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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: Jun 23, 2025

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
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Quantum error-correction using humming sparrow optimization based self-adaptive deep cnn noise correction module.

Umesh Uttamrao Shinde1, Ravikumar Bandaru2

  • 1Department of Mathematics, School of Advanced Sciences, VIT-AP University, Besides AP Secretariate, Amaravati, Andhra Pradesh, 522237, India.

Scientific Reports
|June 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Humming Sparrow Optimization based Self-adaptive Deep CNN (HSO-based SADCNN) model to enhance quantum error correction in heavy hexagonal quantum codes. The new model significantly improves reliability for quantum computing applications.

Keywords:
Heavy hexagonal code and quantum computingHumming sparrow optimizationQuantum error correctionSelf adaptive deep CNN

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Area of Science:

  • Quantum Information Science
  • Quantum Error Correction
  • Quantum Computing

Background:

  • Heavy hexagonal quantum codes are crucial for advancing quantum computing reliability.
  • Optimal decoder design for quantum error correction in these codes presents a significant challenge.
  • Superconducting qubits introduce unique complexities for topological quantum error-correcting codes.

Purpose of the Study:

  • To develop an advanced error correction model for heavy hexagonal quantum codes.
  • To improve the reliability and performance of quantum computing applications.
  • To address the challenge of finding optimal decoders for heavy hexagonal codes.

Main Methods:

  • Development of a Humming Sparrow Optimization based Self-adaptive Deep CNN (HSO-based SADCNN) model.
  • Integration of a Self-adaptive Deep CNN (SADCNN) Noise Correction Module within the decoder.
  • Evaluation of the decoder's efficacy across code distances of three, five, and seven using the Humming Sparrow Optimization (HSO) algorithm.

Main Results:

  • The HSO algorithm effectively fine-tuned the SADCNN decoder, enhancing error correction capabilities for heavy hexagonal quantum codes.
  • Significant progress demonstrated by Training Percentage (TP) 90 metrics, achieving high accuracy.
  • Reduced logical error probability and a diminished bit error rate (5.51 and 3.72, respectively) were observed.

Conclusions:

  • The HSO-based SADCNN model represents a critical advancement in quantum error correction for heavy hexagonal codes.
  • The proposed decoder significantly enhances the reliability of quantum computing, particularly with superconducting qubits.
  • This research contributes to the frontier of quantum error correction, paving the way for more robust quantum computers.