摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据新闻报道来自科罗拉多州博尔德市的NewsR X编辑的一项研究表明，“我们建立在最近关于使用利用连续弱测度估计哈密顿参数的机器学习模型量子位作为输入。我们考虑了两种环境来训练我们的模型：（1）监督学习，其中弱测量训练记录可以用已知的哈密顿参数标记，和(2)无监督学习，没有LABE LS。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingout of Boulder, Colorado, by NewsR x editors, research stated, “We build upon recent work on the useof machine-lea rning models to estimate Hamiltonian parameters using continuous weak measuremen tof qubits as input. We consider two settings for the training of our model: (1 ) supervised learning,where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2)unsupervised learning, where no labe ls are available.”
NETL
NSTL