Variational Quantum Circuits for Efficient Transformer Attention

Prasanth

Keywords: Quantum machine learning, Quantum transformers, Variational quantum circuits, Quantum attention mechanisms, Hybrid quantum-classical models, Quantum feature maps, Quantum natural language processing, Near-term quantum algorithms, Quantum computing, Transformer architectures

TL;DR: We present a theoretical framework for quantum-enhanced transformer architecture that integrates variational quantum circuits into the attention mechanism of transformer networks

Abstract: We present a theoretical framework for quantum-enhanced transformer architectures that integrates variational quantum circuits into the attention mechanism of transformer networks. Our approach proposes leveraging quantum feature maps to encode classical attention queries and keys into quantum states, processing them through parameterized quantum circuits, and extracting attention scores via expectation value measurements. We establish the theoretical foundations for this hybrid approach, demonstrating how quantum computational elements could be integrated into existing transformer frameworks. Our theoretical analysis reveals that quantum attention mechanisms could potentially represent exponentially more complex relationships than their classical counterparts, though practical implementation faces significant challenges from current near-term quantum device limitations. The architecture maintains conceptual compatibility with existing transformer frameworks while introducing quantum computational elements that could provide advantages as quantum hardware matures.

Submission Number: 2