Quantum state estimation when qubits are lost: a no-data-left-behind approach

Abstract

We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean estimate (BME) for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the BME for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity.

Keywords

• quantum state estimation
• Bayesian
• qubit
• inference
• slice sampling
• Monte Carlo