Few‐Qubit Magnetic Resonance Quantum Information Processors: Simulating Chemistry and Physics

Criger, Ben; Park, Daniel K.; Baugh, Jonathan

doi:10.1002/9781118742631.ch08

Cited by 7 publications

(8 citation statements)

References 136 publications

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“…In NMR QIP, irradiating the nuclei with resonant radio frequency (RF) pulses allows to manipulate the nuclei, giving rise to generic single-qubit gates. , In a heteronuclear spin system, in which the nuclei have distinct nuclear g-factors, the qubits can easily be individually addressed through RF pulses . In larger heteronuclear spin systems, as every spin will be a long way from resonance with every other spin, simple hard pulses applied on resonance can be used .…”

Section: Resultsmentioning

confidence: 99%

Enhancing NMR Quantum Computation by Exploring Heavy Metal Complexes as Multiqubit Systems: A Theoretical Investigation

2020

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Assembled together with the most common qubits used in nuclear resonance magnetic (NMR) quantum computation experiments, spin-1/2 nuclei, such as 113 Cd, 199 Hg, 125 Te, and 77 Se, could leverage the prospective scalable quantum computer architectures, enabling many and heteronuclear qubits for NMR quantum information processing (QIP) implementations. A computational design strategy for prescreening recently synthesized complexes of cadmium, mercury, tellurium, selenium, and phosphorus (called MRE complexes) as suitable qubit molecules for NMR QIP is reported. Chemical shifts and spin−spin coupling constants (SSCCs) in five MRE complexes were examined using the spin−orbit zeroth order regular approximation (ZORA) at the density functional theory level and the four-component relativistic Dirac−Kohn−Sham approach. In particular, the influence of different conformers, basis sets, exchange−correlation functionals, and methods to treat the relativistic as well as solvent effects were studied. The differences in the chemical shifts and SSCCs between different low energy conformers of the studied complexes were found to be very small. The TZ2P basis set was found to be the optimum choice for the studied chemical shifts, while the TZ2P-J basis set was the best for the couplings studied in this work. The PBE0 exchange−correlation functional exhibited the best performance for the studied MRE complexes. The addition of solvent effects has not improved on the gas phase results in comparison to the experiment, with the exception of the phosphorus chemical shift. The use of MRE complexes as qubit molecules for NMR QIP could face the challenges in single qubit control and multiqubit operations. They exhibit chemical shifts appropriately dispersed, allowing qubit addressability and exceptionally large spin−spin couplings, which could reduce the time of quantum gate operations and likely preserve the coherence.

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Section: Resultsmentioning

confidence: 99%

Enhancing NMR Quantum Computation by Exploring Heavy Metal Complexes as Multiqubit Systems: A Theoretical Investigation

2020

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“…As the quest for fault-tolerant quantum computers continues, noisy intermediate-scale quantum (NISQ) computers are expected to be available in the near future [1], supported by the recent technological advances in quantum computing [2][3][4][5][6][7][8][9]. Although the size of quantum circuits that NISQ devices can execute reliably is limited, the size of the quantum state space efficiently manipulated by them is much beyond what classical computers can handle.…”

Section: Introductionmentioning

confidence: 99%

Compact quantum distance-based binary classifier

Blank,

da Silva,

de Albuquerque

et al. 2022

Preprint

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Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers can efficiently construct a model of a classifier by engineering the quantum interference effect to carry out the kernel evaluation in parallel. For practical applications of these quantum machine learning methods, an important issue is to minimize the size of quantum circuits. We present the simplest quantum circuit for constructing a kernel-based binary classifier. This is achieved by generalizing the interference circuit to encode data labels in the relative phases of the quantum state and by introducing compact amplitude encoding, which encodes two training data vectors into one quantum register. When compared to the simplest known quantum binary classifier, the number of qubits is reduced by two and the number of steps is reduced linearly with respect to the number of training data. The two-qubit measurement with post-selection required in the previous method is simplified to single-qubit measurement. Furthermore, the final quantum state has a smaller amount of entanglement than that of the previous method, which advocates the cost-effectiveness of our method. Our design also provides a straight-forward way to handle an imbalanced data set, which is often encountered in many machine learning problems.

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“…SIMPSON was lately expanded to implement the optimal control algorithm GRAPE. 2,7,13,[24][25][26][27][28] In this work, we designed, using SIMPSON, a polarization exchange (PE) pulse, on a 3-qubit system. We then also implemented that designed pulse experimentally.…”

Section: Introductionmentioning

confidence: 99%

Quantum computing gates via optimal control

Atia

Elias

Mor

et al. 2014

Int. J. Quantum Inform.

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We demonstrate the use of optimal control to design two entropy-manipulating quantum gates which are more complex than the corresponding, commonly used, gates, such as CNOT and Toffoli (CCNOT): A 2-qubit gate called PE (polarization exchange) and a 3-qubit gate called COMP (polarization compression) were designed using GRAPE, an optimal control algorithm. Both gates were designed for a three-spin system. Our design provided efficient and robust NMR radio frequency (RF) pulses for 13 C 2 -trichloroethylene (TCE), our chosen three-spin system. We then experimentally applied these two quantum gates onto TCE at the NMR lab. Such design of these gates and others could be relevant for near-future applications of quantum computing devices.

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Few‐Qubit Magnetic Resonance Quantum Information Processors: Simulating Chemistry and Physics

Cited by 7 publications

References 136 publications

Enhancing NMR Quantum Computation by Exploring Heavy Metal Complexes as Multiqubit Systems: A Theoretical Investigation

Enhancing NMR Quantum Computation by Exploring Heavy Metal Complexes as Multiqubit Systems: A Theoretical Investigation

Compact quantum distance-based binary classifier

Quantum computing gates via optimal control

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