International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
SJC-2026
Conferences Published ↓
AIMAR-2025
SVGASCA-2025
ICCE-2025
ICMESS-24
Chinai-2023
PIPRDA-2023
ICMRS'23
ICCAIoT23
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 8 Issue 1
January-February 2026
Indexing Partners
Bridging Quantum Computing and NLP: A Novel Framework for Advanced Bias Detection with Next-Generation Computing
| Author(s) | Dr. MK Jayanthi Kannan, Mr. Abir Barman, Ms. Anjali Yadav, Ms. Santosini Sahu, Ms. Samikshya Prus |
|---|---|
| Country | India |
| Abstract | Bias entails specific challenges in NLP that lead to inequitable results and ethical dilemmas in AI applications. A unique approach to bias detection is advanced herein that is marketed as Quantum Bias Detection (QBD). The main objective contextualizes the work into evaluating the possible gains from using quantum algorithms in identifying the accuracy and sensitivity of bias in textual data. We have taken a hybrid approach, utilizing quantum machine learning tools alongside typical techniques for bias detection, in which we perform quantum state embeddings and quantum kernel methodology on large and complicated text corpora. Through exhaustive experimentation, we compared the power of QBD ability versus a set of baselines involving both classical and quantum methods, tested against criteria of accuracy, precision, recall, and detection capacity for faintly manifested bias. The most striking discoveries indicate that the Quantum Bias Detection framework greatly increased bias detection rates, especially those of subtle ones that are ambiguously context-dependent, for which traditional approaches often fail. The proof shows that quantum computing could improve bias detection and also should help in building more just AI systems. By way of implication, these findings remain significant for AI ethics and claim to quantum-inspired methods for good bias mitigation in NLP applications. |
| Keywords | Quantum Computing, NLP, Quantum Bias Detection (QBD), Quantum Machine Learning Tools, Techniques for Bias Detection, Quantum State Embeddings, Quantum kernel Methodology, Quantum Methods, Accuracy, Precision, Recall, Detection Capacity, Quantum Computing Driven AI Systems, Quantum Computing for Advanced Bias Detection in NLP, Quantum Cryptography, Quantum-inspired Data Security, Quantum Technologies. |
| Field | Computer Applications |
| Published In | Volume 7, Issue 4, July-August 2025 |
| Published On | 2025-07-13 |
| DOI | https://doi.org/10.36948/ijfmr.2025.v07i04.50889 |
| Short DOI | https://doi.org/g9s9rb |
Share this

E-ISSN 2582-2160
CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.