Proof-of-principle experimental demonstration of quantum gate verification

To employ a quantum device, the performance of the quantum gates in the device needs to be evaluated first. Since the dimensionality of a quantum gate grows exponentially with the number of qubits, evaluating the performance of a quantum gate is a challenging task. Recently, a scheme called quantum gate verification (QGV) has been proposed, which can verifies quantum gates with near-optimal efficiency. In this work, we implement a proof-of-principle optical experiment to demonstrate this QGV scheme. We show that for a single-qubit quantum gate, only $sim400$ samples are needed to confirm the fidelity of the quantum gate to be at least $97%$ with a $99%$ confidence level using the QGV method, while at least $sim5000$ samples are needed to achieve the same result using the standard quantum process tomography method. The QGV method validated by this work has the potential to be widely used for the evaluation of quantum devices in various quantum information applications.

Direct Fidelity Estimation of Quantum States using Machine Learning

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets the expectations. In this paper, we propose a new approach to solve this problem using machine learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $pm1%$ precision in a non-adversarial scenario. This machine learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.