Kitaev chain limit
• Majorana sweet spots in 3-site Kitaev chains
• Subgap states in semiconductor-superconductor devices for quantum technologies: Andreev qubits and minimal Majorana chains
• Braiding Majoranas in a linear quantum dot-superconductor array: Mitigating the errors from Coulomb repulsion and residual tunneling
• Hamiltonian-learning quantum magnets with non-local impurity tomography
• Automated in situ optimization and disorder mitigation in a quantum device
• Machine-learning-enabled characterization of individual ring resonators in integrated photonic lattices
• Properties and prevalence of false poor man's Majoranas in two- and three-site artificial Kitaev chains
• Transfer learning of many-body electronic correlation entropy from local measurements