12 citations · 33 across the 7 of their papers we have counts for
5 papers · 1 filter
Machine-learned tuning to protected states by probing noise resilience
Rodrigo A. Dourado, Nicolás Martínez-Valero, Jacob Benestad +3
Protected states are promising for quantum technologies due to their intrinsic resilience against noise. However, such states often emerge at discrete points or small regions in pa…
Automated in situ optimization and disorder mitigation in a quantum device
Jacob Benestad, Torbjørn Rasmussen, Bertram Brovang +9
We investigate automated in situ optimization of the potential landscape in a quantum point contact device, using a gate array patterned atop the constriction. Optimiz…
Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements
Jacob Benestad, Athanasios Tsintzis, Rubén Seoane Souto +3
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…
Physics-informed tracking of qubit fluctuations
Fabrizio Berritta, Jan A. Krzywda, Jacob Benestad +9
Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to time scales set by the estima…
Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient
Jacob Benestad, Jan A. Krzywda, Evert van Nieuwenburg +1
Slow fluctuations of Overhauser fields are an important source for decoherence in spin qubits hosted in III-V semiconductor quantum dots. Focusing on the effect of the field gradie…