English(EN) Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding

新的TNQE框架提供电路高效的量子数据编码

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一个名为张量网络量子数据编码（TNQE）的新框架，以解决当前将经典数据编码为量子状态的低效方法。TNQE利用结构化酉张量网络表示来创建更浅、所需量子资源更少的编码电路。这种方法允许直接优化编码过程，并在量子硬件上展示了实际可行性，与传统的幅度编码相比，电路深度最多可减少25倍。 AI

影响这项研究通过减少数据编码的计算开销，可能加速量子机器学习应用的发展和可扩展性。

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arXiv cs.LG TIER_1 English(EN) · Guang Lin, Toshihisa Tanaka, Qibin Zhao · 2026-10-23 04:00

Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding

arXiv:2602.16266v2 Announce Type: replace-cross Abstract: Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and substantial quantum resources, which limits s…