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Investigation into the Potential of Parallel Quantum Annealing for Simultaneous Optimization of Multiple Problems: A Comprehensive Study

Arit Kumar Bishwas, Anuraj Som, Saurabh Choudhary

TL;DR

Insight is provided into the potential and limitations of this parallelization method, which holds promise for substantial speed-up, as indicated by the Time-to-Solution (TTS) metric, compared to traditional quantum annealing, which solves problems sequentially and may leave qubits unutilized.

Abstract

Parallel Quantum Annealing is a technique to solve multiple optimization problems simultaneously. Parallel quantum annealing aims to optimize the utilization of available qubits on a quantum topology by addressing multiple independent problems in a single annealing cycle. This study provides insights into the potential and the limitations of this parallelization method. The experiments consisting of two different problems are integrated, and various problem dimensions are explored including normalization techniques using specific methods such as DWaveSampler with Default Embedding, DWaveSampler with Custom Embedding and LeapHybridSampler. This method minimizes idle qubits and holds promise for substantial speed-up, as indicated by the Time-to-Solution (TTS) metric, compared to traditional quantum annealing, which solves problems sequentially and may leave qubits unutilized.

Investigation into the Potential of Parallel Quantum Annealing for Simultaneous Optimization of Multiple Problems: A Comprehensive Study

TL;DR

Insight is provided into the potential and limitations of this parallelization method, which holds promise for substantial speed-up, as indicated by the Time-to-Solution (TTS) metric, compared to traditional quantum annealing, which solves problems sequentially and may leave qubits unutilized.

Abstract

Parallel Quantum Annealing is a technique to solve multiple optimization problems simultaneously. Parallel quantum annealing aims to optimize the utilization of available qubits on a quantum topology by addressing multiple independent problems in a single annealing cycle. This study provides insights into the potential and the limitations of this parallelization method. The experiments consisting of two different problems are integrated, and various problem dimensions are explored including normalization techniques using specific methods such as DWaveSampler with Default Embedding, DWaveSampler with Custom Embedding and LeapHybridSampler. This method minimizes idle qubits and holds promise for substantial speed-up, as indicated by the Time-to-Solution (TTS) metric, compared to traditional quantum annealing, which solves problems sequentially and may leave qubits unutilized.
Paper Structure (27 sections, 6 equations, 24 figures)

This paper contains 27 sections, 6 equations, 24 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Quantum Annealing and QUBO:
  4. Parallel Annealing
  5. Advantages of parallel annealing
  6. Proposed methodology
  7. DWaveSampler with Default Embedding:
  8. DWaveSampler with Custom Embedding:
  9. LeapHybridSampler:
  10. Normalization
  11. Normalization techniques explored:
  12. Results and Discussions
  13. Metrics to determine correctness of solution:
  14. Solution Quality Value ($SQV$):
  15. Problem specific number of violations:
  16. ...and 12 more sections

Figures (24)

  • Figure 1: Framework of Parallel Quantum Annealing
  • Figure 2: Comparison of SQV for normalization techniques on DWaveSampler(Default Embedding) for QUBO Size: 26×26
  • Figure 3: Comparison of variation in solution for normalization techniques on DWaveSampler(Default Embedding) for QUBO Size: 26×26
  • Figure 4: Comparison of TTS for normalization techniques on DWaveSampler(Default Embedding) for QUBO Size: 26×26
  • Figure 5: Comparison of average number of violations for normalization techniques on DWaveSampler(Default Embedding) for QUBO Size: 26×26
  • ...and 19 more figures