Variational Quantum Classifiers Through the Lens of the Hessian

by   Pinaki Sen, et al.

In quantum computing, the variational quantum algorithms (VQAs) are well suited for finding optimal combinations of things in specific applications ranging from chemistry all the way to finance. The training of VQAs with gradient descent optimization algorithm has shown a good convergence. At an early stage, the simulation of variational quantum circuits on noisy intermediate-scale quantum (NISQ) devices suffers from noisy outputs. Just like classical deep learning, it also suffers from vanishing gradient problems. It is a realistic goal to study the topology of loss landscape, to visualize the curvature information and trainability of these circuits in the existence of vanishing gradients. In this paper, we calculated the Hessian and visualized the loss landscape of variational quantum classifiers at different points in parameter space. The curvature information of variational quantum classifiers (VQC) is interpreted and the loss function's convergence is shown. It helps us better understand the behavior of variational quantum circuits to tackle optimization problems efficiently. We investigated the variational quantum classifiers via Hessian on quantum computers, started with a simple 4-bit parity problem to gain insight into the practical behavior of Hessian, then thoroughly analyzed the behavior of Hessian's eigenvalues on training the variational quantum classifier for the Diabetes dataset.



There are no comments yet.


page 1

page 2

page 5

page 6

page 7

page 8

page 9


Mode connectivity in the loss landscape of parameterized quantum circuits

Variational training of parameterized quantum circuits (PQCs) underpins ...

Towards Efficient Ansatz Architecture for Variational Quantum Algorithms

Variational quantum algorithms are expected to demonstrate the advantage...

Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training

Variational quantum algorithms are expected to demonstrate the advantage...

Optimal training of variational quantum algorithms without barren plateaus

Variational quantum algorithms (VQAs) promise efficient use of near-term...

Recent advances for quantum classifiers

Machine learning has achieved dramatic success in a broad spectrum of ap...

Representation Learning via Quantum Neural Tangent Kernels

Variational quantum circuits are used in quantum machine learning and va...

Adaptive shot allocation for fast convergence in variational quantum algorithms

Variational Quantum Algorithms (VQAs) are a promising approach for pract...

Code Repositories

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.