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Related papers: A Factorisation Algorithm in Adiabatic Quantum Com…

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Let $f(x)\in \mathbb{F}_q[x]$ be an irreducible polynomial of degree $m$ and exponent $e$, and $n$ be a positive integer such that $\nu_p(q-1)\ge \nu_{p}(e)+\nu_p(n)$ for all $p$ prime divisor of $n$. We show a fast algorithm to determine…

Number Theory · Mathematics 2015-12-01 F. E. Brochero Martínez , Lucas Reis

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, the…

Data Structures and Algorithms · Computer Science 2019-07-15 Zhihuai Chen , Yinan Li , Xiaoming Sun , Pei Yuan , Jialin Zhang

We construct a set of instances of 3SAT which are not solved efficiently using the simplest quantum adiabatic algorithm. These instances are obtained by picking random clauses all consistent with two disparate planted solutions and then…

Quantum Physics · Physics 2012-03-30 Edward Farhi , Jeffrey Goldstone , David Gosset , Sam Gutmann , Harvey B. Meyer , Peter Shor

Hittmeir recently presented a deterministic algorithm that provably computes the prime factorisation of a positive integer $N$ in $N^{2/9+o(1)}$ bit operations. Prior to this breakthrough, the best known complexity bound for this problem…

Number Theory · Mathematics 2020-10-13 David Harvey

This paper proposes new factorizations for computing the Neumann series. The factorizations are based on fast algorithms for small prime sizes series and the splitting of large sizes into several smaller ones. We propose a different basis…

Numerical Analysis · Computer Science 2017-07-20 Vassil Dimitrov , Diego Coelho

We review the proposal of a quantum algorithm for Hilbert's tenth problem and provide further arguments towards the proof that: (i) the algorithm terminates after a finite time for any input of Diophantine equation; (ii) the final ground…

Quantum Physics · Physics 2007-05-23 Tien D. Kieu

Tensor factorization with hard and/or soft constraints has played an important role in signal processing and data analysis. However, existing algorithms for constrained tensor factorization have two drawbacks: (i) they require…

Numerical Analysis · Mathematics 2024-07-01 Shunsuke Ono , Takuma Kasai

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for…

Machine Learning · Computer Science 2024-05-02 Jan-Nico Zaech , Martin Danelljan , Tolga Birdal , Luc Van Gool

The theory of fuzzy mathematics has been proven very effective for defining and solving optimization problems. Fuzzy quadratic programming (FQP) is a consequence of this approach. In this paper, an algorithm has been proposed to solve FQP…

General Mathematics · Mathematics 2022-07-25 Sajal Chakroborty

We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in [E. Farhi, et al., arXiv:quant-ph/0208135]. The algorithm is applied to a random binary optimization problem (a version…

Quantum Physics · Physics 2009-11-10 A. Boulatov , V. N. Smelyanskiy

Integer factorization has been one of the cornerstone applications of the field of quantum computing since the discovery of an efficient algorithm for factoring by Peter Shor. Unfortunately, factoring via Shor's algorithm is well beyond the…

Quantum Physics · Physics 2018-08-28 Eric R. Anschuetz , Jonathan P. Olson , Alán Aspuru-Guzik , Yudong Cao

A classic question in analytic number theory is to find asymptotics for $\sigma_{k}(x)$ and $\pi_{k}(x)$, the number of integers $n\leq x$ with exactly $k$ prime factors, where $\pi_{k}(x)$ has the added constraint that all the factors are…

Number Theory · Mathematics 2023-03-13 Eric Naslund

Adiabatic quantum computation provides an alternative approach to quantum computation using a time-dependent Hamiltonian. The time evolution of entanglement during the adiabatic quantum search algorithm is studied, and its relevance as a…

Quantum Physics · Physics 2009-11-11 Daria Ahrensmeier

Quantum annealing is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with an easy-to-prepare…

An algorithm is given to factor an integer with $N$ digits in $\ln^m N$ steps, with $m$ approximately 4 or 5. Textbook quadratic sieve methods are exponentially slower. An improvement with the aid of an a particular function would provide a…

General Physics · Physics 2007-05-23 Gordon Chalmers

We discuss in this chapter the basics of adiabatic computation, as well as some physical implementations. After a short introduction of the quantum circuit model, we describe quantum adiabatic computation, quantum annealing, and the strong…

Quantum Physics · Physics 2017-11-27 Boaz Tamir , Eliahu Cohen

The arithmetic problem of factoring an integer $N$ can be translated into the physics of a quantum device, a result that supports P\'olya's and Hilbert's conjecture to prove Riemann's hypothesis. The energies of this system, being…

Quantum Physics · Physics 2018-03-28 Jose Luis Rosales , Vicente Martin

Adiabatic quantum computing has evolved in recent years from a theoretical field into an immensely practical area, a change partially sparked by D-Wave System's quantum annealing hardware. These multimillion-dollar quantum annealers offer…

Quantum Physics · Physics 2017-07-28 Timothy D. Goodrich , Travis S. Humble , Blair D. Sullivan

We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size $n$. We use as an example the…

Quantum Physics · Physics 2007-05-23 Vadim N. Smelyanskiy , Udo v. Toussaint , Dogan A. Timucin

We develop two analytic approaches to solve D-optimal approximate designs under generalized linear models. The first approach provides analytic D-optimal allocations for generalized linear models with two factors, which include as a special…

Computation · Statistics 2013-10-14 Liping Tong , Hans W. Volkmer , Jie Yang