Quantum Computing Circuits and Devices

Humble, Travis S.; Thapliyal, Himanshu; Muñoz‐Coreas, Edgard; Mohiyaddin, Fahd A.; Bennink, Ryan S.

doi:10.1109/mdat.2019.2907130

Cited by 64 publications

(43 citation statements)

References 136 publications

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“…Concurrent with developments in CC theory has been the increase in performance of computing technologies, which broadens the reach of computational chemistry techniques. Presently, this trend is continuing with the adaptation of chemistry methods, including CC, to the new technology paradigm of quantum computing (Britt and Humble, 2017;Humble et al, 2019). Because of the shared foundation in quantum mechanics, one of the most immediate applications for quantum computers is quantum chemistry (McArdle et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Adaptive Variational Quantum Eigensolvers

et al. 2020

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By design, the variational quantum eigensolver (VQE) strives to recover the lowest-energy eigenvalue of a given Hamiltonian by preparing quantum states guided by the variational principle. In practice, the prepared quantum state is indirectly assessed by the value of the associated energy. Novel adaptive derivative-assembled pseudo-trotter (ADAPT) ansatz approaches and recent formal advances now establish a clear connection between the theory of quantum chemistry and the quantum state ansatz used to solve the electronic structure problem. Here we benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves for a few selected diatomic molecules, namely H2, NaH, and KH. Using numerical simulation, we find both methods provide good estimates of the energy and ground state, but only ADAPT-VQE proves to be robust to particularities in optimization methods. Another relevant finding is that gradient-based optimization is overall more economical and delivers superior performance than analogous simulations carried out with gradient-free optimizers. The results also identify small errors in the prepared state fidelity which show an increasing trend with molecular size.

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Section: Introductionmentioning

confidence: 99%

Benchmarking Adaptive Variational Quantum Eigensolvers

et al. 2020

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“…To deal with this problem, researchers have been exploring alternative computing techniques such as quantum computing to augment the power of machine learning algorithms [1,6,28]. Quantum computing is inherently suited to carry out complex computation which is not possible using classical computation [21].…”

Section: Introductionmentioning

confidence: 99%

A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

2021

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Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave's quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

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“…We discuss a new type of qubit with integrability, based on a nonlinear quantum capacitor (QC) made of twodimensional materials [1], graphene (Gr), and boron nitride (BN) and their heterostructures [2,3]. Without exception, all existing superconducting qubits [4][5][6][7][8][9] rely on the nonlinear inductance of Josephson junctions (JJs). Here, the nonlinear inductance is replaced by the nonlinear capacitance coming from two Gr monolayers separated by a multilayer BN, which acts as a potential barrier and forms a parallel plate capacitance [1].…”

Section: Introductionmentioning

confidence: 99%

CUBIT: Capacitive qUantum BIT

Khorasani

2018

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Abstract:In this letter, it is proposed that cryogenic quantum bits can operate based on the nonlinearity due to the quantum capacitance of two-dimensional Dirac materials, and in particular graphene. The anharmonicity of a typical superconducting quantum bit is calculated, and the sensitivity of quantum bit frequency and anharmonicity with respect to temperature are found. Reasonable estimates reveal that a careful fabrication process can reveal expected properties, putting the context of quantum computing hardware into new perspectives.

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Quantum Computing Circuits and Devices

Cited by 64 publications

References 136 publications

Benchmarking Adaptive Variational Quantum Eigensolvers

Benchmarking Adaptive Variational Quantum Eigensolvers

A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

CUBIT: Capacitive qUantum BIT

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