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

arXiv: https://arxiv.org/abs/2403.05764 · HTML: https://mlsanity.com/paper/2403.05764/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [An All-Pair Quantum SVM Approach for Big Data Multiclass Classification](https://mlsanity.com/paper/1704.07664/) (2017)
- [Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis](https://mlsanity.com/paper/1705.09844/) (2017)

## Built on by (most cited first, 1 of 1)

- [Reducing Complexity for Quantum Approaches in Train Load Optimization](https://mlsanity.com/paper/2603.29543/) (2026)

## Abstract (arXiv)

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.