Definition
A hybrid quantum–classical algorithm is a computational scheme in which a quantum computer and a classical computer collaborate to solve a problem. The quantum circuit handles subroutines that can benefit from quantum parallelism or entanglement, while the classical computer manages optimization, control, or data preprocessing.
Related Information
- Hybrid approaches are the most widely used in the NISQ (Noisy Intermediate-Scale Quantum) era, since they reduce the quantum resource requirements and compensate for noise with classical computation.
- The best-known examples are Variational Quatum Algorithms (VQAs), such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA).
- Hybrid strategies are central to Quantum Machine Learning (QML), where quantum circuits generate features or cost functions and classical optimizers update parameters.
Challenges
- Optimization bottlenecks: classical optimizers may converge slowly or get stuck in local minima.
- Noise sensitivity: hybrid algorithms must balance the limits of current hardware with the accuracy needed for training or optimization.
- Scalability: as systems grow, ensuring that hybrid loops remain efficient is an open challenge.
Frequently Asked Questions
- Why not perform the entire algorithm on the quantum computer? Current quantum devices are noisy and limited in qubit count. Hybrid algorithms maximize usefulness by combining the strengths of both paradigms.
- Will hybrid algorithms still be relevant in the fault-tolerant era? Yes. Even with error-corrected quantum computers, hybrid methods will remain important, since many workflows (e.g., in ML and optimization) integrate naturally with classical systems.