# SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

arXiv: https://arxiv.org/abs/2606.12808 · HTML: https://mlsanity.com/paper/2606.12808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design](https://mlsanity.com/paper/2103.02438/) (2021)
- [Optimizing Sequential Experimental Design with Deep Reinforcement Learning](https://mlsanity.com/paper/2202.00821/) (2022)
- [Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation](https://mlsanity.com/paper/2003.02183/) (2020)
- [Deep Bayesian Experimental Design for Quantum Many-Body Systems](https://mlsanity.com/paper/2306.14510/) (2023)

## Abstract (arXiv)

Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation. Bayesian design rules are recomputed after every posterior update, and that step can take seconds. Across hundreds of shots, those seconds become a significant wall-clock cost for adaptivity. We introduce SymQNet, an amortized reinforcement-learning approach for low-latency adaptive Hamiltonian learning. SymQNet learns a posterior-conditioned acquisition policy offline, then uses a fast policy forward pass online while retaining Bayesian posterior feedback. On transverse-field Ising benchmarks, SymQNet substantially reduces acquisition latency relative to bounded Fisher-information search and bounded two-step Bayesian active learning by disagreement (BALD). At five qubits, it reduces acquisition-only decision latency by 47.1x and 72.6x relative to these online baselines; at twelve qubits, full simulated steps take 1.02 seconds for SymQNet versus 13.27 seconds for bounded two-step BALD. Overall, we show that learned acquisition can make adaptive Hamiltonian learning practical for repeated low-latency workloads.