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Related Experiment Video

Updated: May 5, 2026

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
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QRBT: Quantum Driven Reinforcement Learning for Scalable Blockchain Transaction Processing.

Kranthi Kumar Lella1, Shiva Rama Krishna Mallu2

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

Plos One
|February 19, 2026
PubMed
Summary
This summary is machine-generated.

Quantum Driven Reinforcement Learning (QRBT) enhances blockchain transaction processing by reducing latency by up to 91% and improving security against quantum attacks. This novel framework offers a scalable and energy-efficient solution for future blockchain systems.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Blockchain Technology

Background:

  • Blockchain transaction processing faces significant challenges in scalability, latency, and quantum security.
  • Existing solutions struggle to balance high throughput with robust cryptographic resilience against emerging quantum threats.

Purpose of the Study:

  • To introduce QRBT (Quantum Driven Reinforcement Learning), a framework designed to overcome blockchain's scaling and latency issues while ensuring quantum security.
  • To leverage quantum computation and reinforcement learning for improved consensus and transaction validation.

Main Methods:

  • QRBT utilizes a four-tier architecture: quantum computation, reinforcement learning, blockchain security, and transaction processing.
  • It integrates variational quantum circuits and Quantum Key Distribution (QKD) with an actor-critic reinforcement learning paradigm.
  • Quantum-enhanced state encoding and circuit refinement drive adaptive policy optimization.

Main Results:

  • Transaction latency reduced by up to 91.264%, cryptographic security improved by up to 96.152%, and throughput enhanced by 92.635%.
  • Consensus energy consumption was minimized, and reinforcement learning convergence stabilized efficiently, outperforming baseline methods.
  • QRBT demonstrated simultaneous high throughput, security, and energy efficiency against quantum attacks.

Conclusions:

  • Quantum-assisted reinforcement learning presents a scalable and secure approach for next-generation blockchain systems.
  • QRBT effectively addresses the critical challenges of blockchain performance and quantum adversary resistance.