Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport

• URL: http://arxiv.org/abs/2606.21256v1
• Date: Fri, 19 Jun 2026 09:32:21 GMT
• Title: Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport
• Abstract summary: We introduce Intrinsic Flow Matching (IFM), a deterministic transport framework on $mathbbCPd-1$ that learns tangent velocity fields.<n>IFM replaces local score and reverse-time sampling with manifold probability flow, while horizontal parameterization removes ambient directions.<n> Empirically, IFM often improves over ambient Euclidean flow matching across higher-qubit, multimodal, spin-coherent, physics-inspired, and amplitude-encoded MNIST image-vector benchmarks.
• Score: 31.082191748525137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum pure-state ensembles live on complex projective space, making flat Euclidean generative modeling geometrically mismatched. We introduce Intrinsic Flow Matching (IFM), a deterministic transport framework on $\mathbb{CP}^{d-1}$ that learns tangent velocity fields using Pancharatnam phase-aligned conditional paths. IFM replaces local score teachers and reverse-time stochastic sampling with manifold probability flow, while horizontal parameterization removes redundant ambient directions. We show that the IFM objective recovers the induced marginal transport field, represents deterministic projective ensemble flows, and yields endpoint and stability guarantees. Empirically, IFM often improves over ambient Euclidean flow matching across higher-qubit, multimodal, spin-coherent, physics-inspired, and amplitude-encoded MNIST image-vector benchmarks, with strongest gains on high-dimensional and coherence-sensitive tasks but not uniformly across every metric.

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