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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach.

Amulyashree Sridhar1, Kalyan Nagaraj2, Shambhavi Bangalore Ravi2

  • 1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru Campus, Bengaluru 560035, Karnataka, India.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

Quantum machine learning (QML) models effectively detect smart contract liabilities, outperforming traditional methods. The Quantum Neural Network (QNN) model achieved 82.43% accuracy, offering a promising solution for blockchain security.

Keywords:
McNemar’s TestQMLQNNliabilitiessmart contracts

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Area of Science:

  • Computer Science
  • Quantum Computing
  • Blockchain Technology

Background:

  • Smart contracts are crucial for blockchain and decentralized applications.
  • Liabilities in smart contracts can lead to system failures.
  • Current static detection tools have limitations, including false positives and inability to generalize.
  • Machine learning (ML) approaches face challenges with smart contract size, storage, and performance.

Purpose of the Study:

  • To explore Quantum Machine Learning (QML) applications for identifying liabilities in smart contracts.
  • To address the limitations of existing static analysis and ML-based methods for smart contract vulnerability detection.

Main Methods:

  • Employed four QML approaches: Quantum Neural Network (QNN), Quantum Support Vector Machine (QSVM), Variational Quantum Circuit (VQC), and Quantum Random Forest (QRF).
  • Trained and evaluated models on smart contract data to detect liabilities.
  • Validated the best-performing model on the SolidiFI dataset.
  • Utilized McNemar's test for statistical validation.

Main Results:

  • The QNN model demonstrated superior performance in detecting smart contract liabilities, achieving an accuracy of 82.43%.
  • QML approaches showed advantages over traditional ML in terms of storage and performance.
  • Consistent results were observed when the QNN model was tested on the SolidiFI dataset.
  • Statistical validation confirmed the model's performance.

Conclusions:

  • QML, particularly the QNN model, presents a viable and effective alternative for detecting smart contract liabilities.
  • QML offers improved storage and performance benefits compared to traditional ML methods for this task.
  • The findings suggest QML can enhance the security and reliability of smart contracts and decentralized systems.