arXiv:2508.12413v4 Announce Type: replace-cross Abstract: The flow matching has rapidly become a dominant paradigm in classical generative modeling, offering an efficient way to interpolate between two complex distributions. We extend this idea to the quantum realm and introduce the Quantum Flow Matching (QFM), a quantum-circuit realization that offers efficient interpolation between two density matrices. QFM offers systematic preparation of density matrices and generation of samples for accurately estimating observables, and can be realized on quantum computers without the need for costly cir
The continuous advancements in classical generative modeling, particularly flow matching, are naturally inspiring quantum computing researchers to explore analogous quantum paradigms.
This development represents a significant step towards practical quantum generative AI, potentially enabling more efficient simulation, optimization, and quantum algorithm development.
The ability to efficiently interpolate between quantum density matrices opens new avenues for quantum algorithm design and the systematic preparation of quantum states.
- · Quantum computing companies
- · AI researchers
- · Pharmaceuticals (drug discovery)
- · Materials science
- · Classical simulation methods
- · Companies without quantum expertise
Research in quantum generative models accelerates, leading to more sophisticated quantum machine learning applications.
New capabilities in quantum chemistry and materials science emerge due to enhanced state preparation and observable estimation.
The development of truly general-purpose quantum AI agents becomes more feasible, impacting multiple scientific and industrial sectors.
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Read at arXiv cs.LG