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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.25193 |
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| _version_ | 1866914513260904448 |
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| author | Keshvari, Arvin Tuxbury, William Lin, Zin |
| author_facet | Keshvari, Arvin Tuxbury, William Lin, Zin |
| contents | Inverse design has made vast physical parameter spaces a substrate for emergent behavior. In sensing, the stakes of this principle are sharpest at the analog-to-digital boundary, where any information the hardware fails to capture is information no downstream algorithm can recover; hardware optimization alone is therefore not enough, and the geometry must be co-designed with a rule for what to measure next. We formulate this co-design as \emph{joint dynamic programming} (joint-DP): a single optimization over the continuous hardware geometry and a Bellman-optimal adaptive measurement policy. The outer hardware gradient is computed by differentiable dynamic programming with a sharp Bellman maximum, which the envelope theorem makes exact and bias-free, and a relaxation hierarchy carries the common framework from small discrete POMDPs to $10^5$-pixel photonic topologies. Across three case studies, joint-DP beats the natural baseline of its community by a large factor: on a radar beam-search POMDP, classical information-bound-guided geometry selection loses $2.8\times$ in attainable adaptive value; on a superconducting-qubit flux sensor, joint-DP reduces deployed mean-squared error by $11.3\times$ over the joint Bayesian Cramér--Rao baseline, with both geometries numerically co-optimized under their respective objectives; and on a $90{,}000$-pixel photonic metasensor, joint-DP via the Bayesian Fisher-information-matrix surrogate reduces deployed mean-squared error by $123\times$ relative to a randomized baseline. For any sensor whose hardware is designed once but whose policy runs for the device's lifetime, joint optimization of hardware and policy is the minimum principled procedure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25193 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference Keshvari, Arvin Tuxbury, William Lin, Zin Optics Optimization and Control Computational Physics Data Analysis, Statistics and Probability Quantum Physics Inverse design has made vast physical parameter spaces a substrate for emergent behavior. In sensing, the stakes of this principle are sharpest at the analog-to-digital boundary, where any information the hardware fails to capture is information no downstream algorithm can recover; hardware optimization alone is therefore not enough, and the geometry must be co-designed with a rule for what to measure next. We formulate this co-design as \emph{joint dynamic programming} (joint-DP): a single optimization over the continuous hardware geometry and a Bellman-optimal adaptive measurement policy. The outer hardware gradient is computed by differentiable dynamic programming with a sharp Bellman maximum, which the envelope theorem makes exact and bias-free, and a relaxation hierarchy carries the common framework from small discrete POMDPs to $10^5$-pixel photonic topologies. Across three case studies, joint-DP beats the natural baseline of its community by a large factor: on a radar beam-search POMDP, classical information-bound-guided geometry selection loses $2.8\times$ in attainable adaptive value; on a superconducting-qubit flux sensor, joint-DP reduces deployed mean-squared error by $11.3\times$ over the joint Bayesian Cramér--Rao baseline, with both geometries numerically co-optimized under their respective objectives; and on a $90{,}000$-pixel photonic metasensor, joint-DP via the Bayesian Fisher-information-matrix surrogate reduces deployed mean-squared error by $123\times$ relative to a randomized baseline. For any sensor whose hardware is designed once but whose policy runs for the device's lifetime, joint optimization of hardware and policy is the minimum principled procedure. |
| title | Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference |
| topic | Optics Optimization and Control Computational Physics Data Analysis, Statistics and Probability Quantum Physics |
| url | https://arxiv.org/abs/2604.25193 |