Experimental demonstration of a robust and scalable flux qubit

By R. Harris, J. Johansson, A. Berkley, Mark W. Johnson, T. Lanting, Siyuan Han, P. Bunyk, E. Ladizinsky, T. Oh, I. Perminov and 9 more. 2009

176 citations recorded by the source corpus.

Received 23 September 2009; revised manuscript received 11 February 2010; published 7 April 2010 A rf–superconducting quantum interference device SQUID flux qubit that is robust against fabrication variations in Josephson-junction critical currents and device inductance has been implemented. Measurements of the persistent current and of the tunneling energy between the two lowest-lying states, both in the coherent and incoherent regimes, are presented. These experimental results are shown to be in agreement with predictions of a quantum-mechanical Hamiltonian whose parameters were independently calibrated, thus justifying the identification of this device as a flux qubit. In addition, measurements of the flux and critical current noise spectral densities are presented that indicate that these devices with Nb wiring are comparable to the best Al wiring rf SQUIDs reported in the literature thus far, with a 1 /f flux noise spectral density at 1 Hz of 1.3 �0.5 0 /Hz. An explicit formula for converting the observed flux noise spectral density into a freeinduction-decay time for a flux qubit biased to its optimal point and operated in the energy eigenbasis is presented.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Condensed Matter Physics

View Paper: https://www.semanticscholar.org/paper/a39e046fbf2907d86218fc9e90abaef80aaa49ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

In Condensed Matter Physics

Citations in the corpus

Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design (2010, background): “See Figure 1 ( a ) for the schematic of physical qubit-coupler-qubit, and [12] for its design and experimental results.”
Decoherence in adiabatic quantum computation (2015, background): “For such flux qubits, the T2 time can at present range from tens of nanoseconds to a few hundred nanoseconds [21, 22], yet the computation lasts on the order of microseconds to milliseconds.”
Evidence for quantum annealing with more than one hundred qubits (2013, methodology): “[30] Harris, R.”
A practical heuristic for finding graph minors (2014, background): “D-Wave Two solves these problems using adiabatic quantum annealing [8]: a quantum system is slowly evolved until the ground state of the system encodes the solution to the optimization problem [11, 13].”
Simulating chemistry using quantum computers. (2010, background): “13, with Hi ∝ ∑ i σ i x as the initial Hamiltonian (60, 58, 49).”
A scalable control system for a superconducting adiabatic quantum optimization processor (2009, methodology): “Here we provide a brief overview of how these numbers arise, and refer the reader to [14, 15] for further details.”
Qubit compatible superconducting interconnects (2017, background): “Multilayer fabrication with embedded routing layers is a natural solution [7], but integrated dielectric layers on a qubit wafer introduce additional decoherence to the qubits [8].”