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The two most important open questions in quantum algorithms are graph isomorphism and the (gap) shortest-lattice vector problem The rst of these corresponds to the HSP in the symmetric group, and Regev [535] showed that the second can be reduced to the HSP in the dihedral group The dihedral group is a particularly simple non-Abelian group because it has a cyclic subgroup of index 2 The standard quantum algorithm for Abelian HSP can be generalized in a natural way to non-Abelian groups It was shown by Grigni, Schulman, Vazirani, and Vazirani [536] that for su ciently nonAbelian groups the standard algorithm yields only an exponentially small amount of information about the hidden subgroup On the other hand, Ettinger, Hoyer, and Knill [537] showed that the quantum query complexity of the problem is polynomial This suggests that novel algorithmic ideas are necessary to tackle the nonAbelian HSP.

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You can use this information to check dimensions (covered in 14) or to make calculations that you need for drawing.

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In this introduction, some well-known basic concepts are recalled, and a simple example is introduced that will be used in the following paragraphs for the illustration of the different nonlinear analysis methods. Electrical and electronic circuits are described by means of voltages and currents. The equations that ful l the topological constraints of the network, and that form the basis for the network analysis, are Kirchhoff s equations. The equations describe the constraints on voltages (mesh equations) or currents (nodal equations), expressing the constraint that the sum of all the voltages in each mesh, or, respectively, that all the currents entering each node, must sum up to zero. The number of equations is one half of the total number of the unknown voltages and currents. The system can be solved when the relation between voltage and current in each element of the network is known (constitutive relations of the elements). In this way, for example, in the case of nodal equations, the currents that appear in the equations are expressed as functions of the voltages that are the actual unknowns of the problem. Let us illustrate this by means of a simple example (Figure 1.1). is + ig + iC = 0 is = is (t) ig = g v dv iC = C dt Nodal Kirchhoff s equation (1.1) (1.2)

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Recently, Kuperberg [538] gave a O(2 n) algorithm for the dihedral HSP The algorithm was an interesting modi cation of the standard algorithm Other computational problems that are potential targets for quantum algorithms are the nonsolvable group membership, the McElise cryptosystem, and the learning AC0 circuits A di erent approach to designing quantum optimization algorithms via adiabatic evolution was proposed by Farhi et al [539] Initial e orts in this direction concentrated on the question about whether adiabatic optimization could solve NP-complete problems such as variants on SAT in polynomial time Surprisingly, query lower bounds do not rule out this possibility [481] However, van Dam and Vazirani [540] and more recently Reichardt [541] gave classes of SAT instances for which the spectral gap is exponentially small Nevertheless, Farhi et al [542] showed that adiabatic quantum.

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frequency f 1 is decreased and is lower than 1900 MHz On the other hand, the higher frequency f 2 is very slightly affected by the variation in the spur-line length and is about the same as the frequency f 10 of the simple triangular microstrip antenna This is because the embedded spur lines are parallel to the excited patch surface current path of the TM10 mode, and thus a very small perturbation in the TM10 mode can be expected Such different effects of the spur-line length on f 1 and f 2 provide the present design with a tunable frequency-ratio range of about 12 21 It can be seen that the optimal feed positions in this design are almost about the same, which indicates that the feed position is insensitive to the variation in the spur-line length Radiation patterns at the two operating frequencies were also measured.

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