Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915308226215936 |
|---|---|
| author | Tecot, Lucas Luo, Di Hsieh, Cho-Jui |
| author_facet | Tecot, Lucas Luo, Di Hsieh, Cho-Jui |
| contents | Advancements in quantum computing have spurred significant interest in harnessing its potential for speedups over classical systems. However, noise remains a major obstacle to achieving reliable quantum algorithms. In this work, we present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers. Our method, with a natural connection to Evolutionary Strategies, guarantees resilience to parameter noise with minimal adjustments to commonly used optimization algorithms. Our approach is function-agnostic and adaptable to various quantum circuits, successfully demonstrated in quantum phase classification tasks. By developing provably guaranteed optimization theory with quantum circuits, our work opens new avenues for practical, robust applications of near-term quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Tecot, Lucas Luo, Di Hsieh, Cho-Jui Quantum Physics Machine Learning Computational Physics Advancements in quantum computing have spurred significant interest in harnessing its potential for speedups over classical systems. However, noise remains a major obstacle to achieving reliable quantum algorithms. In this work, we present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers. Our method, with a natural connection to Evolutionary Strategies, guarantees resilience to parameter noise with minimal adjustments to commonly used optimization algorithms. Our approach is function-agnostic and adaptable to various quantum circuits, successfully demonstrated in quantum phase classification tasks. By developing provably guaranteed optimization theory with quantum circuits, our work opens new avenues for practical, robust applications of near-term quantum computers. |
| title | Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise |
| topic | Quantum Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2505.18478 |