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Progress in Solid-State implementations of Quantum Computing

Overview
About two decades ago quantum computing (QC) was introduced with a promise to the solution of many unmanageable problems (Ruggiero, 2005). Experiments related to a small group of qubits using Nuclear Magnetic Resonance (NMR) showed that QC can be done (Keyes, 2003). After the arrival of powerful algorithms for quantum computation; efforts are made to implement these devices physically. According to research, two qubits (the basic element of a quantum computer) interactions were ample for quantum algorithm implementation (Keyes, 2003). It was proved that a scalable physical system with qubits which is initializable to a simple fiducial state, with longer decoherence time and composed of a universal set of quantum gates that allows high quantum efficiency measurements, can be a candidate for implementation of QC (DiVincenzo, 2000). NMR with molecules is bound to ten qubits whereas a useful quantum computer would need more. To achieve this, solid-state technology is the easiest way (Keyes, 2003).

Recent Advances
In early 2000, the solid-state QC in its infancy showed only the simplest quantum logical operation with a hope for scalable architectures. This newly emerging field at that time could take advantage of the research meant for the progress of smaller and faster logical devices (Kane, 2003). There are difficulties in integrating qubits into solid-state systems because of the fact that the necessary decoupling required for QC is hard to achieve because 1023 atoms are present in a solid-state device. Recently, research is more concentrating on superconducting qubits where quantum information is stored on flux states in a SQUID or in semiconductors on electron or nuclear spin qubits. Also coding the quantum information repeatedly into several qubits can lessen the errors along with correction in quantum computation (Kane, 2003).

The creation and detailed analysis of highly optimized self refocusing pulse shapes for various rotation angles was presented by Pryadko and Sengupta, (2008) calling it the second order shaped pulses for solid-state quantum computation. Description of the constructed pulses with the help of appearing coefficients in the Magnus expansion up to second order which permits a semi analytical analysis of the functioning of these constructed shapes in a sequence and the complex pulses with the help of computing the similar leading order error operators. In a previous technique higher orders can also be analyzed while here the technique was demonstrated by the analysis of several composite pulses that are designed to guard against pulse amplitude errors and on decoupling sequences capable of long chains of qubits that have on the scene and nearest neighbour couplings as shown in fig.3.

In a recent research issues related to practical implementation of QC like scalability, switching of coupling interaction and decoherence free behaviour were addressed (Ruda and Qiao, 2003). A few approaches for implementation of solid-state were brought-in where one of the best was the silicon based nuclear spin or electron spin computers. Still the recognition of these schemes is dependent on the future technology for implementation of nanostructures (Ruggiero, 2005). Here a new approach for fabricating large scale lattices of nanostructures was demonstrated but again the implementation on QC is dependent on future technology. Ruda and Qiao (2003) has discussed the problem of decoherence and the use of error avoiding approaches. More precisely a novel scheme based on sub-dynamics was presented and a bright future for solid-state QC was predicted provided the architecture and control strategies are suitable.

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