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Error Management on Near-Term Quantum Computers

Date Issued
December 1, 2024
Author(s)
Gowrishankar, Meenambika  
Advisor(s)
Travis S. Humble
Additional Advisor(s)
Rebekah Herrman
James Ostrowski
Himanshu Thapliyal
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/19563
Abstract

Quantum computing devices are developing at a rapid pace with error rates that are on the cusp of early fault tolerance. The promise quantum computing offers for quantum chemistry is exponential speed up in simulations of large scale molecular systems with enough accuracy to predict outcomes of experiments in the real world. Delivering on the promise of high accuracy and precision requires methods to evaluate the computational accuracy of the quantum computing devices. We develop and demonstrate a device agnostic framework to benchmark the computational accuracy of near-term noisy intermediate scale quantum computing (NISQ) devices using a quantum chemistry application and the domain specific threshold of chemical accuracy. We use numerical simulations of noisy quantum circuits to model the impact of noise on the accuracy and variance of the energy estimate, and the fidelity of the prepared state when simulating the ground state of prototypical two-electron chemistry problems using a near-term quantum-classical algorithm, the variational quantum eigensolver (VQE). We use device agnostic error-mitigation schemes, quantum error detection (QED) and read out error detection, with post-selection, to mitigate different types of noise and show improvement in the accuracy of the results along with the impact on the precision due to post-selection. With this framework of evaluating the computational accuracy of a quantum computing device we estimate the resources required to perform the benchmark and demonstrate the analysis for a commercially available trapped-ion device.

Subjects

Quantum Computing

Quantum Noise

Quantum Error Detecti...

Quantum Algorithms

Quantum Chemistry

Quantum Simulation

Disciplines
Quantum Physics
Theory and Algorithms
Degree
Doctor of Philosophy
Major
Energy Science and Engineering
File(s)
Thumbnail Image
Name

Disseration_Revised_MGv0.2.pdf

Size

3.55 MB

Format

Adobe PDF

Checksum (MD5)

9b9a2c50f49334fef391df762da258c7

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