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This research establishes provable theoretical guarantees that graph sparsification can significantly reduce circuit complexity for the Quantum Approximate Optimization Algorithm (QAOA), specifically targeting noise resilience in trapped-ion simulators. By lowering the asymptotic scaling of required Ising pulses and Pauli-X operations, these compilation schemes directly address a critical bottleneck near-term quantum devices face regarding error accumulation during complex optimization tasks like Max-Cut.
Graph decomposition boosts resilience in quantum optimization
New compilation schemes leverage graph sparsification and decomposition to reduce QAOA expense for the Max-Cut problem, offering provable gains in quantum noise resilience and circuit complexity.
Jai Moondra of Carnegie Mellon University, Phillip C. Lotshaw of Oak Ridge National Laboratory, Greg Mohler of Georgia Tech Research Institute, and Swati Gupta of Massachusetts Institute of Technology have established the first provable guarantees for improving quantum noise resilience and reducing circuit complexity through graph sparsification and decomposition. Their work focuses on compilation schemes for the Quantum Approximate Optimization Algorithm (QAOA), specifically when solving the Max-Cut problem with trapped-ion simulators utilizing Pauli-X operations. The researchers demonstrate that graph sparsification directly reduces circuit complexity for…
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Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations
Quantum Journal · 7 aug. 2026
Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations
arXiv · 28 iul. 2026
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