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

Any quantum algorithm should be bench­marked against wel-known classi­cal solvers, such as the proprie­tary Gurobi Suite compri­sing vast experi­ence and honed algorithms for combi­na­to­rial optimiza­tion. Used algorithm classes can include simula­ted anneal­ing, simplex (style) algorithms, branch and bound metho­do­logy. Moreo­ver, it is likely that to become truly effici­ent, quantum qlgorithms cannot be used brute-force and stand-alone, but must rather profit from the decade long experi­ence of proven classi­cal algorithms, by either being embedded in them or draw inspi­ra­tion from them.

Topics in the Subfield Quantum Optimiza­tion:

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Quantum anneal­ing

Many combi­na­to­rial optimiza­tion problems, such as the trave­ling sales­per­son problem or planning and schedu­ling tasks, can be…

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…

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…