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Resource-Efficient Adaptive Variational Quantum Algorithm for Combinatorial Optimization Problems
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DOI：10.1002/qute.202400484.png)
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Abstract
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Variational quantum algorithms (VQAs) are quantum-classical hybrid algorithms that are promising for the near future. The Quantum Approximate Optimization Algorithm (QAOA) is a representative VQA for solving combinatorial optimization problems. However, the parameterized quantum circuit (PQC) of QAOA still has room for improvement. The existing method, called ADAPT-QAOA, has improved the PQC of QAOA, but the circuit depth remains deep. A Resource-Efficient Adaptive VQA (RE-ADAPT-VQA) that utilizes gates from a new gate pool is proposed to construct a PQC. RE-ADAPT-VQA incrementally integrates parameterized quantum gates based on the gradient until the predefined stopping criteria are satisfied, and a rollback mechanism is proposed to ensure that the circuit remains shallow. The algorithm is experimentally simulated to solve Max-Cut problem and the maximum independent set problem. The results show that RE-ADAPT-VQA significantly reduces circuit depth, single-qubit gates, and CNOT gates compared to existing methods, while maintaining the same level of energy error.
Keywords:
max-cut
maximum independent set
parameterized quantum circuit
variational quantum algorithm
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4.3
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3.2K
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