Quantum logical elements for quantum computer based on nanocrystalline silicon

2004 ◽  
Author(s):  
Dmitri E. Milovzorov
2005 ◽  
Vol 10 ◽  
pp. 85-88
Author(s):  
K Theodoropoulos ◽  
D Ntalaperas ◽  
I Petras ◽  
N Konofaos

2014 ◽  
Vol 12 (03) ◽  
pp. 1430002 ◽  
Author(s):  
Eliahu Cohen ◽  
Boaz Tamir

On May 2011, D-Wave Systems Inc. announced "D-Wave One", as "the world's first commercially available quantum computer". No wonder this adiabatic quantum computer based on 128-qubit chip-set provoked an immediate controversy. Over the last 40 years, quantum computation has been a very promising yet challenging research area, facing major difficulties producing a large scale quantum computer. Today, after Google has purchased "D-Wave Two" containing 512 qubits, criticism has only increased. In this work, we examine the theory underlying the D-Wave, seeking to shed some light on this intriguing quantum computer. Starting from classical algorithms such as Metropolis algorithm, genetic algorithm (GA), hill climbing and simulated annealing, we continue to adiabatic computation and quantum annealing towards better understanding of the D-Wave mechanism. Finally, we outline some applications within the fields of information and image processing. In addition, we suggest a few related theoretical ideas and hypotheses.


2004 ◽  
Vol 02 (03) ◽  
pp. 379-392 ◽  
Author(s):  
G. P. BERMAN ◽  
D. I. KAMENEV ◽  
V. I. TSIFRINOVICH

The errors caused by the transitions with large frequency offsets (nonresonant transitions) are calculated analytically for a scalable solid-state quantum computer based on a one-dimensional spin chain with Ising interactions between neighboring spins. Selective excitations of the spins are enabled by a uniform gradient of the external magnetic field. We calculate the probabilities of all unwanted nonresonant transitions associated with the flip of each spin with nonresonant frequency and with flips of two spins: one with resonant and one with nonresonant frequencies. It is shown that these errors oscillate with changing gradient of the external magnetic field. Choosing the optimal values of this gradient allows us to decrease these errors by 50%.


2017 ◽  
Vol 46 (2) ◽  
pp. 109-120 ◽  
Author(s):  
I. I. Ryabtsev ◽  
I. I. Beterov ◽  
E. A. Yakshina ◽  
D. B. Tretyakov ◽  
V. M. Entin ◽  
...  

2000 ◽  
Vol 61 (11) ◽  
pp. 7526-7535 ◽  
Author(s):  
G. D. Sanders ◽  
K. W. Kim ◽  
W. C. Holton

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