Michael Hersche, Mustafa Zeqiri, et al.
Nature Machine Intelligence
An important step in calibration and control is Hamiltonian learning, which involves learning the parameters given a Hamiltonian model and description of noise sources through queries to the quantum system. Standard techniques require ๐(๐โ2) queries to achieve learning error ๐ due to the standard quantum limit. To minimize the number of queries required and improve the scaling with ๐, we propose a Hamiltonian active learner based on Fisher information (โHAL-FIโ). Each input query specifies the initial state, measurement operator and interaction time, and the resulting output is a single shot binary valued measurement. Seeded with data from an initial set of queries, HAL-FI optimizes subsequent queries. Performance of HAL-FI is evaluated on a two-qubit cross-resonance gate on a 20-qubit IBM Quantum device, using Qiskit Pulse to model readout noise, imperfect pulse-shaping and decoherence. HAL-FI realizes a 27% reduction in resource requirements over an uniformly random approach, with an order of magnitude reduction over quantum process tomography. Moreover, by spacing out queries non-uniformly in time, HAL-FI can achieve learning error which scales inversely with the number of queries, meeting the Heisenberg bound.
Michael Hersche, Mustafa Zeqiri, et al.
Nature Machine Intelligence
Andrew Eddins, Tanvi Gujarati, et al.
APS March Meeting 2021
Jiri Stehlik, David Zajac, et al.
APS March Meeting 2021
Guglielmo Mazzola, Simon Mathis, et al.
APS March Meeting 2021