Scholarlink

<p>Distributed quantum computing offers a promising pathway to overcome the limitations of individual quantum processors by connecting them into a networked system. Due to the physical constraints, the allowed quantum operation in the distributed quantum computing paradigm is local operations and classical communication (LOCC). However, designing practical LOCC protocols for large systems is generally challenging, often requiring exponential computational resources. Here, we propose a general and flexible framework called dynamic LOCCNet (DLOCCNet) for designing and optimizing LOCC protocols using optimization techniques. Rather than designing large-scale protocols directly, DLOCCNet decomposes the large-size problems into small, recursively trainable optimization problems. Protocols designed by this framework achieve performance comparable to existing methods while significantly reducing computational resource demands. We conduct numerical experiments to demonstrate its effectiveness in entanglement distillation and distributed state discrimination tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic local operations and classical communication for automated entanglement manipulation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月20日

查看全文
  • Xia Liu,
  • Jiayi Zhao,
  • Benchi Zhao,
  • Xin Wang

摘要

Distributed quantum computing offers a promising pathway to overcome the limitations of individual quantum processors by connecting them into a networked system. Due to the physical constraints, the allowed quantum operation in the distributed quantum computing paradigm is local operations and classical communication (LOCC). However, designing practical LOCC protocols for large systems is generally challenging, often requiring exponential computational resources. Here, we propose a general and flexible framework called dynamic LOCCNet (DLOCCNet) for designing and optimizing LOCC protocols using optimization techniques. Rather than designing large-scale protocols directly, DLOCCNet decomposes the large-size problems into small, recursively trainable optimization problems. Protocols designed by this framework achieve performance comparable to existing methods while significantly reducing computational resource demands. We conduct numerical experiments to demonstrate its effectiveness in entanglement distillation and distributed state discrimination tasks.