AI-Guided Quantum Compiler Design Using Superalgebraic Symmetries

Abstract: The evolution of quantum computing depends not only on advancements in hardware but also on breakthroughs in compiler design that can translate high-level quantum algorithms into efficient, executable circuits. This research introduces a novel approach to quantum compiler design that leverages AI guidance in tandem with superalgebraic symmetries to optimize gate synthesis, resource usage, and noise resilience. By embedding superalgebraic representations—mathematical structures that unify bosonic and fermionic transformations—into the compilation pipeline, and training AI models to recognize optimal symmetry-preserving transformations, we construct a quantum compiler that is adaptive, structure-aware, and scalable. This framework enhances circuit depth minimization, gate reduction, and fidelity preservation in real-time quantum compilation workflows. Keywords: Quantum Compiler, Superalgebras, Artificial Intelligence, Gate Optimization, Quantum Circuits, Symmetry-Preserving Transformations, Qubit Mapping, Quantum Machine Learning