Quantum Machine Learning and Fraud Detection

Pierro, Alessandra Di; Incudini, Massimiliano

doi:10.1007/978-3-030-91631-2_8

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…Models: To reflect an actual application in the NISQ era, we choose not to randomly generate a parameterized quantum circuit model. Instead, we expanded the existing example of Quantum Convolutional Neural Network (QCNN) [32] in the QCNN tutorial 9 of TensorFlow Quantum from 8 qubits (see Fig. 3) to 27 qubits.…”

Section: Scalability In the Nisq Eramentioning

confidence: 99%

“…Because of these reasons, quantum machine learning has been introduced to be applied independently or embedded in classical decision-making models, e.g. fraud detection (in transaction monitoring) [8,9], credit assessments (risk scoring for customers) [10,11], and recommendation systems for content dissemination [12] (see reviews [13,14] for more information). Similar to the classical counterparts, the quantum models are trained on individuals' information, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Verifying Fairness in Quantum Machine Learning

Guan¹,

Wang²,

Ying³

2022

Preprint

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Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition -any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure -Tensor Networks -and implemented on Google's TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits (2 27 -dimensional state space) tripling (2 18 times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.

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Section: Scalability In the Nisq Eramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Verifying Fairness in Quantum Machine Learning

Guan¹,

Wang²,

Ying³

2022

Preprint

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“…Quantum kernels have been shown to improve the performances of classical machine learning algorithms for some problems, such as the prediction of the output of quantum systems (Huang et al 2021, and in learning from distributions based on the discrete logarithm (Liu et al 2021). They have been applied to several real-world, industrial scale problems such as anomaly detection (Liu and Rebentrost 2018), fraud detection (Di Pierro and Incudini 2021;Grossi et al 2022;Kyriienko and Magnusson 2022), the effectiveness of pharmaceutical treatments (Krunic et al 2022), and supernova classifications (Peters et al 2021). These approaches have been experimentally tested on superconducting (Peters et al 2021;, optical (Bartkiewicz et al 2020), and NMR (Kusumoto et al 2021) quantum devices, and their effectiveness is usually assessed empirically.…”

Section: Introductionmentioning

confidence: 99%

Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Marcantonio

Incudini

Tezza

et al. 2023

Quantum Mach. Intell.

Self Cite

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Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has supported the development of a multitude of software frameworks (e.g. Qiskit, Pennylane, Braket) to implement, simulate, and execute quantum algorithms. Most of them allow us to define quantum circuits, run basic quantum algorithms, and access low-level primitives depending on the hardware such software is supposed to run. For most experiments, these frameworks have to be manually integrated within a larger machine learning software pipeline. The researcher is in charge of knowing different software packages, integrating them through the development of long code scripts, analyzing the results, and generating the plots. Long code often leads to erroneous applications, due to the average number of bugs growing proportional with respect to the program length. Moreover, other researchers will struggle to understand and reproduce the experiment, due to the need to be familiar with all the different software frameworks involved in the code script. We propose QuASK, an open-source quantum machine learning framework written in Python that aids the researcher in performing their experiments, with particular attention to quantum kernel techniques. QuASK can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results. QuASK implements most state-of-the-art algorithms to analyze the data through quantum kernels, with the possibility to use projected kernels, (gradient-descent) trainable quantum kernels, and structure-optimized quantum kernels. Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.

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“…Examples include quantum support vector machines [4], quantum convolution neural networks [5], quantum recurrent neural networks [6], quantum generative adversarial networks [7] and quantum reinforcement learning networks [8]. Subsequently, these models have been tested to solve a wide range of real-world problems, such as fraud detection (in transaction monitoring) [9,10], credit assessments (risk scoring for customers) [11,12] and handwritten digit recognition [13]. On the other hand, a series of quantum machine learning algorithms without the classical counterparts have also been designed to solve specific problems.…”

Section: Introductionmentioning

confidence: 99%

Detecting Violations of Differential Privacy for Quantum Algorithms

Guan,

Fang,

Huang

et al. 2023

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

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Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and other ethical issues in quantum computing naturally rises up. In this paper, we define a formal framework for detecting violations of differential privacy for quantum algorithms. A detection algorithm is developed to verify whether a (noisy) quantum algorithm is differentially private and automatically generates bugging information when the violation of differential privacy is reported. The information consists of a pair of quantum states that violate the privacy, to illustrate the cause of the violation. Our algorithm is equipped with Tensor Networks, a highly efficient data structure, and executed both on TensorFlow Quantum and TorchQuantum which are the quantum extensions of famous machine learning platforms -TensorFlow and PyTorch, respectively. The effectiveness and efficiency of our algorithm are confirmed by the experimental results of almost all types of quantum algorithms already implemented on realistic quantum computers, including quantum supremacy algorithms (beyond the capability of classical algorithms), quantum machine learning models, quantum approximate optimization algorithms, and variational quantum eigensolvers with up to 21 quantum bits.

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Quantum Machine Learning and Fraud Detection

Cited by 8 publications

References 24 publications

Verifying Fairness in Quantum Machine Learning

Verifying Fairness in Quantum Machine Learning

Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Detecting Violations of Differential Privacy for Quantum Algorithms

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