Quantum Measurement Discrimination using Cumulative Distribution Functions
Zachery Utt, Daniel Volya, Prabhat Mishra · 2023
Quantum measurement is one of the critical steps in quantum computing that determines the probabilities associated with qubit states after conducting several circuit ex-ecutions and measurements. As a mesoscopic quantum system, real quantum computers are prone to noise. Therefore, a major challenge in quantum measurement is how to correctly inter-pret the noisy results of a quantum computer. While there are promising classification based solutions, they either produce incorrect results (misclassify) or require many measurements (expensive). In this paper, we present an efficient technique to estimate a qubit's state through analysis of probability distributions of post-measurement data. Specifically, we estimate the state of a qubit using cumulative distribution functions to compare the measured distribution of a sample with the distributions of basis states$\vert 0\rangle$and$\vert 1\rangle$. Our experimental results demonstrate a drastic reduction (78%) in single qubit readout error. It also provides significant reduction (12%) when used to boost existing multi-qubit discriminator models.