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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.16623 |
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| _version_ | 1866914550618521600 |
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| author | Su, Howard Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Tseng, Huan-Hsin |
| author_facet | Su, Howard Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Tseng, Huan-Hsin |
| contents | Variational Quantum Circuits (VQC) are promising models for quantum machine learning, but standard monolithic architectures face an expressivity--trainability dilemma: small circuits can be under-parameterized, while larger circuits are difficult to simulate and optimize. We propose Multi-Layer Fully-Connected Variational Quantum Circuits (FC-VQC), a modular framework that decomposes high-dimensional inputs into fixed-size local VQC blocks connected by deterministic block-mixing rules. This design keeps each quantum computation local while allowing the number of trainable quantum parameters to scale linearly with input dimension. We evaluate FC-VQC across tabular regression, tabular classification, and spatio-temporal BSDE/PDE approximation. Across the evaluated tasks, FC-VQC improves over monolithic VQC baselines and achieves competitive or improved performance relative to structure-matched deep neural network (DNN) baselines, while using substantially fewer trainable parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16623 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits Su, Howard Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Tseng, Huan-Hsin Quantum Physics Variational Quantum Circuits (VQC) are promising models for quantum machine learning, but standard monolithic architectures face an expressivity--trainability dilemma: small circuits can be under-parameterized, while larger circuits are difficult to simulate and optimize. We propose Multi-Layer Fully-Connected Variational Quantum Circuits (FC-VQC), a modular framework that decomposes high-dimensional inputs into fixed-size local VQC blocks connected by deterministic block-mixing rules. This design keeps each quantum computation local while allowing the number of trainable quantum parameters to scale linearly with input dimension. We evaluate FC-VQC across tabular regression, tabular classification, and spatio-temporal BSDE/PDE approximation. Across the evaluated tasks, FC-VQC improves over monolithic VQC baselines and achieves competitive or improved performance relative to structure-matched deep neural network (DNN) baselines, while using substantially fewer trainable parameters. |
| title | Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.16623 |