State Similarity in Modular Superconducting Quantum Processors with Classical Communications
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910999821418496 |
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| author | Wu, Bujiao Xie, Changrong Mi, Peng Wu, Zhiyi Guo, Zechen Huang, Peisheng Huang, Wenhui Sun, Xuandong Zhang, Jiawei Zhang, Libo Qiu, Jiawei Linpeng, Xiayu Tao, Ziyu Chu, Ji Jiang, Ji Liu, Song Niu, Jingjing Zhou, Yuxuan Du, Yuxuan Ren, Wenhui Zhong, Youpeng Liu, Tongliang Yu, Dapeng |
| author_facet | Wu, Bujiao Xie, Changrong Mi, Peng Wu, Zhiyi Guo, Zechen Huang, Peisheng Huang, Wenhui Sun, Xuandong Zhang, Jiawei Zhang, Libo Qiu, Jiawei Linpeng, Xiayu Tao, Ziyu Chu, Ji Jiang, Ji Liu, Song Niu, Jingjing Zhou, Yuxuan Du, Yuxuan Ren, Wenhui Zhong, Youpeng Liu, Tongliang Yu, Dapeng |
| contents | As quantum devices continue to scale, distributed quantum computing emerges as a promising strategy for executing large-scale tasks across modular quantum processors. A central challenge in this paradigm is verifying the correctness of computational outcomes when subcircuits are executed independently following circuit cutting. Here we propose a cross-platform fidelity estimation algorithm tailored for modular architectures. Our method achieves substantial reductions in sample complexity compared to previous approaches designed for single-processor systems. We experimentally implement the protocol on modular superconducting quantum processors with up to 6 qubits to verify the similarity of two 11-qubit GHZ states. Beyond verification, we show that our algorithm enables a federated quantum kernel method that preserves data privacy. As a proof of concept, we apply it to a 5-qubit quantum phase learning task using six 3-qubit modules, successfully extracting phase information with just eight training samples. These results establish a practical path for scalable verification and trustworthy quantum machine learning of modular quantum processors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | State Similarity in Modular Superconducting Quantum Processors with Classical Communications Wu, Bujiao Xie, Changrong Mi, Peng Wu, Zhiyi Guo, Zechen Huang, Peisheng Huang, Wenhui Sun, Xuandong Zhang, Jiawei Zhang, Libo Qiu, Jiawei Linpeng, Xiayu Tao, Ziyu Chu, Ji Jiang, Ji Liu, Song Niu, Jingjing Zhou, Yuxuan Du, Yuxuan Ren, Wenhui Zhong, Youpeng Liu, Tongliang Yu, Dapeng Quantum Physics As quantum devices continue to scale, distributed quantum computing emerges as a promising strategy for executing large-scale tasks across modular quantum processors. A central challenge in this paradigm is verifying the correctness of computational outcomes when subcircuits are executed independently following circuit cutting. Here we propose a cross-platform fidelity estimation algorithm tailored for modular architectures. Our method achieves substantial reductions in sample complexity compared to previous approaches designed for single-processor systems. We experimentally implement the protocol on modular superconducting quantum processors with up to 6 qubits to verify the similarity of two 11-qubit GHZ states. Beyond verification, we show that our algorithm enables a federated quantum kernel method that preserves data privacy. As a proof of concept, we apply it to a 5-qubit quantum phase learning task using six 3-qubit modules, successfully extracting phase information with just eight training samples. These results establish a practical path for scalable verification and trustworthy quantum machine learning of modular quantum processors. |
| title | State Similarity in Modular Superconducting Quantum Processors with Classical Communications |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.01657 |