.png){kind=icon}
返回
Filtering variational quantum algorithms for combinatorial optimization
.png){kind=icon}
OA
AI
DOI：10.1088/2058-9565/ac3e54.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the filtering variational quantum eigensolver which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the quantum approximate optimization algorithm. We also demonstrate the experimental feasibility of our algorithms on a Quantinuum trapped-ion quantum processor powered by Honeywell.
Keyword:
eigensolver
variational quantum algorithm
combinatorial optimization
NISQ devices
hardware-efficient
期刊
IF:
5
论文数:
1.4K
被引数:
5.1K
机构
暂无机构信息
引用论文
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.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Towards a cyanobacterial biorefinery: Carbohydrate fingerprint, biocomposition and enzymatic hydrolysis of Nostoc biomass走向蓝细菌生物精炼厂: Nostoc生物质的碳水化合物指纹，生物组成和酶促水解
PREAI
分享收藏Stochastic gradient descent for hybrid quantum-classical optimization混合量子-经典优化的随机梯度下降QUANTUMOAAI
QUANTUMIF5.4
OAAI分享收藏分享收藏
查看更多内容