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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.
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 ...
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."
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.
A decision problem is a member of BQP if there exists a quantum algorithm (an algorithm that runs on a quantum computer) that solves the decision problem with high probability and is guaranteed to run in polynomial time. A run of the algorithm will correctly solve the decision problem with a probability of at least 2/3.
Quantum approximate optimization algorithm (QAOA) can be employed to solve Knapsack problem using quantum computation by minimizing the Hamiltonian of the problem. The Knapsack Hamiltonian is constructed via embedding the constraint condition to the cost function of the problem with a penalty term. [ 29 ]
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 ]