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We provide a quantum algorithm for simulating the dynamics of sparse Hamiltonians with complexity sublogarithmic in the inverse error, an exponential improvement over previous methods. Specifically, we show that a $d$-sparse Hamiltonian $H$…

Quantum Physics · Physics 2014-10-09 Dominic W. Berry , Andrew M. Childs , Richard Cleve , Robin Kothari , Rolando D. Somma

We describe a data structure that supports access, rank and select queries, as well as symbol insertions and deletions, on a string $S[1,n]$ over alphabet $[1..\sigma]$ in time $O(\lg n/\lg\lg n)$, which is optimal even on binary sequences…

Data Structures and Algorithms · Computer Science 2013-02-04 Gonzalo Navarro , Yakov Nekrich

In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint $k$ whose approximation ratio is arbitrarily close to $1-1/e$, is…

Machine Learning · Computer Science 2019-07-16 Adam Breuer , Eric Balkanski , Yaron Singer

Combinatorial optimization problems are computationally hard in general, but they are ubiquitous in our modern life. A coherent Ising machine (CIM) based on a multiple-pulse degenerate optical parametric oscillator (DOPO) is an alternative…

Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is therefore…

Optimization and Control · Mathematics 2019-08-06 Mathieu Granzotto , Romain Postoyan , Lucian Buşoniu , Dragan Nešić , Jamal Daafouz

We describe the first nearly linear-time approximation algorithms for explicitly given mixed packing/covering linear programs, and for (non-metric) fractional facility location. We also describe the first parallel algorithms requiring only…

Data Structures and Algorithms · Computer Science 2014-11-06 Neal E. Young

We give a quantum approximation scheme (i.e., $(1 + \varepsilon)$-approximation for every $\varepsilon > 0$) for the classical $k$-means clustering problem in the QRAM model with a running time that has only polylogarithmic dependence on…

Quantum Physics · Physics 2025-05-27 Ragesh Jaiswal

Classical simulation of quantum operations is essential for algorithm design, noise characterisation, and benchmarking of quantum hardware. The most general physically realisable operation can be described by a positive linear map acting on…

Quantum Physics · Physics 2026-04-20 Timo Ziegler

Two recent lower bounds on the compressibility of repetitive sequences, $\delta \le \gamma$, have received much attention. It has been shown that a length-$n$ string $S$ over an alphabet of size $\sigma$ can be represented within the…

Data Structures and Algorithms · Computer Science 2023-11-10 Tomasz Kociumaka , Gonzalo Navarro , Francisco Olivares

The logarithm-determinant is an widely-present operation in many areas of physics and computer science. Derivatives of the logarithm-determinant compute physically relevant quantities in statistical physics models, quantum field theories,…

Quantum Physics · Physics 2025-09-23 Thomas E. Baker , Jaimie A. Greasley

We present a version of the sieve of Eratosthenes that can factor all integers $\le x$ in $O(x \log\log x)$ arithmetic operations using at most $O(\sqrt{x}/\log\log x)$ bits of space. This is an improved space bound under the condition that…

Data Structures and Algorithms · Computer Science 2024-11-13 Samuel Hartman , Jonathan P. Sorenson

We give new sublinear and parallel algorithms for the extensively studied problem of approximating n-variable r-CSPs (constraint satisfaction problems with constraints of arity r up to an additive error. The running time of our algorithms…

Data Structures and Algorithms · Computer Science 2014-07-31 Grigory Yaroslavtsev

We present randomized distributed algorithms for the maximal independent set problem (MIS) that, while keeping the time complexity nearly matching the best known, reduce the energy complexity substantially. These algorithms work in the…

Data Structures and Algorithms · Computer Science 2023-05-24 Mohsen Ghaffari , Julian Portmann

The difficulty of simulating quantum dynamics depends on the norm of the Hamiltonian. When the Hamiltonian varies with time, the simulation complexity should only depend on this quantity instantaneously. We develop quantum simulation…

Quantum Physics · Physics 2020-04-21 Dominic W. Berry , Andrew M. Childs , Yuan Su , Xin Wang , Nathan Wiebe

We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions…

Machine Learning · Computer Science 2010-10-22 Kenneth L. Clarkson , Elad Hazan , David P. Woodruff

We present a practical algorithm based on symplectic splitting methods to integrate numerically in time the Schr\"odinger equation. When discretized in space, the Schr\"odinger equation can be recast as a classical Hamiltonian system…

Numerical Analysis · Mathematics 2015-02-24 S. Blanes , F. Casas , A. Murua

We present a quantum algorithm for the simulation of molecular systems that is asymptotically more efficient than all previous algorithms in the literature in terms of the main problem parameters. As in previous work [Babbush et al., New…

The subject of this paper is the time complexity of approximating Knapsack, Subset Sum, Partition, and some other related problems. The main result is an $\widetilde{O}(n+1/\varepsilon^{5/3})$ time randomized FPTAS for Partition, which is…

Data Structures and Algorithms · Computer Science 2019-05-07 Marcin Mucha , Karol Węgrzycki , Michał Włodarczyk

Approximating Subset Sum is a classic and fundamental problem in computer science and mathematical optimization. The state-of-the-art approximation scheme for Subset Sum computes a $(1-\varepsilon)$-approximation in time…

Data Structures and Algorithms · Computer Science 2020-10-28 Karl Bringmann , Vasileios Nakos

We present efficient parallel algorithms for computing maximal matchings in hypergraphs. Our algorithm finds locally maximal edges in the hypergraph and adds them in parallel to the matching. In the CRCW PRAM models our algorithms achieve…

Data Structures and Algorithms · Computer Science 2026-03-13 Henrik Reinstädtler , Christian Schulz , Nodari Sitchinava , Fabian Walliser
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