The Research & Development (R&D) phase of drug development is a lengthy and costly process, usually spanning from six to nine years [1] and costing four hundred to fourteen hundred million USD [2]. To revolutionize this process, we introduce our new concept-the combination of Quantum-based Machine Learning network (QML) and Quantum Computing Simulation (QS)-to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. Our program takes the inputs of the target protein/gene structure and the primary essay [3]. For Hit Generation [3], the QML network generates possible hits [4] according to the molecular structure of the target protein while the QS filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization [3], the resultant molecules generated and filtered from QML and QS are compared, and the ones that appear as a result of both processes will be made into dozens of molecular variations, while others will only undergo simple modifications. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. Our concept of the combination of QML and QS can also prove revolutionary in many other fields, such as agriculture research, genetic editing, and even aerospace engineering.