C. J. S. Truncik

TL;DR: This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples, and may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.

TL;DR: In this article, a superconducting chip containing a regular array of flux qubits, tunable interqubit inductive couplers, an XY-addressable readout system, on-chip programmable magnetic memory, and a sparse network of analog control lines has been studied.

TL;DR: In this paper, the authors explore the use of a quantum optimization algorithm for obtaining low-energy conformations of protein models, which are in turn mapped to a system of coupled quantum bits.

TL;DR: The method is applied to the case of adiabatic Grover search and it is shown that performance better than classical is possible with a super-Ohmic environment, with no a priori knowledge of the energy spectrum.

TL;DR: In this paper, numerically the evolution of an adiabatic quantum computer in the presence of a Markovian Ohmic environment was studied by considering Ising spin-glass systems with up to 20 qubits independently coupled to this environment via two conjugate degrees of freedom.