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We motivate the use of quantum algorithms in particle physics and provide a brief overview of the most recent applications at high-energy colliders. In particular, we discuss in detail how a quantum approach reduces the complexity of jet…

High Energy Physics - Phenomenology · Physics 2024-01-30 Germán Rodrigo

This paper presents a novel quantum walk approach to simulating parton showers on a quantum computer. We demonstrate that the quantum walk paradigm offers a natural and more efficient approach to simulating parton showers on quantum…

High Energy Physics - Phenomenology · Physics 2022-09-07 Simon Williams , Sarah Malik , Michael Spannowsky , Khadeejah Bepari

The quantum information science community has seen a surge in new algorithmic developments across scientific domains. These developments have demonstrated polynomial or better improvements in computational and space complexity,…

Quantum Physics · Physics 2022-05-03 Vincent R. Pascuzzi , Ning Bao , Ang Li

A new method for combining QCD matrix elements and parton showers in Monte Carlo simulations of hadronic final states is outlined. The aim is to provide at least a leading-order description of all hard multi-jet configurations together with…

High Energy Physics - Phenomenology · Physics 2007-05-23 B. R. Webber

Computations of chemical systems' equilibrium properties and non-equilibrium dynamics have been suspected of being a "killer app" for quantum computers. This review highlights the recent advancements of quantum algorithms tackling complex…

Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in systems and control…

Systems and Control · Electrical Eng. & Systems 2025-12-23 Jan Schneider , Julian Berberich

The application of quantum computation to accelerate machine learning algorithms is one of the most promising areas of research in quantum algorithms. In this paper, we explore the power of quantum learning algorithms in solving an…

Quantum Physics · Physics 2023-04-19 Yusen Wu , Bujiao Wu , Jingbo Wang , Xiao Yuan

Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale…

Quantum Physics · Physics 2023-12-07 David Peral García , Juan Cruz-Benito , Francisco José García-Peñalvo

Accurate models for open quantum systems -- quantum states that have non-trivial interactions with their environment -- may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction…

Quantum algorithms could efficiently solve certain classically intractable problems by exploiting quantum parallelism. To date, whether the quantum entanglement is useful or not for quantum computing is still a question of debate. Here, we…

Quantum Physics · Physics 2018-01-24 He-Liang Huang , Ashutosh K. Goswami , Wan-Su Bao , Prasanta K. Panigrahi

Quantum computers hold the promise to solve certain computational task much more efficiently than classical computers. We review the recent experimental advancements towards a quantum computer with trapped ions. In particular, various…

Quantum Physics · Physics 2008-11-20 H. Haeffner , C. F. Roos , R. Blatt

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical…

In this research notebook in the four-part, quantum computation and applications, quantum computation and algorithms, quantum communication protocol, and universal quantum computation for quantum engineers, researchers, and scientists, we…

Quantum Physics · Physics 2021-01-07 Bhupesh Bishnoi

Stochastic models are highly relevant tools in science, engineering, and society. Recent work suggests emerging quantum computing technologies can substantially decrease the memory requirements for simulating stochastic models. Here we show…

Quantum Physics · Physics 2019-06-04 John Realpe-Gómez , Nathan Killoran

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT…

Quantum Physics · Physics 2015-05-27 M. Schuld , I. Sinayskiy , F. Petruccione

Monte Carlo methods play a central role in particle physics, where they are indispensable for simulating scattering processes, modeling detector responses, and performing multi-dimensional integrals. However, traditional Monte Carlo methods…

Quantum Physics · Physics 2025-10-14 Heechan Yi , Kayoung Ban , Myeonghun Park , Kyoungchul Kong

We present a survey of quantum algorithms, primarily for an intended audience of pure mathematicians. We place an emphasis on algorithms involving group theory.

Quantum Physics · Physics 2007-05-23 Michael Batty , Samuel L. Braunstein , Andrew J. Duncan , Sarah Rees

Quantum Computers offer an intriguing challenge in modern Computer Science. With the inevitable physical limitations to Moore's Law, quantum hardware provides avenues to solve grander problems faster by utilizing Quantum Mechanical…

Emerging Technologies · Computer Science 2024-07-17 Samuel T. Bieberich , Ketan C. Maheshwari , Sean R. Wilkinson , Prasanna Date , In-Saeng Suh , Rafael Ferreira da Silva

Quantifying the resources available to a quantum computer appears to be necessary to separate quantum from classical computation. Among them, entanglement, nonstabilizerness and coherence are arguably of great significance. We introduce…

We consider the problem of estimating the expected outcomes of Monte Carlo processes whose outputs are described by multidimensional random variables. We tightly characterize the quantum query complexity of this problem for various choices…

Quantum Physics · Physics 2021-07-09 Arjan Cornelissen , Sofiene Jerbi