Quantum Speedup for the Quadratic Assignment Problem (2410.12181v1)

Abstract: We demonstrate that the search space of the quadratic assignment problem (QAP), known as an NP-hard combinatorial optimization problem, can be reduced using Grover adaptive search (GAS) with Dicke state operators. To that end, we first revise the traditional quadratic formulation of the QAP into a higher-order formulation, introducing a binary encoding method ordered by a descending Hamming weight, such that the number of terms in the objective function is reduced. We also show that the phase gate in the GAS can be replaced by a rotation gate about the Z axis, simplifying the quantum circuit without any penalty. Algebraic analyses in terms of the number of qubits, quantum gates, and query complexity are performed, which indicate that our proposed approach significantly reduces the search space size, improving convergence performance to the optimal solution compared to the conventional one. Interestingly, it is suggested that the higher-order formulation is effective for problems whose size are powers of two, while the quadratic formulation is more effective for other sizes, indicating that switching between the two formulations can enhance the feasibility of the GAS-solved QAP.