An Efficient Quantum Circuit for Flow Model Execution Using Quantum Neural Networks

Key points

• Flow matching learns this velocity field by modeling the transport dynamics between the two distributions.1
• In this paper, we investigate accurate and efficient quantum simulation of the wavefunction flow, thereby realizing the efficient implementation of flow models on quantum computers.1
• We first leverage a quantum read-only memory (QROM)-based phase kickback framework for the wavefunction flow simulation, generating probability densities that closely match those produced by the corresponding conventional flow model.1
• To address the high circuit-resource cost, we further incorporate a trained quantum neural network (QNN) into the phase kickback framework, replacing QROM for data encoding.1

Sources (1)

• [1]An Efficient Quantum Circuit for Flow Model Execution Using Quantum Neural Networks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 09:07 AM

  Wavefunction flow establishes a formal connection between flow models and quantum dynamics by introducing a continuity Hamiltonian, which drives the Schrödinger evolution of quantum states.

  Flow matching learns this velocity field by modeling the transport dynamics between the two distributions.

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