JigSaw: Boosting Fidelity of NISQ Programs via Measurement Subsetting
Poulami Das, Swamit Tannu, Moinuddin Qureshi
Abstract
Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most error-prone operations (with an average error-rate of 4%) and often limit the size of quantum programs that can be run reliably on these systems. As quantum programs create and manipulate correlated states, all the program qubits are measured in each trial and thus, the severity of measurement errors increases with the program size. The fidelity of quantum programs can be improved by reducing the number of measurement operations.
Topics & Concepts
FidelityComputer scienceQubitBoosting (machine learning)Quantum computerQuantumAlgorithmLimit (mathematics)High fidelityQuantum algorithmComputer engineeringSoftwareTheoretical computer scienceTraining setNoisy dataObservational errorWorkflowQuantum error correctionData miningQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyAdvancements in Semiconductor Devices and Circuit Design