Year: 2026
Type: article
Abstract: One of the key non-classical resources involved in the promise of quantum computing is quantum entanglement. It allows for correlations between qubits that are not possible by independent local states, thus it can be used for computational speed-up, quantum simulation, quantum error correction, quantum machine learning, and secure quantum communication. This paper explores the role of entanglement in quantum computing, and looks at its applications with a secondary-data based research design. This study will evaluate the progress of entanglement from a fundamental physical phenomenon to an engineering resource for processors, algorithms, and fault-tolerant architectures from peer-reviewed literature, institutional reports and public research data from 2018 to 2025. The paper is divided into five subheadings in the introduction, four subheadings in the literature review, a research methodology, and a results section with five secondary-data tables, and a conclusion. The findings reveal a significant acceleration in noisy intermediate-scale quantum devices between 2018 and 2025, along with efforts to prove quantum computational advantage, grow of variational algorithms and increased advancement toward logical qubits and quantum error correction. The evidence also shows that there are important limitations for practical quantum benefit, including decoherence, errors in quantum gates, difficulties loading data, benchmarking arguments, and the high cost of overhead error correction in fault-tolerant quantum computing. The authors conclude that entanglement is not just a theoretical property of quantum physics, but the resource that is needed to link hardware and software with applications. In the future, fidelity of entangling gates, scalable qubit connectivity, error-mitigated algorithms, and fault-tolerant logical qubit designs will all be important.
Author: Shanu Yadav
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7220141406