Concentration-Free Quantum Kernel Learning in the Rydberg Blockade
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912537850675200 |
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| author | Sarkar, Ayana Schnee, Martin Radgohar, Roya Fadaie, Mojde Drouin-Touchette, Victor Kourtis, Stefanos |
| author_facet | Sarkar, Ayana Schnee, Martin Radgohar, Roya Fadaie, Mojde Drouin-Touchette, Victor Kourtis, Stefanos |
| contents | Quantum kernel methods (QKMs) offer an appealing framework for machine learning on near-term quantum computers. However, QKMs generically suffer from exponential concentration, requiring an exponential number of measurements to resolve the kernel values, with the exception of trivial (i.e., classically simulable) kernels. Here we propose a QKM that is free of exponential concentration, yet remains hard to simulate classically. Our QKM utilizes the weak ergodicity-breaking many-body dynamics in the Rydberg blockade of coherently driven neutral atom arrays. We demonstrate the fundamental properties of our QKM by analytically solving an approximate toy model of its underpinning quantum dynamics, as well as by extensive numerical simulations on randomly generated datasets. We further show that the proposed kernel exhibits effective learning on real data. The proposed QKM can be implemented in current neutral atom quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10819 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Concentration-Free Quantum Kernel Learning in the Rydberg Blockade Sarkar, Ayana Schnee, Martin Radgohar, Roya Fadaie, Mojde Drouin-Touchette, Victor Kourtis, Stefanos Strongly Correlated Electrons Quantum Physics Quantum kernel methods (QKMs) offer an appealing framework for machine learning on near-term quantum computers. However, QKMs generically suffer from exponential concentration, requiring an exponential number of measurements to resolve the kernel values, with the exception of trivial (i.e., classically simulable) kernels. Here we propose a QKM that is free of exponential concentration, yet remains hard to simulate classically. Our QKM utilizes the weak ergodicity-breaking many-body dynamics in the Rydberg blockade of coherently driven neutral atom arrays. We demonstrate the fundamental properties of our QKM by analytically solving an approximate toy model of its underpinning quantum dynamics, as well as by extensive numerical simulations on randomly generated datasets. We further show that the proposed kernel exhibits effective learning on real data. The proposed QKM can be implemented in current neutral atom quantum computers. |
| title | Concentration-Free Quantum Kernel Learning in the Rydberg Blockade |
| topic | Strongly Correlated Electrons Quantum Physics |
| url | https://arxiv.org/abs/2508.10819 |