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Open AccessJournal ArticleDOI

Prospects for Quantum Enhancement with Diabatic Quantum Annealing

TLDR
Diabatic quantum annealing is argued for as the most promising route to quantum enhancement within this framework on the basis that improved coherence times and control capabilities will enable the near-term exploration of several heuristic quantum optimization algorithms that have been introduced in the literature.
Abstract
We assess the prospects for algorithms within the general framework of quantum annealing (QA) to achieve a quantum speedup relative to classical state of the art methods in combinatorial optimization and related sampling tasks. We argue for continued exploration and interest in the QA framework on the basis that improved coherence times and control capabilities will enable the near-term exploration of several heuristic quantum optimization algorithms that have been introduced in the literature. These continuous-time Hamiltonian computation algorithms rely on control protocols that are more advanced than those in traditional ground-state QA, while still being considerably simpler than those used in gate-model implementations. The inclusion of coherent diabatic transitions to excited states results in a generalization called diabatic quantum annealing (DQA), which we argue for as the most promising route to quantum enhancement within this framework. Other promising variants of traditional QA include reverse annealing and continuous-time quantum walks, as well as analog analogues of parameterized quantum circuit ansatzes for machine learning. Most of these algorithms have no known (or likely to be discovered) efficient classical simulations, and in many cases have promising (but limited) early signs for the possibility of quantum speedups, making them worthy of further investigation with quantum hardware in the intermediate-scale regime. We argue that all of these protocols can be explored in a state-of-the-art manner by embracing the full range of novel out-of-equilibrium quantum dynamics generated by time-dependent effective transverse-field Ising Hamiltonians that can be natively implemented by, e.g., inductively-coupled flux qubits, both existing and projected at application scale.

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Foundations of Computer Science

TL;DR: A machine equivalent to Turing machine, that is more intuitive in its working is defined and his incompleteness theorems are proved without using any metalanguage.
Journal ArticleDOI

Ising machines as hardware solvers of combinatorial optimization problems

TL;DR: Ising machines as discussed by the authors are special-purpose hardware solvers that aim to find the absolute or approximate ground states of the Ising model, which is of fundamental computational interest because any problem in the complexity class NP can be formulated as an Ising problem with only polynomial overhead and thus a scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications.
Proceedings Article

Proceedings of the thirty-sixth annual ACM symposium on Theory of computing

TL;DR: The papers in this volume were presented at the Thirty-Sixth Annual ACM Symposium on Theory of Computing (STOC 2004), held in Chicago, Illinois, June 13-15, 2004, and included three invited plenary talks.
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Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal ArticleDOI

Simulating physics with computers

TL;DR: In this paper, the authors describe the possibility of simulating physics in the classical approximation, a thing which is usually described by local differential equations, and the possibility that there is to be an exact simulation, that the computer will do exactly the same as nature.
Journal ArticleDOI

Supplementary information for "Quantum supremacy using a programmable superconducting processor"

TL;DR: In this paper, an updated version of supplementary information to accompany "Quantum supremacy using a programmable superconducting processor", an article published in the October 24, 2019 issue of Nature, is presented.
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