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For combinatorial optimization, the quantum approximate optimization algorithm (QAOA) [6] briefly had a better approximation ratio than any known polynomial time classical algorithm (for a certain problem), [7] until a more effective classical algorithm was proposed. [8] The relative speed-up of the quantum algorithm is an open research question.
In quantum computing, the variational quantum eigensolver (VQE) is a quantum algorithm for quantum chemistry, quantum simulations and optimization problems.It is a hybrid algorithm that uses both classical computers and quantum computers to find the ground state of a given physical system.
The quantum approximate optimization algorithm takes inspiration from quantum annealing, performing a discretized approximation of quantum annealing using a quantum circuit. It can be used to solve problems in graph theory. [50] The algorithm makes use of classical optimization of quantum operations to maximize an "objective function."
D-Wave's quantum annealing technology could prove valuable for optimization problems even before universal quantum computers arrive, giving them a potential head start in commercial applications ...
Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima; such as finding [1] the ground state of a spin glass or solving the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as ...
If all off-diagonal elements of are non-positive, the corresponding QUBO problem is solvable in polynomial time. [8] QUBO can be solved using integer linear programming solvers like CPLEX or Gurobi Optimizer. This is possible since QUBO can be reformulated as a linear constrained binary optimization problem.
IBM researchers used a 127-qubit quantum computer in 2023 to accurately solve a certain type of problem, albeit one without any real-world applications, faster than a traditional computer.
A quantum algorithm for solving this problem exists. This algorithm is, like the factor-finding algorithm, due to Peter Shor and both are implemented by creating a superposition through using Hadamard gates, followed by implementing f {\displaystyle f} as a quantum transform, followed finally by a quantum Fourier transform. [ 3 ]