Entanglement in a Quantum Annealing Processor

By T. Lanting, A. Przybysz, A. Smirnov, F. Spedalieri, Mohammad H. Amin, A. Berkley, R. Harris, F. Altomare, Sergio Boixo, P. Bunyk and 16 more. 2014

328 citations recorded by the source corpus.

Abstract : Entanglement lies at the core of quantum algorithms designed to solve problems that are intractable by classical approaches. One such algorithm, quantum annealing (QA), provides a promising path to a practical quantum processor. We have built a series of architecturally scalable QA processors consisting of networks of manufactured interacting spins (qubits). Here, we use qubit tunneling spectroscopy to measure the energy eigen spectrum of two- and eight-qubit systems within one such processor, demonstrating quantum coherence in these systems. We present experimental evidence that, during a critical portion of QA, the qubits become entangled and entanglement persists even as these systems reach equilibrium with a thermal environment. Our results provide an encouraging sign that QA is a viable technology for large scale quantum computing.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/8b11f7020feff694f18680427482ffcb723bc295

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Citations in the corpus

Superconductor digital electronics: Scalability and energy efficiency issues (Review Article) (2016, methodology): “There is no published description of the process, but some information can be found in [31]-[34].”
Quantum information processing with superconducting circuits: a review (2016, background): “As a result, the coherence and entanglement properties have to be investigated by performing various types of experi ments on the machines and their components: Physics experi ments on the hardware [123, 489], and ‘benchmarking’ of the performance by running a range of QA schemes [109, 111, 393, 4”
Quantum Boltzmann Machine (2016, background): “Up to now many experiments have confirmed the existence of quantum phenomena in such processors [15,35–38], which includes entanglement [39].”
Variational Quantum Circuits for Deep Reinforcement Learning (2019, background): “Although quantum computing machines have been brought to the market by a few technological companies (e.g., IBM’s and D-Wave’s hardware solutions [16, 17].)”