Discrimination Power of a Quantum Detector

We investigate the ability of a quantum measurement device to discriminate two states or, generically, two hypotheses. In full generality, the measurement can be performed a number $n$ of times, and arbitrary preprocessing of the states and postprocessing of the obtained data are allowed. There is an intrinsic error associated with the measurement device, which we aim to quantify, that limits its discrimination power. We minimize various error probabilities (averaged or constrained) over all pairs of $n$-partite input states. These probabilities, or their exponential rates of decrease in the case of large $n$, give measures of the discrimination power of the device. For the asymptotic rate of the averaged error probability, we obtain a Chernoff-type bound, dual to the standard Chernoff bound for which the state pair is fixed and the optimization is over all measurements. The key point in the derivation is that identical copies of input states become optimal in asymptotic settings. Optimal asymptotic rates are also obtained for constrained error probabilities, dual to Stein’s lemma and Hoeffding’s bound. We further show that adaptive protocols where the state preparer gets feedback from the measurer do not improve the asymptotic rates. These rates thus quantify the ultimate discrimination power of a measurement device.

Figure 1

Adaptive protocol. At each step (left to right), Alice sends to Bob (solid arrows) the state in Eq. (5), which she has prepared using Bob’s feedback (dashed arrows).