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Quantum anneal­ing

Many combi­na­to­rial optimiza­tion problems (such as e.g. trave­ling sales­per­son or schedu­ling) can be written as an energy-like cost function over many discrete confi­gu­ra­ti­ons. Quantum anneal­ing encodes this cost function into a quantum system. Then slowly evolves the system so it relaxes towards its lowest-energy state. Such a state corre­sponds to a good or even optimal solution.

Topics in the Subfield Quantum Optimiza­tion:

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Varia­tio­nal optimiza­tion (e.g. QAOA, VQE)

A varia­tio­nal quantum algorithm (e.g. VQE or QAOA) uses a parame­ter­i­zed quantum circuit to…

Feedback based optimiza­tion (e.g. FALQON, EFT)

FALQON is a feedback based quantum optimiza­tion algorithm that serves as an alternative…

Bench­mar­king versus classi­cal optimi­zers (e.g. Gurobi, Simplex, simula­ted annealing)

Every quantum algorithm should be bench­marked against well estab­lished classi­cal solvers, such as…

Simula­ted anneal­ing (e.g. quantum inspi­red Ising machi­nes, Fujitsu Digital Annea­ler, Evo Annealer)

Simula­ted anneal­ing is a classi­cal optimiza­tion method in which combi­na­to­rial problems are formu­la­ted as…

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