Machine learning

Quantum machine learning (QML) is an emerging field that combines quantum computing and machine learning to explore new ways of analyzing data, recognizing patterns, and solving complex computational problems. It applies concepts from quantum mechanics, such as superposition and entanglement, to develop quantum algorithms and machine learning models that can run on quantum computers or integrate quantum and classical computing. Research in QML includes quantum neural networks, quantum kernel methods, and hybrid quantum-classical algorithms, which use classical computers to optimize models while quantum processors perform specific computations. Although QML might offer advantages for certain tasks, practical benefits over classical machine learning remain an active area of research.

Application functions

• Singularity Machine Learning by Multiverse Computing

  A hybrid quantum-classical classification solution that combines classical ensemble methods with quantum algorithms such as QAOA to optimize model diversity and generalization for classification tasks.

  Browse documentation