Quantum Simulation of Helium Hydride Cation in a Solid-State Spin Register

Wang, Ya; Dolde, Florian; Biamonte, Jacob; Babbush, Ryan; Bergholm, Ville; Yang, Sen; Jakobi, Ingmar; Neumann, Philipp; Aspuru‐Guzik, Alán; Whitfield, James D.; Wrachtrup, Jörg

doi:10.1021/acsnano.5b01651

Cited by 156 publications

(133 citation statements)

References 55 publications

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“…Two interesting examples are lithium hydride (LiH) and lithium imide (Li 2 NH). LiH is used in the nuclear industry either as a shielding agent or fuel in energy reactors (Welch, 1974;Veleckis, 1977). LiH is an ionic crystal that stabilises in the rocksalt structure at ambient conditions; Li is the cation (positively charged ion) and H the anion (negatively charged ion).…”

Section: Lithium and Related Compoundsmentioning

confidence: 99%

“…Diamonds with negatively charged nitrogen-vacancy centers, another type of carbonaceous nanomaterial, are playing a crucial role on the development of scalable quantum computing components (Fuchs et al, 2011;Nemoto et al, 2014). This class of crystals also can be employed as tunable quantum simulators that, in analogy to ultracold atom gases trapped in optical lattices (Lewestein et al, 2007), can be used to answer fundamental questions in the fields of condensed matter, biology, and high energy physics (Georgescu et al, 2014;Wang et al, 2015). Finally, quantum paraelectrics find numerous applications in nanoelectronics as varistors, supercapacitor electrodes, and substrates on which to grow epitaxial films of other perovskite compounds (Lawless, 1974;Schlom et al, 2007;Cazorla and Stengel, 2012).…”

Section: Introduction a Quantum Crystals: Definition And Interestsmentioning

confidence: 99%

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Simulation and understanding of atomic and molecular quantum crystals

2017

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Quantum crystals abound in the whole range of solid-state species. Below a certain threshold temperature the physical behavior of rare gases ( 4 He and Ne), molecular solids (H 2 and CH 4 ), and some ionic (LiH), covalent (graphite), and metallic (Li) crystals can be only explained in terms of quantum nuclear effects (QNE). A detailed comprehension of the nature of quantum solids is critical for achieving progress in a number of fundamental and applied scientific fields like, for instance, planetary sciences, hydrogen storage, nuclear energy, quantum computing, and nanoelectronics. This review describes the current physical understanding of quantum crystals formed by atoms and small molecules, as well as the wide palette of simulation techniques that are used to investigate them. Relevant aspects in these materials such as phase transformations, structural properties, elasticity, crystalline defects and the effects of reduced dimensionality, are discussed thoroughly. An introduction to quantum Monte Carlo techniques, which in the present context are the simulation methods of choice, and other quantum simulation approaches (e. g., path-integral molecular dynamics and quantum thermal baths) is provided. The overarching objective of this article is twofold. First, to clarify in which crystals and physical situations the disregard of QNE may incur in important bias and erroneous interpretations. And second, to promote the study and appreciation of QNE, a topic that traditionally has been treated in the context of condensed matter physics, within the broad and interdisciplinary areas of materials science.

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Section: Lithium and Related Compoundsmentioning

confidence: 99%

Section: Introduction a Quantum Crystals: Definition And Interestsmentioning

confidence: 99%

Simulation and understanding of atomic and molecular quantum crystals

2017

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“…Optimal control methods have been applied to several quantum information processing tasks with NV centres, [17][18][19][20][21][22] affirming their necessity and significance for quantum technology. However, the previously reported experiments, [17][18][19][20][21][22] utilise openloop optimisation techniques where the optimisation is performed before the actual experiment by separate computer simulations.…”

Section: Introductionmentioning

confidence: 99%

“…However, the previously reported experiments, [17][18][19][20][21][22] utilise openloop optimisation techniques where the optimisation is performed before the actual experiment by separate computer simulations. The technique requires system-environment coupling information as detailed as possible to provide a robust solution.…”

Section: Introductionmentioning

confidence: 99%

Autonomous calibration of single spin qubit operations

et al. 2017

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Fully autonomous precise control of qubits is crucial for quantum information processing, quantum communication, and quantum sensing applications. It requires minimal human intervention on the ability to model, to predict, and to anticipate the quantum dynamics, as well as to precisely control and calibrate single qubit operations. Here, we demonstrate single qubit autonomous calibrations via closed-loop optimisations of electron spin quantum operations in diamond. The operations are examined by quantum state and process tomographic measurements at room temperature, and their performances against systematic errors are iteratively rectified by an optimal pulse engineering algorithm. We achieve an autonomous calibrated fidelity up to 1.00 on a time scale of minutes for a spin population inversion and up to 0.98 on a time scale of hours for a single qubit π 2 -rotation within the experimental error of 2%. These results manifest a full potential for versatile quantum technologies.npj Quantum Information (2017) 3:48 ; doi:10.1038/s41534-017-0049-8 INTRODUCTIONThe ability to precisely control and calibrate single qubit operations in solids is a key element for reliable and scalable high-performance quantum technologies, for instance quantumenhanced sensors and metrological devices. It is also the backbone of many quantum information processing tasks, which paves the way for the future realisation of quantum computation and communication. Together with efficient quantum system characterisations and dynamical predictions, 1,2 where human intervention is minimised, autonomous calibration of a single spin qubit is necessary for the realisation of such advanced quantum technologies. We report here experimental demonstrations of the autonomous calibration of a single spin qubit in diamond using closed-loop optimisation. Our spin qubit implementation is a single nitrogen-vacancy (NV) colour centre in diamond. It provides a suitable platform for a precise qubit manipulation to be realised. 3,4 Its remarkable features, such as optical initialisation and readout, and the ability to be manipulated by microwave fields at room temperature, make this physical system extremely attractive for many quantum technologies. 5 We have witnessed a vast array of demonstrations of the NV centres showing a great potential for future technologies, ranging from sub pico-Tesla magnetometry, 6 electric field and temperature sensing, 7,8 to probing molecular dynamics, 9 and single-cell magnetic imaging. 10 Furthermore, intertwinements between quantum information and metrology using NV centre-based systems yield novel and effective techniques towards the realisation of high-performance technologies, e.g. applying quantum error correction 11 and phase estimation 12 to improve magnetic field sensitivity. One way to reach such technology is to apply the closed-loop optimisation method for auto-calibrating the controls required to drive the system in the presence of experimental limitations and noise. Closed-loop optimal control has been already applied...

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“…Many quantum gates [6][7][8][9], quantum algorithms [10], quantum error corrections [11,12] and quantum simulations [13,14] have been demonstrated on it. However, so far no adiabatic quantum algorithm has been realized on this system.…”

mentioning

confidence: 99%

Experimental Adiabatic Quantum Factorization under Ambient Conditions Based on a Solid-State Single Spin System

Xie

et al. 2017

Phys. Rev. Lett.

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The adiabatic quantum computation is a universal and robust method of quantum computing. In this architecture, the problem can be solved by adiabatically evolving the quantum processor from the ground state of a simple initial Hamiltonian to that of a final one, which encodes the solution of the problem. By far, there is no experimental realization of adiabatic quantum computation on a single solid spin system under ambient conditions, which has been proved to be a compatible candidate for scalable quantum computation. In this letter, we report on the first experimental realization of an adiabatic quantum algorithm on a single solid spin system under ambient conditions. All elements of adiabatic quantum computation, including initial state preparation, adiabatic evolution, and final state readout, are realized experimentally. As an example, we factored 35 into its prime factors 5 and 7 on our adiabatic quantum processor.The nitrogen-vacancy (NV) center in diamond is an excellent quantum processor and quantum sensor at room temperature [1].The spin qubits of NV center are promising for quantum information processing due to fast resonant spin manipulation [2], long coherence time [3,4], easy initialization and read-out by laser illumination [5]. Many quantum gates [6-9], quantum algorithms [10], quantum error corrections [11,12] and quantum simulations [13,14] have been demonstrated on it. However, so far no adiabatic quantum algorithm has been realized on this system. In circuit-model quantum computation, the computational process is implemented by a sequence of quantum gates. In 2000, Farhi et al.[15] developed another architecture of quantum computation, i.e., the adiabatic quantum computing (AQC), in which the computational process can be realized through the adiabatic evolution of a system's Hamiltonian, and it is proved to be equivalent to circuit model quantum computing [16].In contrast to multiplying of large prime numbers, up to now, no efficient classical algorithm for the factorization of large number is known [17]. Previously, many experimental work on large number factorization have been done based on Shor's algorithm [18][19][20][21][22][23][24]. To demonstrate the AQC on the room temperature single spin system, we take 35 as an example and factored it on the adiabatic quantum processor. The core idea used here is to transform a factorization problem to an optimization problem and solve it under the AQC framework [25,26].Generally, to solve a problem under the AQC framework, first we need to find a problem Hamiltonian H p , and the solution of the problem is encoded in the ground state of H p . We start from the ground state of H 0 and the Hamiltonian of the evolution progress is H(t) =(1 − s(t))H 0 + s(t)H p , s(0) = 0, s(T ) = 1.(1)Where T is the total evolution time, and the whole system is governed by the Schrödinger equation

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Quantum Simulation of Helium Hydride Cation in a Solid-State Spin Register

Cited by 156 publications

References 55 publications

Simulation and understanding of atomic and molecular quantum crystals

Simulation and understanding of atomic and molecular quantum crystals

Autonomous calibration of single spin qubit operations

Experimental Adiabatic Quantum Factorization under Ambient Conditions Based on a Solid-State Single Spin System

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