A multi-framework engineering study of Quantum Circuit Born Machines across CPU and GPU simulation backends
As AI demands increasingly complex and high-quality data, quantum generative models offer a new approach to creating structured synthetic datasets.
Quantum Circuit Born Machines (QCBMs) offer a promising approach to modeling complex probability distributions and generating structured synthetic data for data-intensive domains such as healthcare, life sciences, finance, insurance, and chemistry.
This POV explores how different quantum simulation frameworks perform when building QCBM-based synthetic data pipelines—and what engineering teams should consider when choosing the right approach.
What’s inside the POV?
Get a closer look at how quantum generative models can move from experimental circuits toward practical synthetic data pipelines. The PoV explores QCBM-based synthetic data generation, including:
QCBM architecture and data encoding
CPU vs. GPU simulation approaches
Comparison of four quantum frameworks
Training and sampling performance benchmarks
Synthetic data quality evaluation
Scalability considerations and future directions
Ready to go from qubits to datasets?
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