Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
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Quantum education is best viewed as a non-linear ecosystem with multiple entry points, feedback loops, and transition gaps rather than a linear pipeline, facing challenges in access, standardization, and evaluation.
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CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
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The Quantum Education Ecosystem: A Review of Global Initiatives, Methods, and Challenges
Quantum education is best viewed as a non-linear ecosystem with multiple entry points, feedback loops, and transition gaps rather than a linear pipeline, facing challenges in access, standardization, and evaluation.