Quantum deep learning by sampling neural nets with a quantum annealer

Article Multidisciplinary Sciences

Parallel quantum annealing

Elijah Pelofske et al.

Summary: This article introduces the quantum annealers of D-Wave Systems, Inc. and discusses a method called parallel quantum annealing to improve computational efficiency. Parallel processing with quantum annealing can significantly accelerate the time-to-solution, and even solving a single problem using parallel quantum annealing can reduce the solving time.

SCIENTIFIC REPORTS (2022)

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Article Optics

Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond

Adam Callison et al.

Summary: The article discusses the significance of hybrid quantum-classical algorithms in current research on quantum computing. It explores the definition of hybrid algorithms and their characteristics. The article reviews the historical development of hybrid algorithms and discusses their future prospects. It concludes that the evolution of quantum computing will follow a similar trend as classical computing, with quantum processors augmenting classical processors through specialized tasks.

PHYSICAL REVIEW A (2022)

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Article Physics, Multidisciplinary

Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer

Vivek Dixit et al.

Summary: This study calculates the model expectation of gradient learning for RBM using a quantum annealer, showing improved learning speed; compares the training and classification results of RBM trained using a quantum annealer with CD-based methods in terms of classification accuracies, image reconstruction, and log-likelihood results; samples from a quantum annealer can be used to train an RBM, but the CD-based method performs better in image reconstruction and log-likelihood results.

FRONTIERS IN PHYSICS (2021)

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Article Physics, Multidisciplinary