Hybrid quantum-classical algorithms are computational approaches that leverage both classical computing and quantum computing to solve complex problems. In these algorithms, quantum processors are used for specific tasks where they can provide advantages, such as solving optimization problems or performing simulations, while classical processors handle the remainder of the computations. This combination is particularly useful since current quantum computers have limitations in terms of qubit count, error rates, and coherence times, making them suitable primarily for certain tasks rather than all computational processes.
A well-known example of a hybrid quantum-classical algorithm is the Variational Quantum Eigensolver (VQE). VQE is used to find the ground state energy of quantum systems, a task important in fields such as chemistry and materials science. In this algorithm, a classical computer optimizes the parameters of a quantum circuit designed to approximate the quantum state. The classical processor runs an optimization algorithm, continuously adjusting the parameters and querying the quantum processor to evaluate the energy until it converges on the best solution. This interplay between classical and quantum resources allows developers to make the most of the limited capabilities of current quantum hardware.
Another example is the Quantum Approximate Optimization Algorithm (QAOA), which focuses on solving combinatorial optimization problems. QAOA employs a classical optimization routine to maximize a cost function while using a quantum circuit to represent the problem solution. By working together, classical and quantum elements can capitalize on their strengths: classical systems excel at managing higher-level computations and resource allocation, while quantum systems can explore large solution spaces more efficiently than classical algorithms alone. This synergy provides a practical way for developers to harness quantum computing effectively, even as the technology matures.