Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best solution to a problem (according to some criteria) from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize an error which depends on the solution: the optimal solution has the minimal error. Different optimization techniques are applied in various fields such as mechanics, economics and engineering, and as the complexity and amount of data involved rise, more efficient ways of solving optimization problems are needed. The power of quantum computing may allow problems which are not practically feasible on classical computers to be solved, or suggest a considerable speed up with respect to the best known classical algorithm. (Wikipedia).
Artificial General Intelligence | Tim Ferriss & Eric Schmidt | GEONOW
✅ Subscribe: https://bit.ly/3slupxs Quantum AI is the use of quantum computing for computation of machine learning algorithms. Thanks to computational advantages of quantum computing, quantum AI can help achieve results that are not possible to achieve with classical computers. Quantum da
From playlist ML & Deep Learning
Andrew Childs - Efficient quantum algorithm for dissipative nonlinear differential equations
Recorded 24 January 2022. Andrew Childs of the University of Maryland presents "Efficient quantum algorithm for dissipative nonlinear differential equations" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: While there has been extensive previous work on efficient quantum alg
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
Quantized Energy Equation (Quantum Physics)
#Quantum #Physics #Engineering #tiktok #NicholasGKK #shorts
From playlist Quantum Mechanics
Linear algebra for Quantum Mechanics
Linear algebra is the branch of mathematics concerning linear equations such as. linear functions and their representations in vector spaces and through matrices. In this video you will learn about #linear #algebra that is used frequently in quantum #mechanics or #quantum #physics. ****
From playlist Quantum Physics
Results and open problems in theory of quantum complexity - Anindya De
Andris Ambainis University of Latvia; Member, School of Mathematics April 22, 2014 I will survey recent results and open problems in several areas of quantum complexity theory, with emphasis on open problems which can be phrased in terms of classical complexity theory or mathematics but ha
From playlist Mathematics
Andras Gilyen - Quantum Algorithms for Quantum Information Processing Tasks - IPAM at UCLA
Recorded 24 January 2022. Andras Gilyen of the Renyi Institute of Mathematics presents "Quantum Algorithms for Quantum Information Processing Tasks" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: Quantum linear algebra methods, in particular block-encoding and quantum singu
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
How do quantum computers work?
Quantum computers are said to have the potential to offer computing power far larger than what we have today. Are they really these miracles of quantum computing or are they just over-hyped? You can have brief information in our video on how quantum computing works related to superposi
From playlist Radical Innovations
Quantum Computers: How They Work and What Can They Do?
We are beginning to evolve beyond classical computing into a new data era called quantum computing. The quantum computing power and speed will help us solve some of the biggest and most complex challenges we face.
From playlist Quantum Computing
Are Quantum Computers Really A Threat To Cryptography?
Shor's Algorithm for factoring integer numbers is the big threat to cryptography (RSA/ECC) as it reduces the complexity from exponential to polynomial, which means a Quantum Computer can reduce the time to crack RSA-2048 to a mere 10 seconds. However current noisy NISQ type quantum compute
From playlist Blockchain
Alexandra Kolla - Quantum Approximate Optimization Algorithm (QAOA) and Local Max-Cut - IPAM at UCLA
Recorded 27 January 2022. Alexandra Kolla of the University of California, Santa Cruz, presents "Quantum Approximate Optimization Algorithm (QAOA) and Local Max-Cut" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: We will discuss methods to determine how good of an approxima
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
Yulong Dong - Fast algorithms for quantum signal processing - IPAM at UCLA
Recorded 24 January 2022. Yulong Dong of the University of California, Berkeley, presents "Fast algorithms for quantum signal processing" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: The recently developed quantum singular value transformation (QSVT) [Gilyen, Su, Low, Wie
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
AQC 2016 - Quantum Monte Carlo vs Tunneling vs. Adiabatic Optimization
A Google TechTalk, June 27, 2016, presented by Aram Harrow (MIT) ABSTRACT: Can quantum adiabatic evolution solve optimization problems much faster than classical computers? One piece of evidence for this has been their apparent advantage in "tunneling" through barriers to escape local mi
From playlist Adiabatic Quantum Computing Conference 2016
AQC - 2016 Quantum vs. Classical Optimization - A Status Report on the Arms Race
A Google TechTalk, June 27, 2016, presented by Helmut Katzgraber (Texas A&M) ABSTRACT: To date, a conclusive detection of quantum speedup remains elusive. However, recent results from quantum Monte Carlo simulations, as well as the D-Wave 2X quantum annealer show a scaling that clearly o
From playlist Adiabatic Quantum Computing Conference 2016
AQC 2016 - What is the Computational Value of Finite Range Tunneling?
A Google TechTalk, June 27, 2016, presented by Vasil Denchev (Google) ABSTRACT: Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For
From playlist Adiabatic Quantum Computing Conference 2016
Dominic Berry - Optimal scaling quantum linear systems solver via discrete adiabatic theorem
Recorded 25 January 2022. Dominic Berry of Macquarie University presents "Optimal scaling quantum linear systems solver via discrete adiabatic theorem" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: Recently, several approaches to solving linear systems on a quantum compute
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
David Mazziotti - Contracted Quantum Eigensolver for the Quantum Simulation of Many-electron Systems
Recorded 05 May 2022. David Mazziotti of the University of Chicago, Chemistry, presents "Contracted Quantum Eigensolver for the Quantum Simulation of Many-electron Systems" at IPAM's Large-Scale Certified Numerical Methods in Quantum Mechanics Workshop. Abstract: We will introduce a novel
From playlist 2022 Large-Scale Certified Numerical Methods in Quantum Mechanics
Understanding quantum algorithms via query complexity – Andris Ambainis – ICM2018
Mathematical Aspects of Computer Science Invited Lecture 14.2 Understanding quantum algorithms via query complexity Andris Ambainis Abstract: Query complexity is a model of computation in which we have to compute a function f(x_1, …, x_N) of variables x_i which can be accessed via querie
From playlist Mathematical Aspects of Computer Science
Closing Keynote: Quantum Computing: Reality vs. Hype - John Preskill - 6/27/2019
AstroInformatics 2019 Conference: Methodology Transfer, Quantum Computing, and Looking Ahead http://astroinformatics2019.org/
From playlist AstroInformatics 2019 Conference
Stanford Seminar - Highly optimized quantum circuits synthesized via data-flow engines
Peter Rakyta, Department of Physics of Complex Systems at Eötvös Loránd University November 9, 2022 The formulation of quantum programs in terms of the fewest number of gate operations is crucial to retrieve meaningful results from the noisy quantum processors accessible these days. In th
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series