دسته بندی:

محاسبات کوانتومی - Quantum-Computing

---

سال انتشار:

2022

---

عنوان انگلیسی مقاله:

Learning to Learn Variational Quantum Algorithm

---

ترجمه فارسی عنوان مقاله:

آموزش یادگیری الگوریتم کوانتومی متغیر

---

منبع:

ieee - ieee Transactions on Neural Networks and Learning Systems; ;PP;99;10:1109/TNNLS:2022:3151127

---

نویسنده:

Rui Huang; Xiaoqing Tan; Qingshan Xu

---

چکیده انگلیسی:

Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called “meta-learner.” Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H2), lithium hydride (LiH), and helium hydride cation (HeH+). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.
Index Terms: Meta-learning | quantum algorithm | quantum computing | quantum information | quantum machine learning(QML).

---

---

قیمت: رایگان

---

توضیحات اضافی:

---

---

---