Variational Quantum Models for Knowledge Graph Embeddings on NISQ Devices

Variational Quantum Algorithms are being leveraged to enhance knowledge graph embeddings on near-term quantum hardware, also known as NISQ devices. By combining quantum circuits with classical optimization, these models can potentially tackle complex problems more efficiently. Recent proposals have emerged with distinct architectures, differing in their score function computation and qubit requirements. Researchers are exploring the capabilities of these models, which could lead to significant advancements in knowledge graph embedding. The use of variational quantum models on NISQ devices has the potential to revolutionize the field of quantum computing, enabling more efficient and effective processing of complex data. This development is crucial for practitioners, as it may lead to breakthroughs in fields such as cryptography and computation, thereby rendering existing security protocols obsolete¹. The implications of this research are far-reaching, and its applications could significantly impact the future of secure data processing.