arXiv:2606.12808v1 Announce Type: cross Abstract: 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
The increasing complexity and scale of quantum device calibration highlight the need for more efficient adaptive learning methods, which traditional Bayesian approaches struggle to provide in real-time.
This development addresses a critical bottleneck in quantum computing, speeding up the calibration and characterization of quantum devices, which is essential for scaling and practical applications.
Adaptive Hamiltonian learning processes, previously burdened by high latency, can now be significantly accelerated through amortized reinforcement learning, enabling more dynamic and efficient quantum device control.
- · Quantum computing researchers
- · Quantum hardware manufacturers
- · AI/ML for scientific discovery sector
- · Traditional Bayesian optimization methods in quantum control
Faster and more reliable quantum experimentation and device development.
Accelerated progress in quantum algorithm development and quantum error correction.
Potential for earlier commercialization and widespread adoption of quantum technologies across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI