Stony Brook University
Title: Hybrid quantum-classical methods for amplitude estimation for current and near future quantum devices
Date: Friday, October 16, 2020
Place and Time: Zoom, 3:35-4:25 pm
Abstract. The field of quantum computing, although still in its nascent phase, has seen some significant progress since its inception in the 1980s. However, quantum noise is still a major obstacle for today?s quantum machines, often referred to as the Noisy Intermediate-Scale Quantum (NISQ) devices. Thus, it is important to design quantum computing algorithms that work around the limitations of the current NISQ devices. One of the most notable algorithms with several applications is the Quantum Amplitude Estimation (QAE) algorithm, but it is prohibitively expensive for NISQ devices. We compare two recently proposed quantum-classical variants of this algorithm, namely the maximum likelihood estimation and the iterative amplitude estimation, by implementing them on the IBM Quantum machine using Qiskit, an open source framework for quantum computing. We analyze and discuss the advantages of each algorithm from the point of view of their implementation and performance on a quantum computer.