Abstract
Quantum computing exploits fundamentally new models of computation based on quantum mechanical properties instead of classical physics, and it is believed that quantum computers are able to dramatically improve computational power for particular tasks. At present, nuclear magnetic resonance (NMR) has been one of the most successful platforms amongst all current implementations. It has demonstrated universal controls on the largest number of qubits, and many advanced techniques developed in NMR have been adopted to other quantum systems successfully. In this review, we show how NMR quantum processors can satisfy the general requirements of a quantum computer, and describe advanced techniques developed towards this target. Additionally, we review some recent NMR quantum processor experiments. These experiments include benchmarking protocols, quantum error correction, demonstrations of algorithms exploiting quantum properties, exploring the foundations of quantum mechanics, and quantum simulations. Finally we summarize the concepts and comment on future prospects.
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- 1.
For simplicity, we only consider spin ½ systems in this chapter.
- 2.
The Clifford group is the group of unitary operations that leave take Pauli operators to Pauli operators.
- 3.
In general, a protected space does not necessarily encode a single qubit, and single qubit gates are often not transversal.
- 4.
The argument regarding the number of elements to be summed is somewhat simplistic, since we only require an estimate. Nevertheless, there is good reason to expect the computation scale exponentially with n [78].
- 5.
Strictly speaking, it should also be scalable.
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Acknowledgement
We thank Rolf Horn for helpful comments and discussions. This work is supported by Industry Canada, NSERC and CIFAR.
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Lu, D., Brodutch, A., Park, J., Katiyar, H., Jochym-O’Connor, T., Laflamme, R. (2016). NMR Quantum Information Processing. In: Takui, T., Berliner, L., Hanson, G. (eds) Electron Spin Resonance (ESR) Based Quantum Computing. Biological Magnetic Resonance, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3658-8_7
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