Adaptive Reconstruction of Bosonic Quantum States
2026-08-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new method to measure how close a bosonic quantum system's state is to a group of related target states, rather than just one. Their technique uses smart guessing and learning from measurements to pick the best points in phase space to sample, making the process faster and more efficient. They tested this on a circuit quantum electrodynamics setup with Schrödinger cat states and showed it works well even when the states are shifted or rotated. This method also helps improve quantum control by feeding fidelity information into optimization routines.
bosonic quantum systemsstate tomographyfidelity estimationWigner functionphase spaceSchrödinger cat statesBayesian inferencecircuit quantum electrodynamicsquantum optimal controladaptive sampling
Authors
Vasilisa Usova, Phila Rembold, Ian Yang, Marco Rossignolo, Simone Montangero, Samuele Tosatto, Gerhard Kirchmair
Abstract
Bosonic quantum systems provide a hardware-efficient platform for quantum information processing but remain challenging to characterise due to their large Hilbert space and the high measurement cost of state tomography. Existing approaches estimate the fidelity with respect to a single target state, making them unsuitable for applications in which physically equivalent states differ by phase space translations, rotations, or other transformations. Here, we introduce an adaptive reconstruction technique that estimates the fidelity with respect to a family of bosonic states while reconstructing the underlying Wigner function from a small number of measurements. The method combines a physics-informed parametric model with Bayesian inference, bootstrap, and active learning to iteratively select the most informative phase space sampling points. We implement the approach on a circuit quantum electrodynamics platform and benchmark it on Schrödinger cat states with amplitudes $α\in[1,3]$. The reconstruction yields reproducible fidelity estimates within a few minutes, remains robust to substantial displacements and rotations in phase space despite using a mismatched prior, and is sensitive to subtle state imperfections. We further compare the adaptive strategy with existing Wigner function sampling protocols experimentally, demonstrating the advantage of adaptive sampling for measurement-efficient fidelity estimation with respect to a family of cat states. Finally, we incorporate the reconstructed fidelity into the figure of merit used in a proof-of-principle closed-loop quantum optimal control experiment, demonstrating the applicability of the method to autonomous optimisation of bosonic quantum states.