Artificial Intelligence and Electrical & Electronics Engineering: AIEEE Open Access
Graphene Hardware Combined with Transformer Software for Quantum Computing: Critical Role of Giant Magnetoresistance at 0.4 Tesla
Abstract
Chur Chin
The convergence of graphene-based quantum hardware with transformer-architecture quantum software represents a paradigm shift in quantum information processing. We propose and theoretically characterize a hybrid quantum computing platform in which bilayer graphene qubits constitute the physical hardware layer and a quantum-adapted transformer model constitutes the software framework. A central and critical finding of this work is that the system requires a precisely tuned giant magnetoresistance (GMR) field of 0.4 Tesla to maintain qubit spin coherence and prevent decoherence-induced computation failure. At this field strength, the GMR ratio peaks at approximately 197%, the coherence time T2 exceeds 120 μs, and the single-qubit gate fidelity reaches 99.7%. We describe the physical mechanism by which GMR at 0.4 T locks spin-valley polarization in graphene, enabling stable superposition states that can be manipulated by quantum transformer attention layers, variational quantum encoders, and parameterized quantum circuits. The implications for scalable fault-tolerant quantum computing are discussed.