Maximum likelihood quantum state tomography is inadmissible
arXiv:1808.01072
Abstract
Maximum likelihood estimation (MLE) is the most common approach to quantum state tomography. In this letter, we investigate whether it is also optimal in any sense. We show that MLE is an inadmissible estimator for most of the commonly used metrics of accuracy, i.e., some other estimator is more accurate for every true state. MLE is inadmissible for fidelity, mean squared error (squared Hilbert-Schmidt distance), and relative entropy. We prove that almost any estimator that can report both pure states and mixed states is inadmissible. This includes MLE, compressed sensing (nuclear-norm regularized) estimators, and constrained least squares. We provide simple examples to illustrate why reporting pure states is suboptimal even when the true state is itself pure, and why "hedging" away from pure states generically improves performance.
References in corpus (12)
- On the Measurement of Qubits
- Quantum state tomography via compressed sensing
- Quantum state estimation
- Optimal, reliable estimation of quantum states
- Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
- Maximum Likelihood, Minimum Effort
- Quantum State Tomography via Linear Regression Estimation
- Experimental quantum compressed sensing for a seven-qubit system
- Hedged maximum likelihood estimation
- Behavior of the maximum likelihood in quantum state tomography
- Near-optimal quantum tomography: estimators and bounds
- Accurate quantum state estimation via "Keeping the experimentalist honest"
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