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.

并行量子退火通过在单个退火周期中处理多个独立问题来优化量子拓扑结构上可用量子位的利用，从而最小化闲置量子位和比传统量子退火方法提供更快的解决时间。