Designing Shadow Tomography Protocols by Natural Language Processing

• URL: http://arxiv.org/abs/2509.12782v1
• Date: Tue, 16 Sep 2025 07:58:43 GMT
• Title: Designing Shadow Tomography Protocols by Natural Language Processing
• Authors: Yadong Wu, Pengfei Zhang, Ce Wang, Juan Yao, Yi-Zhuang You,
• Abstract summary: We introduce a novel artificial intelligence-driven protocol for quantum circuit design, benchmarked using shadow tomography for efficient quantum state readout.<n>Inspired by techniques from natural language processing (NLP), our approach selects a compact gate dictionary by optimizing the entangling power of two-qubit gates.<n>We implement a recurrent neural network trained via reinforcement learning to generate high-performing quantum circuits.
• Score: 9.926400664774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum circuits form a foundational framework in quantum science, enabling the description, analysis, and implementation of quantum computations. However, designing efficient circuits, typically constructed from single- and two-qubit gates, remains a major challenge for specific computational tasks. In this work, we introduce a novel artificial intelligence-driven protocol for quantum circuit design, benchmarked using shadow tomography for efficient quantum state readout. Inspired by techniques from natural language processing (NLP), our approach first selects a compact gate dictionary by optimizing the entangling power of two-qubit gates. We identify the iSWAP gate as a key element that significantly enhances sample efficiency, resulting in a minimal gate set of {I, SWAP, iSWAP}. Building on this, we implement a recurrent neural network trained via reinforcement learning to generate high-performing quantum circuits. The trained model demonstrates strong generalization ability, discovering efficient circuit architectures with low sample complexity beyond the training set. Our NLP-inspired framework offers broad potential for quantum computation, including extracting properties of logical qubits in quantum error correction.

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