### Quantum-computing related developments

On this page we post about interesting quantum-computing related research and news which we are following.

On this page we post about interesting quantum-computing related research and news which we are following.

October 17, 2016

- Source: Arxiv

In this paper, Venturelli et al. present a quantum annealing solver for the renowned job-shop scheduling problem (JSP). They formulate the problem as a time-indexed quadratic unconstrained binary optimization problem, several pre-processing and graph embedding strategies are employed to compile optimally parametrized families of the JSP for scheduling instances of up to six jobs and six machines on the D-Wave Systems Vesuvius (DW2) processor. Problem simplifications and partitioning algorithms are discussed and the results from the processor are compared against state-of-the-art global-optimum solvers.

September 22, 2016

- Source: Arxiv

In this paper, Puri et al. propose an alternative to the typical quantum annealing architecture with a scalable network of all-to-all connected, two-photon driven Kerr-nonlinear resonators. Each of these resonators encode an Ising spin in a robust degenerate subspace formed by two coherent states of opposite phases. A fully-connected optimization problem is mapped onto local fields driving the resonators, which are themselves connected by local four-body interactions. They describe an adiabatic annealing protocol in this system and analyze its performance in the presence of photon loss. Numerical simulations indicate substantial resilience to this noise channel, making it a promising platform for implementing a large scale quantum Ising machine.

September 22, 2016

- Source: Arxiv

A recommendation engine uses the past purchases or ratings to construct a (partial) preference matrix, which is used to provide personalized recommendations to individual users. In this paper, Kerenidis et al. present a quantum recommendation system which updates the partial preference matrix each time data comes in and can, based on such matrix, provides a recommendation in time polylog to the matrix-dimension, exponentially faster than classical recommendation systems.

August 11, 2016

- Source: Arxiv

Mean-variance portfolio optimization problems are traditionally solved as continuous-variable problems. However, for assets that can only be traded in large lots, or for asset managers who are constrained to trading large blocks of assets, solving the continuous problem yields an approximation. The discrete problem, is expected to provide better results, but is non-convex due to the fragmented nature of the domain, and is therefore much harder to solve. In this paper, Rosenberg et al. attempt to solve a discrete multi-period portfolio optimisation problem using D-Wave Systems' quantum annealer. They derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. They also present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.

December 21, 2015

- Source: ArXiv

In this paper, Ashley Montanaro, gives a broad overview of quantum algorithms, focusing on algorithms with clear applications and rigorous performance bounds, and including recent progress in the field. The paper does not a detailed discussion of how the quantum algorithms mentioned work, but aims to provide structure to the different classes of quantum-algorithms, which were known in November 2015

February 23, 2013

- Source: Arxiv

In this paper, Andrew Lucas provides Ising formulations for many NP-complete and NP-hard problems, including all of Karp's 21 NP-complete problems. In each case, the required number of spins is at most cubic in the size of the problem. This work may be useful in designing adiabatic quantum optimization algorithms.

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