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Entanglement in a quantum annealing processor

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TLDR
In this paper, a series of scalable quantum annealing (QA) processors consisting of networks of manufactured interacting spins (qubits) were built and the authors used qubit tunneling spectroscopy to measure the energy eigenspectrum of two and eight qubit systems.
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 scalable QA processors consisting of networks of manufactured interacting spins (qubits). Here, we use qubit tunneling spectroscopy to measure the energy eigenspectrum 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 that 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.

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Journal ArticleDOI

Quantum information processing with superconducting circuits: a review

TL;DR: The time is ripe for describing some of the recent development of superconducting devices, systems and applications as well as practical applications of QIP, such as computation and simulation in Physics and Chemistry.
Journal ArticleDOI

Microwave photonics with superconducting quantum circuits

TL;DR: In the past 20 years, impressive progress has been made both experimentally and theoretically in superconducting quantum circuits, which provide a platform for manipulating microwave photons as mentioned in this paper, and many higher-order effects, unusual and less familiar in traditional cavity quantum electrodynamics with natural atoms, have been experimentally observed.
Book

Quantum Machine Learning: What Quantum Computing Means to Data Mining

Peter Wittek
TL;DR: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning by paring down the complexity of the disciplines involved.
Journal ArticleDOI

Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models

TL;DR: It is shown how many different types of constraining relationships arising in practice can be embodied within the "unconstrained" QUBO formulation in a very natural manner using penalty functions, yielding exact model representations in contrast to the approximate representations produced by customary uses of penalty functions.
References
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Journal ArticleDOI

Entanglement of Formation of an Arbitrary State of Two Qubits

TL;DR: In this article, an explicit formula for the entanglement of formation of a pair of binary quantum objects (qubits) as a function of their density matrix was conjectured.
Journal ArticleDOI

Computable measure of entanglement

TL;DR: A measure of entanglement that can be computed effectively for any mixed state of an arbitrary bipartite system is presented and it is shown that it does not increase under local manipulations of the system.
Journal ArticleDOI

Entanglement in many-body systems

TL;DR: In this article, the properties of entanglement in many-body systems are reviewed and both bipartite and multipartite entanglements are considered, and the zero and finite temperature properties of entangled states in interacting spin, fermion and boson model systems are discussed.
Journal ArticleDOI

Surface codes: Towards practical large-scale quantum computation

TL;DR: The concept of the stabilizer, using two qubits, is introduced, and the single-qubit Hadamard, S and T operators are described, completing the set of required gates for a universal quantum computer.
Journal ArticleDOI

Bell’s theorem without inequalities

TL;DR: In this article, it was shown that the premisses of the Einstein-Podolsky-Rosen paper are inconsistent when applied to quantum systems consisting of at least three particles.
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