The International Data Corporation’s report forecasts a 48% compound annual growth rate for the quantum machine learning market through 2030. It examines hardware advancements, hybrid variational algorithms, and open-source frameworks driving enterprise QML adoption in pharmaceuticals, finance, and logistics.
Key points
- IDC forecasts a 48% CAGR for the QML market, reaching $8.6 billion by 2027.
- Hybrid variational algorithms (VQE, QAOA) enable near-term QML use cases on NISQ hardware.
- Open-source frameworks like PennyLane and Qiskit democratize enterprise access to quantum computing.
Q&A
- What is quantum machine learning?
- How do hybrid quantum-classical algorithms work?
- What factors drive QML market growth?
- What are current hardware limitations?
