Adaptive Quantum State Tomography Improves Accuracy Quadratically.

Assumption-free reconstruction of quantum states from measurements is essential for benchmarking and certifying quantum devices, but it remains difficult due to the extensive measurement statistics and experimental resources it demands. An approach to alleviating these demands is provided by adaptive measurement strategies, which can yield up to a quadratic improvement in reconstruction accuracy for pure states by dynamically optimizing measurement settings during data acquisition. A key open question is whether these asymptotic advantages remain in realistic experiments, where readout is inevitably noisy. In this work, we analyze the impact of readout noise on adaptive quantum state tomography with readout-error mitigation, focusing on the challenging regime of reconstructing pure states using mixed-state estimators. Using analytical arguments based on Fisher information optimization and extensive numerical simulations using Bayesian inference, we show that any nonzero readout noise eliminates the asymptotic quadratic scaling advantage of adaptive strategies. We numerically investigate the behavior for finite measurement statistics for single- and two-qubit systems with exact readout-error mitigation and find a gradual transition from ideal to sub-optimal scaling. We furthermore investigate realistic scenarios where detector tomography is performed with a limited number of state copies for calibration, showing that insufficient detector characterization leads to estimator bias and limited reconstruction accuracy. Although our result imposes an upper bound on the reconstruction accuracy that can be achieved with adaptive strategies, we nevertheless observe numerically a constant-factor gain in reconstruction accuracy, which becomes larger as the readout noise decreases. This indicates potential practical benefits in using adaptive measurement strategies in well-calibrated experiments.Assumption-free reconstruction of quantum states from measurements is essential for benchmarking and certifying quantum devices, but it remains difficult due to the extensive measurement statistics and experimental resources it demands. An approach to alleviating these demands is provided by adaptive measurement strategies, which can yield up to a quadratic improvement in reconstruction accuracy for pure states by dynamically optimizing measurement settings during data acquisition. A key open question is whether these asymptotic advantages remain in realistic experiments, where readout is inevitably noisy. In this work, we analyze the impact of readout noise on adaptive quantum state tomography with readout-error mitigation, focusing on the challenging regime of reconstructing pure states using mixed-state estimators. Using analytical arguments based on Fisher information optimization and extensive numerical simulations using Bayesian inference, we show that any nonzero readout noise eliminates the asymptotic quadratic scaling advantage of adaptive strategies. We numerically investigate the behavior for finite measurement statistics for single- and two-qubit systems with exact readout-error mitigation and find a gradual transition from ideal to sub-optimal scaling. We furthermore investigate realistic scenarios where detector tomography is performed with a limited number of state copies for calibration, showing that insufficient detector characterization leads to estimator bias and limited reconstruction accuracy. Although our result imposes an upper bound on the reconstruction accuracy that can be achieved with adaptive strategies, we nevertheless observe numerically a constant-factor gain in reconstruction accuracy, which becomes larger as the readout noise decreases. This indicates potential practical benefits in using adaptive measurement strategies in well-calibrated experiments.

quant-pharXiv:2601.04020Physical Review AJan 8, 20260 citations

Adaptive Quantum State Tomography Improves Accuracy Quadratically

Dylan H. Mahler, Lee A. Rozema, Ardavan Darabi +3 more

We introduce a simple protocol for adaptive quantum state tomography, which reduces the worst-case infidelity [$1\ensuremath{-}F(\stackrel{^}{\ensuremath{\rho}},\ensuremath{\rho})$] between the estimate and the true state from $O(1/\sqrt{N})$ to $O(1/N)$ . It uses a single adaptation step and just one extra measurement setting. In a linear optical qubit experiment, we demonstrate a full order of magnitude reduction in infidelity (from 0.1% to 0.01%) for a modest number of samples ($N\ensuremath{\approx}3\ifmmode\times\else\texttimes\fi{}{10}^{4}$).We introduce a simple protocol for adaptive quantum state tomography, which reduces the worst-case infidelity [$1\ensuremath{-}F(\stackrel{^}{\ensuremath{\rho}},\ensuremath{\rho})$] between the estimate and the true state from $O(1/\sqrt{N})$ to $O(1/N)$. It uses a single adaptation step and just one extra measurement setting. In a linear optical qubit experiment, we demonstrate a full order of magnitude reduction in infidelity (from 0.1% to 0.01%) for a modest number of samples ($N\ensuremath{\approx}3\ifmmode\times\else\texttimes\fi{}{10}^{4}$).

arXiv:1303.0436Physical Review LettersOct 29, 2013141 citations

Adaptive quantum tomography

Evan Jerey, Joseph B. Altepeter, Nicholas A. Peters +4 more

OSA Trends in Optics and Photonics SeriesJan 1, 20040 citations

Adaptive quantum state tomography with iterative particle filtering

Syed Muhammad Kazim, Ahmad Farooq, Junaid ur Rehman +1 more

arXiv:2010.12867Quantum Information ProcessingOct 1, 20215 citations

O(1/\sqrt{N})

O(1/N)

. It uses a single adaptation step and just one extra measurement setting. In a linear optical qubit experiment, we demonstrate a full order of magnitude reduction in infidelity (from 0.1% to 0.01%) for a modest number of samples ($N\ensuremath{\approx}3\ifmmode\times\else\texttimes\fi{}{10}^{4}$).