Limitations for adaptive quantum state tomography in the presence of detector noise

Abstract

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.

10 pages and 6 figures + appendix 9 pages and 6 figures, updated after review

References in corpus (11)

• Array Programming with NumPy
• Quantum Error Mitigation
• Measuring measurement
• Quantum channels and their entropic characteristics
• Self-guided quantum tomography
• Adaptive experimental design for one-qubit state estimation with finite data based on a statistical update criterion
• Adaptive quantum tomography
• Readout error mitigated quantum state tomography tested on superconducting qubits
• Quantum states tomography with noisy measurement channels
• Adaptive measurement strategy for noisy quantum amplitude estimation with variational quantum circuits
• Benchmarking the quality of multiplexed qubit readout beyond assignment fidelity