  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.
