Quantum computers are designed to outperform standard computers by running quantum algorithms. Areas in which quantum algorithms can be applied include cryptography, search and optimisation, simulation of quantum systems and solving large systems of line
The structure of this paper is as follows: in Section 2, we provide a basic background on financial problems, common algorithms in quantitative finance, and quantum computing. In Section 3, we examine applications of quantum optimization to finance. In Section 4, we introduce quantum machine lea...
Finally, variational quantum algorithms with a relatively small number of variational parameters may also be accelerated with this new feature. To activate intermediate tensor reuse for constant input tensors, users need to: Mark the constant input tensors when constructing the network (cutensornet...
4 Prospects of other quantum optimization algorithms for DOPs As shown in section 3, many quantum optimization algorithms in Fig. 3 have been applied to DOPs. Yet, there are still few quantum learning algorithms applied to DOPs. Quantum learning algorithms can realize anthropomorphic decision-making...
Overview¶ cuQuantum Python aims to bring the full functionalities of NVIDIA cuQuantum SDK to Python. To do so, we adopt a two-layer approach: Provide 1:1 Python wrappers of the corresponding C APIs in cuQuantum, including both cuStateVec and cuTensorNet. ...
Examples of problems that can use this approach are Variational Quantum Eigensolvers (VQE) and Quantum Approximate Optimization Algorithms (QAOA). Integrated quantum computing With integrated quantum computing, the classical and quantum architectures are tightly coupled, allowing classical computations to be...
Performance & Reliability Game Ready and Studio Drivers Built for Live Streaming NVIDIA Encoder AI-enhanced Voice & Video NVIDIA Broadcast App Fast-Track Your Creativity NVIDIA Studio RTX. It’s On. The Ultimate in Ray Tracing and AI NVIDIA RTX™ is the most advanced platform for ray tracing...
Fang W, Sun J, Chen H, Wu X (2016) A decentralized quantum-inspired particle swarm optimization algorithm with cellular structured population. Inf Sci 330:19–48 ArticleGoogle Scholar Fernandez-Martinez JL, Garcia-Gonzalo E (2011) Stochastic stability analysis of the linear continuous and discrete...
Swarm intelligence and PSO algorithmsThe present work interprets on Particle Swarm Optimization and simple software agents so called particles, move in the explore breathing space of an optimization problem. The position of a particle represents a solution to the optimization problem at hand. Each ...
Of course,quantum programmingtheory is built based onquantum mechanics. So,Section 2.1introduces theHilbert spaceformalism of quantum mechanics, which is exactly the mathematicalknowledge baseof this book. • Quantum circuitsare introduced inSection 2.2. Historically, several majorquantum algorithmsappeared...