Hybrid Quantum-Classical Approach to Quantum Optimal Control.
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TLDR
It is shown that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator.Abstract:
A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.read more
Citations
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Quantum circuit learning
TL;DR: In this paper, a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, called quantum circuit learning, is proposed, which can approximate nonlinear functions.
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Variational Quantum Algorithms
Marco Cerezo,Marco Cerezo,Andrew Arrasmith,Andrew Arrasmith,Ryan Babbush,Simon C. Benjamin,Suguru Endo,Keisuke Fujii,Jarrod R. McClean,Kosuke Mitarai,Kosuke Mitarai,Xiao Yuan,Xiao Yuan,Lukasz Cincio,Lukasz Cincio,Patrick J. Coles,Patrick J. Coles +16 more
TL;DR: An overview of the field of Variational Quantum Algorithms is presented and strategies to overcome their challenges as well as the exciting prospects for using them as a means to obtain quantum advantage are discussed.
Journal ArticleDOI
Variational Quantum Algorithms
Marco Cerezo,Marco Cerezo,Andrew Arrasmith,Andrew Arrasmith,Ryan Babbush,Simon C. Benjamin,Suguru Endo,Keisuke Fujii,Jarrod R. McClean,Kosuke Mitarai,Kosuke Mitarai,Xiao Yuan,Xiao Yuan,Lukasz Cincio,Lukasz Cincio,Patrick J. Coles,Patrick J. Coles +16 more
TL;DR: Variational quantum algorithms (VQAs) as discussed by the authors use a classical optimizer to train a parameterized quantum circuit, which is a leading strategy to address the limitations of classical computers.
Journal ArticleDOI
Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm
J. I. Colless,Vinay Ramasesh,Dar Dahlen,Machiel Blok,Mollie E. Kimchi-Schwartz,Jarrod R. McClean,Jonathan Carter,W. A. de Jong,Irfan Siddiqi +8 more
TL;DR: An extended protocol based on a quantum subspace expansion (QSE) is used to apply the QSE approach to the H2 molecule, extracting both ground and excited states without the need for auxiliary qubits or additional minimization and can mitigate the effects of incoherent errors.
Journal ArticleDOI
Parameterized quantum circuits as machine learning models
TL;DR: In this paper, the authors present the components of these models and discuss their application to a variety of data-driven tasks, such as supervised learning and generative modeling, as well as their application in machine learning applications.
References
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