SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

2024-12-10 · Source: cs.AI updates on arXiv.org · Field: Science & Research — Mathematics & Computational Sciences, Physical Sciences & Chemistry · Depth: Expert, long

Summary

SymQNet is a novel amortized reinforcement-learning approach designed to significantly reduce acquisition latency in adaptive Hamiltonian learning, a critical process for calibrating quantum devices. Traditional Bayesian design rules, such as Fisher-information search and Bayesian active learning by disagreement (BALD), recompute acquisition policies after every posterior update, leading to substantial wall-clock costs. SymQNet addresses this by learning a posterior-conditioned acquisition policy offline, then employing a fast policy forward pass online while maintaining Bayesian posterior feedback. On transverse-field Ising model (TFIM) benchmarks, SymQNet demonstrated substantial speedups. For five qubits, it reduced acquisition-only decision latency by 47.1× and 72.6× compared to bounded two-step BALD and bounded Fisher-information search, respectively. At twelve qubits, full simulated steps for SymQNet took 1.02 s, significantly faster than 13.27 s for bounded two-step BALD and 22.32 s for bounded Fisher-information search, representing reductions of roughly 6.4×10³ and 1.1×10´ in decision latency. This makes adaptive Hamiltonian learning practical for low-latency workloads.

Key takeaway

For AI Scientists and Research Scientists developing quantum device calibration, SymQNet offers a path to overcome the high latency of adaptive Hamiltonian learning. You should consider integrating amortized reinforcement learning, specifically offline policy training, to achieve significant speedups in acquisition decisions. This approach preserves Bayesian posterior feedback while enabling low-latency, repeated workloads, making quantum device characterization more efficient. Explore training with rewards directly tied to final MSE for improved accuracy.

Key insights

Amortized reinforcement learning can drastically reduce latency in adaptive quantum Hamiltonian learning by pre-training acquisition policies.

Principles

• Offline policy training enables fast online acquisition.
• Information gain serves as a dense, posterior-local reward.
• Bayesian filtering can be preserved with learned acquisition.

Method

SymQNet trains an RL policy using PPO offline, reading SMC belief state, graph context, and measurement history to select the next qubit, basis, and evolution time. Online, it uses a fast policy forward pass for acquisition.

In practice

• Use PPO for training acquisition policies.
• Employ VAEs and graph layers for complex state encoding.
• Evaluate with final MSE, not just information gain.

Topics

• Adaptive Hamiltonian Learning
• Quantum Device Calibration
• Reinforcement Learning
• Bayesian Experimental Design
• Quantum Computing
• Low-Latency Systems

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.