Fake News Detection: An Application of Quantum K-Nearest Neighbors

Tian, Ziyan; Baskiyar, Sanjeev

doi:10.1109/ssci50451.2021.9659944

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Consequently, the expected number of tuples in a 𝑖 in which the bits in positions 𝑘 and 𝑘 ′ contain any one of the aforementioned combinations is 𝑑 4 . Thus, irrespective of whether the verification outcome 𝑐 𝑖 𝑘 is 1 or 0, the expected number of tuples in p 𝑖 𝑘 in which the bits in positions 𝑘 and 𝑘 ′ are 𝑐 𝑖 𝑘 𝑐 𝑖 𝑘 or 𝑐 𝑖 𝑘 𝑐 𝑖 𝑘 is 𝑑 4 , which proves property (20). This completes the proof of this proposition.…”

Section: The News Verification Phasesupporting

confidence: 58%

Unsuccessful Transitions: Understanding Dropout from the Athletes’ Perspective

Andronikos¹,

Westbury²,

Martindale³

2019

AJSPO

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In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new, to the best of our knowledge, algorithm that uses quantum "entanglement" (think of it as a special connection) to help news aggregators sniff out bad actors, whether they be other news sources or even fact-checkers trying to spread misinformation. This algorithm doesn't rely on quantum signatures, it just uses basic quantum technology we already have, in particular, special pairs of particles called "EPR pairs" that are much easier to create than other options. More complex entangled states are like juggling too many balls -they're hard to make and slow things down, especially when many players are involved. For instance, bigger, more complex states like "GHZ states" work for small groups, but they become messy with larger numbers. So, we stick with Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are in the game. This means our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Bonus points: it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics.

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Section: The News Verification Phasesupporting

confidence: 58%

Unsuccessful Transitions: Understanding Dropout from the Athletes’ Perspective

Andronikos¹,

Westbury²,

Martindale³

2019

AJSPO

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show abstract

“…For example, Zhang et al proposed an improved quantum KNN algorithm, which uses efficient encoding methods and amplitude estimation techniques to construct a weighted quantum KNN circuit, thereby improving similarity calculation [7]. Tian and Baskiyar also proposed a quantum KNN method and applied it to fake news detection [8]. Gao et al proposed a quantum KNN algorithm, which uses a quantum sub-algorithm to search for the minimum value in an unordered dataset, thereby obtaining K nearest neighbors of the testing data [9].…”

Section: B Quantum Knnmentioning

confidence: 99%

“…Zhang et al proposed a quantum weighted KNN algorithm [7]. Tian and Baskiyar used Ruan et al 's quantum Hamming distance calculation method and Grover search algorithm to construct quantum KNN [8]. Gao et al used quantum Mahalanobis distance and quantum minimum search algorithm to construct quantum KNN [9].…”

Section: Introductionmentioning

confidence: 99%

Quantum KNN Classification With K Value Selection and Neighbor Selection

Li,

Zhang,

Zhang

et al. 2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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The KNN (K-nearest neighbors) algorithm is one of Top-10 data mining algorithms and is widely used in various fields of artificial intelligence. This leads to that quantum KNN algorithms have developed and achieved certain speed improvements, denoted as Q-KNN. However, these Q-KNN methods must face two key problems as follows. The first one is that they are mainly focused on neighbor selection without paying attention to the influence of K value on the algorithm. The second is that only the neighbor selection process is quantized, and the selection of K value is not quantized. To solve these problems, this paper designs a novel quantum circuit for KNN classification, so as to simultaneously quantumize the neighbor selection and K value selection process. Specifically, the least squares loss and sparse regularization term are first used to construct the objective function of the proposed quantum KNN, so that it can simultaneously obtain the optimal K value and K nearest neighbors of the testing data. And then, a new quantum circuit is proposed to quantumize the process through quantum phase estimation, controlled rotation, and inverse phase estimation techniques. Finally, experiments are conducted with qiskit and matlab to output the quantum and classical results of the algorithm, verifying that the proposed algorithm can output the optimal K value and K nearest neighbors for each testing data.

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“…Researchers are actively pursuing the development of algorithmic methods that could effectively detect fake news and deepfakes by integrating quantum machine learning techniques [26]. Tian et al [27] proposed a fake news detection system utilizing quantum K-Nearest Neighbors. Furthermore, Google has introduced an open-source library for quantum machine learning, suggesting the potential for quantum computing to address fake and deepfake challenges in the near term [28].…”

Section: Introductionmentioning

confidence: 99%

A Quantum Approach to News Verification from the Perspective of a News Aggregator

Andronikos,

Sirokofskich

2024

Information

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In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new—to the best of our knowledge—algorithm that uses quantum “entanglement” (think of it as a special connection) to help news aggregators “sniff out” bad actors, whether they are other news sources or even fact-checkers trying to spread misinformation. This algorithm does not rely on quantum signatures; it merely uses basic quantum technology which we already have, in particular, special pairs of particles called “EPR pairs” that are much easier to create than other options. More elaborate entangled states are like juggling too many balls—they are difficult to make and slow things down, especially when many players are involved. So, we adhere to Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are involved. This means that our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Additionally, as a “bonus point”, it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics.

show abstract

Fake News Detection: An Application of Quantum K-Nearest Neighbors

Cited by 6 publications

References 23 publications

Unsuccessful Transitions: Understanding Dropout from the Athletes’ Perspective

Unsuccessful Transitions: Understanding Dropout from the Athletes’ Perspective

Quantum KNN Classification With K Value Selection and Neighbor Selection

A Quantum Approach to News Verification from the Perspective of a News Aggregator

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