THE ALTERMAGNETIC QUANTUM INTELLIGENCE FRAMEWORK AI-Driven Pulse Optimization for Manganese-Based Spin Qubits in Magnetically Shielded Quantum Machine Learning
Year: 2026
Type: book
Abstract: The Altermagnetic Quantum Intelligence Framework addresses the quantum decoherence bottleneck limiting Quantum Machine Learning through a hardware-software co-design paradigm leveraging Manganese Telluride (MnTe). Unlike conventional magnetic materials, altermagnetic MnTe exhibits zero net macroscopic magnetization, establishing a macroscopically magnetically shielded subspace resistant to external uniform magnetic fields, while enabling efficient electrical control via spin-orbit torque. We propose a hybrid architecture where Deep Reinforcement Learning performs offline optimal pulse shaping, generating robust control sequences pre-loaded onto ultra-fast FPGA hardware for sub-nanosecond execution, circumventing the latency constraints of real-time closed-loop control. The framework incorporates ancilla-based syndrome extraction to avoid measurement backaction on data qubits and employs non-Markovian noise models capturing solid-state temporal correlations for realistic simulation-to-reality transfer. This physically grounded approach bridges condensed matter physics and quantum AI, offering a viable pathway to fault-tolerant quantum neural networks while respecting fundamental constraints on control latency, measurement, and noise dynamics.
Author: el-rakhawi, mohamed kamal arafa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225309692