论文标题
QLEET：可视化损失景观，表现性，纠缠能力和训练轨迹的参数化量子电路
qLEET: Visualizing Loss Landscapes, Expressibility, Entangling Power and Training Trajectories for Parameterized Quantum Circuits
论文作者
论文摘要
我们提出了QLEET，这是一种用于研究参数化量子电路（PQC）的开源Python软件包，该软件包被广泛用于各种变异量子算法（VQAS）和量子机器学习（QML）算法中。 QLEET可以通过研究其纠缠谱和IT产生的参数化状态的分布来计算PQC的表达性和纠缠能力等属性。此外，它允许用户可视化PQC的训练轨迹以及他们为不同目标功能生成的高维损失景观。它支持使用流行的量子计算库（例如Qiskit，CIRQ和Pyquil）构建的量子电路和噪声模型。在我们的工作中，我们展示了QLEET如何通过利用ANSATZ的能力和损失景观结构的直观见解来设计和改善混合量子古典算法的机会。
We present qLEET, an open-source Python package for studying parameterized quantum circuits (PQCs), which are widely used in various variational quantum algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables the computation of properties such as expressibility and entangling power of a PQC by studying its entanglement spectrum and the distribution of parameterized states produced by it. Furthermore, it allows users to visualize the training trajectories of PQCs along with high-dimensional loss landscapes generated by them for different objective functions. It supports quantum circuits and noise models built using popular quantum computing libraries such as Qiskit, Cirq, and Pyquil. In our work, we demonstrate how qLEET provides opportunities to design and improve hybrid quantum-classical algorithms by utilizing intuitive insights from the ansatz capability and structure of the loss landscape.
