Abstract. We propose two experimental schemes for quantum state discrimination that achieve the optimal tradeoff between the probability of correct identification and the disturbance on the quantum state. 1.
Typical measurement setups in quantum systems are destructive, meaning that states are irretrievably altered after measurement. In this work, we analyse non-destructive discrimination of two two-mode squeezed vacuum states using local Gaussian measurements. We investigate a tradeoff relation between the success probability of discrimination and the fidelity of the resulting state with the initial state, and construct a protocol given by local Gaussian measurements, which is optimal within our numerically explored class. We also extend to the case where we allow for additional pre-shared entanglement, and show that this regime allows us to exceed the standard local Gaussian bound for the fidelity-success probability tradeoff. Our work provides a natural extension of the tradeoff between information gain and disturbance in entangled-state discrimination, previously established for finite-dimensional quantum systems, to infinite-dimensional continuous-variable systems.
This paper addresses the minimal disturbance quantum discrimination among a set of N pure states which are symmetrically distributed on a Bloch sphere equator. The optimal trade-off between the discrimination probability and the state disturbance is exactly derived. The minimal disturbance discrimination which saturates the trade-off turns out to be a convex combination of the minimum error discrimination and the identity maps.
Sequential quantum information processing may lie in the peaceful coexistence of no-go theorems on quantum operations, such as the no-cloning theorem, the monogamy of correlations, and the no-signalling principle. In this work, we investigate a sequential scenario of quantum state discrimination with maximum confidence, called maximum-confidence discrimination, which generalizes other strategies including minimum-error and unambiguous state discrimination. We show that sequential state discrimination with equally high confidence can be realized only when positive-operator-valued measure elements for a maximum-confidence measurement are linearly independent; otherwise, a party will have strictly less confidence in measurement outcomes than the previous one. We establish a tradeoff between the disturbance of states and information gain in sequential state discrimination, namely, that the less a party learn in state discrimination in terms of a guessing probability, the more parties can participate in the sequential scenario.