Develop a Quantum Based Time Scheduling Algorithm for Digital Microfluidic Biochips

The development of microfluidic biochips has been one of the fastest-growing study fields in recent years. Microfluidic biochips are guiding the miniaturization of laboratory-based bioprotocols on a tiny chip. All bioprotocols, with the exception of dilution, include a stage called “sample preparation” that allows for the blending of several reagents in a specific volumetric ratio. It calls for mixing and storing a number of reagent fluids for real-time implementation, which offers a cheap and dependable method for on-chip sample preparation. Concentrate on minimizing reagent consumption, reducing fluid waste, achieving an accurate volumetric ratio, maximizing resource utilization, minimizing dilution time, and minimizing cycle count during sample preparation because many scheduling algorithms have been developed for performing sequences of mixing operations. To obtain a fixed concentration value, some of the reactants are blended and diluted in the appropriate collection. In practice, a reservoir switching operation takes longer than a mixing operation because it involves unloading, washing, and loading. The reservoir-constrained optimal scheduling (ROS) algorithm proposed a workaround to resolve the switching operation. This algorithm is used to speed up reservoir changeover, although more storage units are needed as a result. The cost of chip manufacture limits the number of storage units in real-time implementation. Hence, we propose a time quantum-based scheduling algorithm for digital microfluidic biochips to meet the storage constraint while reducing the switching count. The time it requires to execute a dilution operation is symbolized by the term “mixing time,” and the term “tree” represents the preparation of a sample. The proposed algorithm focuses on the mixing graph and scheduling algorithm.