The Annals of Statistics, Vol. 39, No. 6 (December 2011), pp. 3262-3289 (28 pages) Adaptive and interacting Markov chain Monte Carlo algorithms (MCMC) have been recently introduced in the literature.

Cambridge Quantum Computing (CQC) says it has discovered an algorithm that accelerates quantum Monte Carlo integration.

Monte Carlo methods: Computational algorithms that utilise repeated random sampling to obtain numerical results, often used for integration, optimisation, and simulation of complex systems.

Tweet this State preparation is a necessary component of many quantum algorithms and is fundamental in expediting Monte Carlo methods, which use randomness to simulate outcomes of complex problems.

A long-standing challenge for AFQMC, as with most other projector Monte Carlo algorithms, including diffusion Monte Carlo (DMC), is the inability to obtain properties at the same level of accuracy as ...

This paper describes a practical algorithm based on Monte Carlo simulation for the pricing of multidimensional American (i.e., continuously exercisable) and Bermudan (i.e., discretely exercisable) ...

The third major pathway to achieving computational expansion during the reasoning phase is the actual adoption of some search technique during the reasoning process.This means that reasoning is no ...

Marking a significant step in the roadmap for quantum advantage for financial applications, Goldman Sachs and QC Ware researchers have designed new, robust quantum algorithms that outperform ...

A web-based tool for calculating project estimates using a Monte Carlo simulation was recently made publicly available. It was created in the hopes that agile teams will use it to facilitate ...

Goldman Sachs is claiming a quantum computing breakthrough, designing algorithms it says could be used on hardware that may be available in as little as five years.