Effects of Quantum Noise on Quantum Approximate Optimization Algorithm
arXiv:1909.02196
Abstract
The quantum-classical hybrid algorithm is an algorithm that holds promise in demonstrating the quantum advantage in NISQ devices. When running such algorithms, effects from quantum noise are inevitable. In our work, we consider a well-known hybrid algorithm, the quantum approximate optimization algorithm (QAOA). We study the effects on QAOA from typical quantum noise channels and produce several numerical results. Our research indicates that the output state fidelity, the cost function, and its gradient obtained from QAOA decrease exponentially with respect to the number of gates and noise strength. Moreover, we find that noise merely flattens the parameter space without changing its structure, so optimized parameters will not deviate from their ideal values. Our result provides evidence for the effectiveness of hybrid algorithms running on NISQ devices.
9 pages, 7 figures
Cited by in corpus (9)
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- Noise-Resilient Variational Hybrid Quantum-Classical Optimization
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- Evaluation of QAOA based on the approximation ratio of individual samples
- Quantum algorithms with local particle number conservation: noise effects and error correction
- The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
- Characterizing Error Mitigation by Symmetry Verification in QAOA
- Lower Bounds on Circuit Depth of the Quantum Approximate Optimization Algorithm
- Error Analysis of the Variational Quantum Eigensolver Algorithm