Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries

In this introduction, we discuss the general methodology of quantum computing based on unitary transformations (gates) of quantum registers, which underpin the potential advancements in computational power over classical systems. We introduce the unique properties of quantum bits, or qubits, quantum calculations implemented by algorithms that evolve qubit states through unitary transformations, followed by measurements that collapse the superposition states to produce specific outcomes, and lastly the challenges faced in practical quantum computing limited by noise, with hybrid approaches that integrate quantum and classical computing to address current limitations. This introductory discussion sets the stage for a deeper exploration into quantum computing for machine learning applications in subsequent sections. Calculations with quantum computers generally require evolving the state of a quantum register by applying a sequence of pulses that implement unitary transformations according to a designed algorithm. A measurement of the resulting quantum state then collapses the coherent state, yielding a specific outcome of the calculation. To obtain reliable results, the process is typically repeated thousands of times, with averages taken over all of the measurements to account for quantum randomness and ensure statistical accuracy. This repetition is essential to achieve convergence, as each individual measurement only provides probabilistic information about the quantum state. Quantum registers are commonly based on qubits. Like classical bits, qubits can be observed in either of two possible states (0 or 1).