Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Quantum algorithm”

A quantum–quantum Metropolis algorithm

Man‐Hong Yung, Alán Aspuru‐Guzik · Proceedings of the National Academy of Sciences · 2012

The classical Metropolis sampling method is a cornerstone of many statistical modeling applications that range from physics, chemistry, and biology to economics. This method is particularly suitable for sampling the thermal distributions of classical systems. The challenge of extending this method to the simulation of arbitrary quantum systems is that, in general, eigenstates of quantum Hamiltonians cannot be obtained efficiently with a classical computer. However, this challenge can be overcome by quantum computers. Here, we present a quantum algorithm which fully generalizes the classical Metropolis algorithm to the quantum domain. The meaning of quantum generalization is twofold: The proposed algorithm is not only applicable to both classical and quantum systems, but also offers a quantum speedup relative to the classical counterpart. Furthermore, unlike the classical method of quantum Monte Carlo, this quantum algorithm does not suffer from the negative-sign problem associated with fermionic systems. Applications of this algorithm include the study of low-temperature properties of quantum systems, such as the Hubbard model, and preparing the thermal states of sizable molecules to simulate, for example, chemical reactions at an arbitrary temperature.

Cited by 158Open access

Quantum Algorithm Uncertainty Principles 215 QUANTUM ALGORITHM UNCERTAINTY PRINCIPLES

Ken Loo · 2004

Abstract. Previously, Bennet and Feynman asked if Heisenberg’s uncertainty principle puts a limitation on a quantum computer (Quantum Mechanical Computers, Richard P. Feynman, Foundations of Physics, Vol. 16, No. 6, p597-531, 1986). Feynman’s answer was negative. In this paper, we will revisit the same question for the discrete time Fourier transform uncertainty principle. We will show that the discrete time Fourier transform uncertainty principle plays a fundamental role in showing that Shor’s type of quantum algorithms has efficient running time and conclude that the discrete time uncertainty principle is an aid in our current formulation and understanding of Shor’s type of quantum algorithms. It turns out that for these algorithms, the probability of measuring an element in some set T (at the end of the algorithm) can be written in terms of the time-limiting and band-limiting operators from finite Fourier analysis. Associated with these operators is the finite Fourier transform uncertainty principle. The uncertainty principle provides a lower bound for the above probability. We will derive lower bounds for these types of probabilities in general. We will call these lower bounds quantum algorithm uncertainty principles or QAUP. QAUP are important because they give us some sense of the probability of measuring something desirable. We will use these lower bounds to

Cited by 0

Quantum algorithm complexity

Vasilijs Kravcevs · E-resource repository of the University of Latvia (University of Latvia) · 2015

Cited by 1Open access

Quantum search algorithm

Lukáš Mejdrech · Digital Repository (National Repository of Grey Literature) · 2007

In this work we study Grover's algorithm for quantum computers. This algorithm promises to search in an unstructured database in time comparable with a square root of the number of objects. A description of basic quantum computer principles is followed by the original Grover's formulation and a proof of the algorithm, as well as a later geometrical description of the algorithm. We also mention their suitability for educational purposes. We also include a description of the Grover's algorithm in a programming language, which makes it possible to simulate a quantum computing on classical computers.

Cited by 0

Quantum Algorithm Developer

Deven Nandapurkar · Zenodo (CERN European Organization for Nuclear Research) · 2025

With applications in medicine, finance, cybersecurity, and AI, this role stands among the most important jobs of the future.

Cited by 0Open access

Universal Quantum Algorithm

Avatar Tulsi · arXiv (Cornell University) · 2016

Quantum amplitude amplification and quantum phase estimation are two fundamental quantum algorithms. All known quantum algorithms are derived from these two algorithms. Even the adiabatic quantum algorithms can also be efficiently simulated using quantum phase estimation. We present a universal quantum algorithm which explains these two algorithms as special cases. An interesting result is that we do not need quantum fourier transform to do quantum phase estimation.

Cited by 1Open access

Quantum Relief algorithm

Wen-Jie Liu, Pei-Pei Gao, Wen-Bin Yu et al. · Quantum Information Processing · 2018

Cited by 29Open access

Quantum Algorithm Developer

Deven Nandapurkar · Zenodo (CERN European Organization for Nuclear Research) · 2025

With applications in medicine, finance, cybersecurity, and AI, this role stands among the most important jobs of the future.

Cited by 0Open access

Quantum Defragmentation Algorithm

Burgarth, Daniel, Giovannetti, Vittorio · arXiv (Cornell University) · 2010

In this addendum of our paper [D. Burgarth and V. Giovannetti, Phys. Rev. Lett. 99, 100501 (2007)] we prove that during the transformation that allows one to enforce control by relaxation on a quantum system, the ancillary memory can be kept at a finite size, independently from the fidelity one wants to achieve. The result is obtained by introducing the quantum analog of defragmentation algorithms which are employed for efficiently reorganizing classical information in conventional hard-disks. Our result also implies that the reduced dynamics in any noisy system can be simulated with finitely many resources.

Cited by 0Open access

Quantum Algorithm III

Masanori Ohya, И. В. Волович · Theoretical and mathematical physics · 2011

Cited by 1

Quantum Computing Algorithm

Waqar Hassan, Mujahid Tabassum, Muhammad Nawaz · Information systems engineering and management · 2025

Cited by 0

Quantum Google algorithm

Giuseppe Davide Paparo, Markus Müller, Francesc Comellas et al. · The European Physical Journal Plus · 2014

Cited by 16Open access

Stego Quantum Algorithm

Gabriela Mogos · 2008

Steganography is the art and science of hiding the fact that communication is taking place. Steganographic systems can hide messages inside of images or other digital objects. To a casual observer inspecting these images, the messages are invisible. The security of a classical steganographic system relies on the secrecy of the encoding system. Once the encoding system is known, the steganographic system is defeated. However, modern steganography should be detectable only if secret information is known, namely, asecret key. Because of their invasive nature, steganographic systems leave detectable traces within a medium's characteristics. This allows an eavesdropper to detect modified media, revealing that secret communication is taking place. Although the secret content is not exposed, its existence is revealed, which defeats the mainpurpose of steganography. This work has as purpose to expand the field of applicability of the steganography from the classical informatics to the quantum one.

Cited by 17

Quantum computing quantum Monte Carlo algorithm

Yukun Zhang, Yifei Huang, Jinzhao Sun et al. · arXiv (Cornell University) · 2022

Quantum computing and quantum Monte Carlo (QMC) are respectively the state-of-the-art quantum and classical computing methods for understanding many-body quantum systems. Here, we propose a hybrid quantum-classical algorithm that integrates these two methods, inheriting their distinct features in efficient representation and manipulation of quantum states and overcoming their limitations. We first introduce non-stoquasticity indicators (NSIs) and their upper bounds, which measure the sign problem, the most notable limitation of QMC. We show that our algorithm could greatly mitigate the sign problem, which decreases NSIs with the assistance of quantum computing. Meanwhile, the use of quantum Monte Carlo also increases the expressivity of shallow quantum circuits, allowing more accurate computation that is conventionally achievable only with much deeper circuits. We numerically test and verify the method for the N$_2$ molecule (12 qubits) and the Hubbard model (16 qubits). Our work paves the way to solving practical problems with intermediate-scale and early-fault tolerant quantum computers, with potential applications in chemistry, condensed matter physics, materials, high energy physics, etc.

Cited by 10Open access