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Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

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Abstract

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled–controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the “Luck-Choose” mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

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Acknowledgements

All experiments were performed on Bruker Avance-III 600 and 400 MHz FT-NMR spectrometers at the NMR Research Facility at IISER Mohali. K.D. acknowledges funding from DST India under Grant No. EMR/2015/000556. A. acknowledges funding from DST India under Grant No. EMR/2014/000297. H. S. acknowledges financial support from CSIR India. P. P. acknowledges funding from the Indian Academy of Sciences, Bangalore, India, under the Summer Research Fellowship Programme.

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Devra, A., Prabhu, P., Singh, H. et al. Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming. Quantum Inf Process 17, 67 (2018). https://doi.org/10.1007/s11128-018-1835-8

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