Chinese Physics B

A new method of constructing adversarial examples for quantum variational circuits

Jinge Yan, Lili Yan, Shibin Zhang

Published Oct 19, 2022• 0 citations

Abstract

A quantum variational circuit is a quantum machine learning model similar to a neural network. A crafted adversarial example can lead to incorrect results for the model. Using adversarial examples to train the model will greatly improve its robustness. The existing method is to use automatic differentials or finite difference to obtain a gradient and use it to construct adversarial examples. This paper proposes an innovative method for constructing adversarial examples of quantum variational circuits. In this method, the gradient can be obtained by measuring the expected value of a quantum bit respectively in a series quantum circuit. This method can be used to construct the adversarial examples for a quantum variational circuit classifier. The implementation results prove the effectiveness of the proposed method. Compared with the existing method, our method requires fewer resources and is more efficient.

DOI: 10.1088/1674-1056/ac9b32

Related papers

Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions

Jonathan Romero, Alán Aspuru‐Guzik

Abstract A hybrid quantum–classical approach to model continuous classical probability distributions using a variational quantum circuit is proposed. The architecture of this quantum generator consists of a quantum circuit that encodes a classical random variable into a quantum state and a parameterized quantum circuit trained to mimic the target distribution. The model allows for easy interfacing with a classical function, such as a neural network, and is trained using an adversarial learning approach. It is shown that the quantum generator is able to learn using either a classical neural network or a variational quantum circuit as the discriminator model. This implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of the variational quantum generators can serve as a blueprint for designing hybrid quantum–classical models for other machine learning tasks.

arXiv:1901.00848Advanced Quantum TechnologiesDec 3, 2020185 citations

Quantum generative adversarial networks

Pierre-Luc Dallaire-Demers, Nathan Killoran

Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Furthermore, we also show how to compute gradients---a key element in generative adversarial network training---using another quantum circuit. We give an example of a simple practical circuit ansatz to parametrize quantum machine learning models and perform a simple numerical experiment to demonstrate that quantum generative adversarial networks can be trained successfully.

arXiv:1804.08641Physical Review AJul 23, 2018506 citations

Ideal Parametrisation Estimation for Variational Quantum Circuit Classifiers Using Machine Learning

Mostafa Fathi, Walid E. Gomaa, Yasutaka Wada +3 more

Quantum neural network implementation through variational quantum circuits is getting attention due to their applicability to the existing quantum computer technology and significant model parameters reduction. This paper considers the problem of automatically transforming existing neural networks into corresponding variational quantum circuits. In particular, given a trained neural network, the paper considers estimating the number of ansatz layers so that the corresponding circuit is not over or under-parametrized. This is achieved by constructing a dataset containing many trained neural networks each labelled with the corresponding optimal number of variational quantum layers, obtained through experimentation. The paper further performs dataset augmentation using an LLM model to speed up dataset construction. The paper then selects a set of features based on both the network architecture and the trained parameters, and then trains a feed-forward neural network. The trained network achieves 86.7% accuracy on unseen unaugmented test circuits.

IEEE International Conference on Quantum Computing and Engineering (QCE)Sep 15, 20240 citations

Differentiable learning of quantum circuit Born machines

Jin-Guo Liu, Lei Wang

Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. One can efficiently draw samples from the quantum circuits via projective measurements on qubits. However, similar to the leading implicit generative models in deep learning, such as the generative adversarial networks, the quantum circuits cannot provide the likelihood of the generated samples, which poses a challenge to the training. We devise an efficient gradient-based learning algorithm for the quantum circuit Born machine by minimizing the kerneled maximum mean discrepancy loss. We simulated generative modeling of the BARS-AND-STRIPES dataset and Gaussian mixture distributions using deep quantum circuits. Our experiments show the importance of circuit depth and the gradient-based optimization algorithm. The proposed learning algorithm is runnable on near-term quantum device and can exhibit quantum advantages for probabilistic generative modeling.

arXiv:1804.04168Physical Review ADec 19, 2018279 citations

Learning and inference on generative adversarial quantum circuits

Jinfeng Zeng, Yufeng Wu, Jin-Guo Liu +2 more

Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to the lack of an efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed-up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuits using the prototypical bars-and-stripes dataset. Generative adversarial quantum circuits are a fresh approach to machine learning which may enjoy the practically useful quantum advantage of near-term quantum devices.

arXiv:1808.03425Physical Review AMay 6, 201994 citations

Quantum Adversarial Transfer Learning

Longhan Wang, Yifan Sun, Xiangdong Zhang

Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of tasks from insufficient target data. In this study, we introduce the notion of quantum adversarial transfer learning, where data are completely encoded by quantum states. A measurement-based judgment of the data label and a quantum subroutine to compute the gradients are discussed in detail. We also prove that our proposal has an exponential advantage over its classical counterparts in terms of computing resources such as the gate number of the circuits and the size of the storage required for the generated data. Finally, numerical experiments demonstrate that our model can be successfully trained, achieving high accuracy on certain datasets.

EntropyJul 20, 20236 citations