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Quantum Computing and Visualization Research Challenges and Opportunities

E. Wes Bethel, Roel Van Beeumen, Talita Perciano

TL;DR

Research challenges and opportunities are examined along the path from initial feasibility to practical use of QC platforms applied to meaningful problems in the field of visualization.

Abstract

Quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.

Quantum Computing and Visualization Research Challenges and Opportunities

TL;DR

Research challenges and opportunities are examined along the path from initial feasibility to practical use of QC platforms applied to meaningful problems in the field of visualization.

Abstract

Quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.
Paper Structure (15 sections, 3 figures, 3 tables)

This paper contains 15 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. What is Quantum Computing?
  4. Is Quantum Computing Useful?
  5. Generations of Quantum Systems
  6. A Visualization Example: Quantum Feasibility and Practicality
  7. Quantum Practicality in Practice
  8. Quantum Visualization Processing
  9. Quantum Feasibility and Practicality in Visualization
  10. Data Challenges
  11. QC and Visualization Algorithms
  12. Visualization for Quantum Computing
  13. Sidebar: Summary of Research Challenges
  14. Conclusions
  15. Acknowledgment

Figures (3)

  • Figure 1: A canonical visualization pipeline: 3D data is input to a mapping process, in this case isocontouring, to transform real-valued data into surface geometry, which is then input to a rendering process that generates pixels that are then presented to a user.
  • Figure 2: Working with classical data on quantum platforms by definition requires a hybrid quantum--classical workflow. The classical platform performs data normalization, circuit preparation, and post-processing of quantum state measurement, while the quantum processor performs state initialization, quantum computation, and measurement.
  • Figure 3: In QC, entanglement occurs when the state of two qubits is linked: change in the state of one is "seen" by the other. Both cases show circuits that leverage entanglement and superposition to achieve actions in the circuit that are not possible in any classical sense where a change in one qubit's state affects others.