BDD2Seq framework enhances reversible-circuit synthesis for quantum computing

Researchers have developed BDD2Seq, a novel graph-to-sequence framework designed to improve reversible-circuit synthesis for quantum computing. This approach utilizes a Graph Neural Network encoder and a Pointer-Network decoder to predict optimal variable orderings for Binary Decision Diagrams (BDDs), which are crucial for minimizing resource consumption like Quantum Cost. BDD2Seq addresses the limitations of traditional heuristics in handling complex circuits by learning structural dependencies previously overlooked. Experiments demonstrate that BDD2Seq significantly reduces Quantum Cost and speeds up synthesis compared to existing methods. AI

IMPACT Improves efficiency and scalability in quantum circuit design by leveraging graph-based AI models.

RANK_REASON The cluster contains a research paper detailing a new method for circuit synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• BDD2Seq
• Binary Decision Diagrams
• Boolean Functions
• Diverse Beam Search
• graph neural network
• Mingkai Miao
• Pointer Networks
• quantum computing
• Quantum Cost

• paper
• infra

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