.png){kind=icon}
返回
Quantum approximate optimization algorithm applied to the binary perceptron
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevB.107.094202.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We apply digitized quantum annealing (QA) and quantum approximate optimization algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron. At variance with the usual QAOA applications to MaxCut, or to quantum spin-chains ground -state preparation, here the classical cost function is characterized by highly nonlocal multispin interactions. Yet, we provide evidence for the existence of optimal smooth solutions for the QAOA parameters, which are transferable among typical instances of the same problem, and we prove numerically an enhanced performance of QAOA over traditional QA. We also investigate on the role of the classical cost-function landscape geometry in this problem. By artificially breaking this geometrical structure, we show that the detrimental effect of a gap -closing transition, encountered in QA, is also negatively affecting the performance of our QAOA implementation.
期刊
IF:
3.7
论文数:
15.4W
被引数:
41.0W
机构
引用论文
Instance Independence of Single Layer Quantum Approximate Optimization Algorithm on Mixed-Spin Models at Infinite Size.png)
QUANTUMOAAI
QUANTUMIF5.4
OAAI分享收藏A variational eigenvalue solver on a photonic quantum processor光子量子处理器上的变分特征值求解器NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic DrivingPHYSICAL REVIEW XOAAI
PHYSICAL REVIEW XIF15.7
OAAI分享收藏分享收藏
查看更多内容