Quantum Random Walks: Discrete and Continuous
Discrete and continuous quantum random walks compared with classical walks, and their uses in algorithms, cryptography and models of biology.
Discrete and continuous quantum random walks compared with classical walks, and their uses in algorithms, cryptography and models of biology.
Which quantum chemistry algorithms run on today’s hardware, what they compute, and how they may help drug discovery and materials science.
Boson sampling sends photons through a network of beam splitters. Why its output is hard to simulate classically and what the experiments have shown.
The quantum adiabatic algorithm slowly evolves a simple Hamiltonian into one encoding a problem. The adiabatic theorem, annealing and hardware limits.
Quantum simulation algorithms model molecules and materials that classical computers struggle with. Uses in chemistry, drug discovery and materials science.
What a qubit is, how it differs from a bit, the types of physical qubits, how they are measured and why scaling them up is hard.
What quantum software does between the user and the hardware, how quantum and classical processing compare, and which industries use it.
What quantum supremacy experiments actually proved, what they did not, and how supremacy differs from useful quantum advantage.
Quantum yield is the fraction of absorbed photons that produce a given effect. How it is measured and why it matters in fluorescence, LEDs and solar cells.
How quantum computing may help biology: simulating biomolecules, drug discovery, genetic analysis and bioinformatics, plus a look at quantum biology.
Uses of quantum computing being tested in medicine: drug discovery, genomics, disease prediction, diagnostic sensors and medical data security.
How quantum computing could affect drug discovery, materials, encryption, supply chains, climate modeling and AI, and which effects are near-term.