Mitigating correlated noise in quantum machines

Abstract

Today's quantum computers overwhelmingly suffer from complex noise, often referred to as non-Markovian or correlated noise. It has been a formidable challenge to mitigate or even characterize such noise and remains a central obstacle in the way of building fault-tolerant quantum computers. This proposed project will develop tools to suppress complex noise. In the last five years, PI Modi has developed a comprehensive theory to characterize complex correlated noise. Recently, PI Modi and PI HIll tested this theory on several IBM quantum computers to nd remarkable agreement between theory and experiment, with 99.9% fidelity for the characterization of non-Markovian noise. High- fidelity noise-characterization is the fi rst step towards being able to mitigate and control noise. In this proposed project, we will design control pulses, conditionally on the past gates, to maneuver around complex noise. The main risk with this approach is the high degree of complexity. We will tame this complexity by leveraging the recent theoretical advances that quantify the length and strength of non-Markovian noise. Moreover, we have tailored machine learning methods to characterize the noise, which too will expedite the task of pulse design. The proposed project will result in cleaner quantum computers with longer coherence times, which in turn will play a crucial role in the design of future machines.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 21, 2022
Source ID
FA95502110023XX0

Entities

People

  • Kavan Modi

Organizations

  • Air Force Office of Scientific Research
  • Monash University
  • United States Air Force

Tags

Fields of Study

  • Physics

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Distributed Systems and Data Platform Development
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Quantum Computing