Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm
This addresses the problem of efficiently solving combinatorial optimization problems like max-cut on near-term quantum computers, though it appears incremental as it builds on existing quantum optimization methods.
The authors tackled the tradeoff between expressibility and trainability in parameterized quantum circuits for combinatorial optimization by introducing the Quantum Qubit Rotation Algorithm (QQRA) for the max-cut problem, achieving solutions with probability close to 1 and comparing favorably against quantum and classical benchmarks.
Optimizing parameterized quantum circuits promises efficient use of near-term quantum computers to achieve the potential quantum advantage. However, there is a notorious tradeoff between the expressibility and trainability of the parameter ansatz. We find that in combinatorial optimization problems, since the solutions are described by bit strings, one can trade the expressiveness of the ansatz for high trainability. To be specific, by focusing on the max-cut problem we introduce a simple yet efficient algorithm named Quantum Qubit Rotation Algorithm (QQRA). The quantum circuits are comprised with single-qubit rotation gates implementing on each qubit. The rotation angles of the gates can be trained free of barren plateaus. Thus, the approximate solution of the max-cut problem can be obtained with probability close to 1. To illustrate the effectiveness of QQRA, we compare it with the well known quantum approximate optimization algorithm and the classical Goemans-Williamson algorithm.