Diagonal Adaptive Non-local Observables on Quantum Neural Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918502029328384 |
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| author | Tseng, Huan-Hsin Li, Yan Lin, Hsin-Yi Chen, Samuel Yen-Chi |
| author_facet | Tseng, Huan-Hsin Li, Yan Lin, Hsin-Yi Chen, Samuel Yen-Chi |
| contents | Adaptive Non-local Observables (ANOs) have shown that making quantum observables dynamic can substantially enlarge the function space of Variational Quantum Algorithms, partly shifting hardware demands from circuit synthesis to measurement design. However, this advantage is accompanied by a steep increase in the number of parameters, as well as the classical optimization cost for varying general Hermitian observables.
We propose a special form of ANO that significantly reduces this burden by considering only diagonal observables paired with quantum circuits. Mathematically, this is equivalent to the full ANO of a large parameter space since diagonal matrices are canonical representatives of the ANO space modulo unitary similarity. As a result, Diagonal ANO retains the same capability of full ANO while reducing $k$-local observable complexity from $O(4^k)$ to $O(2^k)$ and lowering the corresponding measurement-side classical computation. In this sense, diagonal ANO preserves much of the benefit of full ANO while encompassing conventional VQCs as a special case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15410 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diagonal Adaptive Non-local Observables on Quantum Neural Networks Tseng, Huan-Hsin Li, Yan Lin, Hsin-Yi Chen, Samuel Yen-Chi Quantum Physics Artificial Intelligence Machine Learning Adaptive Non-local Observables (ANOs) have shown that making quantum observables dynamic can substantially enlarge the function space of Variational Quantum Algorithms, partly shifting hardware demands from circuit synthesis to measurement design. However, this advantage is accompanied by a steep increase in the number of parameters, as well as the classical optimization cost for varying general Hermitian observables. We propose a special form of ANO that significantly reduces this burden by considering only diagonal observables paired with quantum circuits. Mathematically, this is equivalent to the full ANO of a large parameter space since diagonal matrices are canonical representatives of the ANO space modulo unitary similarity. As a result, Diagonal ANO retains the same capability of full ANO while reducing $k$-local observable complexity from $O(4^k)$ to $O(2^k)$ and lowering the corresponding measurement-side classical computation. In this sense, diagonal ANO preserves much of the benefit of full ANO while encompassing conventional VQCs as a special case. |
| title | Diagonal Adaptive Non-local Observables on Quantum Neural Networks |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.15410 |