NMR techniques for quantum control and computation

By Lieven M. K. Vandersypen, Isaac L. Chuang Kavli Institute of NanoScience, Delft University of Technology, Center for Bits, A. O. Physics, M. I. O. Technology. 2004

981 citations recorded by the source corpus.

Fifty years of developments in nuclear magnetic resonance (NMR) have resulted in an unrivaled degree of control of the dynamics of coupled two-level quantum systems. This coherent control of nuclear spin dynamics has recently been taken to a new level, motivated by the interest in quantum information processing. NMR has been the workhorse for the experimental implementation of quantum protocols, allowing exquisite control of systems up to seven qubits in size. This article surveys and summarizes a broad variety of pulse control and tomographic techniques which have been developed for, and used in, NMR quantum computation. Many of these will be useful in other quantum systems now being considered for the implementation of quantum information processing tasks.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/60d18faf30f8ec20daeb5821337a171926165290

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Citations in the corpus

A Ten-Qubit Solid-State Spin Register with Quantum Memory up to One Minute (2019, background): “Finally, because control is achieved through the rf field, a multitude of avenues for future investigation are opened up, such as parallelizing gates by frequency multiplexing and using shaped and composite pulses to mitigate dephasing and cross talk [38,56,57].”
Experimental multiparticle entanglement dynamics induced by decoherence (2010, methodology): “Finally, another GHZ-entangling operation and an NMR-like refocusing techniqu”
Cavity-based quantum networks with single atoms and optical photons (2014, background): “For a review, see (Vandersypen and Chuang, 2005).”
Sensitivity optimization for NV-diamond magnetometry (2019, background): “(Vandersypen and Chuang, 2005).”
1 / f noise: Implications for solid-state quantum information (2013, methodology): “Composite pulses are also used in NMR to stabilize a given quantum gate against errors in the control (Becker, 2000; Vandersypen and Chuang, 2005).”
Natural and artificial atoms for quantum computation (2010, methodology): “, with nuclear spins in molecules [6, 7] or in phosphorus impurities in silicon [8, 9], photons [10, 11], and so on) are also be briefly covered here.”
Quantum error correction for quantum memories (2013, neutral): “Standard NMR techniques (Vandersypen and Chuang, 2005) have been adapted in such systems to average out these fluctuations using rapid pulse sequences (e.g., spin echo).”
Quantum decoherence (2019, background): “An example would be classical noise processes arising, for instance, from experimental fluctuations and imperfections, such as variations in laser intensities in ion-trap experiments [109, 110], bias fluctuations in superconducting qubits [111], and inhomogeneities in the magnetic fields used in NMR”
Fault-tolerant architecture for quantum computation using electrically controlled semiconductor spins (2005, background): “The BB1 composite pulse sequence from NMR is effective in eliminating these error”
Methodology of Resonant Equiangular Composite Quantum Gates (2016, background): “Composite quantum gates [1,2] are indispensable to many important quantum technologies, such as nuclear magnetic resonance, magnetic resonance imaging, quantum sensing, and quantum computation [3].”
Detecting crosstalk errors in quantum information processors (2019, neutral): “, [57, 44, 43, 10, 34].”
Grand Unification of Quantum Algorithms (2021, background): “Many such “composite pulse” sequences are known [9, 17, 18], with a wide range of variations, and in experimental quantum computation, they serve to suppress specific kinds of errors and enhance sensitivity to specific kinds of signals, across implementations ranging from quantum dots [19] and nitro”
Quantum control theory and applications: A survey (2009, neutral): “ontrols. Optimal control techniques have been widely applied to control quantum phenomena in physical chemistry (for details, see, e.g., [9], [11], [12], [108], [109]) and NMR experiments [39], [40], [81]. Here we present a simple example to demonstrate optimal quantum control design. We consider th”