Quantum Metropolis Sampling: Monte Carlo, Quantised
Quantum Metropolis sampling adapts the Metropolis-Hastings Monte Carlo method to quantum systems. Its theory, relation to annealing and potential uses.
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739 articlesQuantum Metropolis sampling adapts the Metropolis-Hastings Monte Carlo method to quantum systems. Its theory, relation to annealing and potential uses.
Quantum simulation algorithms model molecules and materials that classical computers struggle with. Uses in chemistry, drug discovery and materials science.
Amplitude amplification generalizes Grover’s search: it raises the probability of target states step by step. How the iteration works and where it is used.
Simon’s algorithm finds a hidden period of a two-to-one function exponentially faster than classical methods, and it directly inspired Shor’s algorithm.
The Deutsch-Jozsa algorithm decides whether a black-box function is constant or balanced in one query. Its circuit, steps, complexity and limits.
The HHL algorithm by Harrow, Hassidim and Lloyd solves linear systems on a quantum computer. Its circuit, state preparation, caveats and applications.
The quantum counting algorithm combines Grover’s search with phase estimation to count solutions. How it works, its speedup and its limitations.
Quantum walks are the quantum version of random walks. How they spread faster than classical walks, run on graphs and feed into algorithm design.
How quantum annealing uses tunneling and a slowly changing transverse field to find low-energy solutions, how D-Wave machines work and where they help.
Quantum teleportation transfers a quantum state, not matter, and needs a classical signal, so nothing travels faster than light. How it works and is used.
Quantum error correction codes and methods: error sources, syndrome measurement, Shor and Steane codes, topological codes and fault tolerance.
How the variational quantum eigensolver finds ground-state energies: ansatz design, energy measurement, parameter optimization and chemistry uses.