The paper establishes a reverse-time quantum diffusion framework that generates complex quantum ensembles from simple distributions by deriving and learning a feedback Hamiltonian from forward trajectory data.
arXiv preprint arXiv:2401.07039 , year=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
IFM learns deterministic tangent velocity fields on CP^{d-1} via Pancharatnam phase-aligned paths, recovering marginal transport with endpoint and stability guarantees while showing empirical gains over Euclidean flow matching on quantum benchmarks.
A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.
Measurement-based quantum diffusion models are introduced to recover pure and mixed quantum states via weak measurements, quantum score matching, and Petz recovery maps with error bounds, bridging to classical stochastic reversals.
Score-based diffusion built intrinsically on the quantum pure-state manifold CP^{d-1}, trained with a local-time Gaussian teacher, matches pure-state ensembles far better than Euclidean baselines in the local-cluster regime, with gains shrinking on globally spread ensembles.
citing papers explorer
-
Generating quantum ensembles via reverse-time quantum diffusions
The paper establishes a reverse-time quantum diffusion framework that generates complex quantum ensembles from simple distributions by deriving and learning a feedback Hamiltonian from forward trajectory data.
-
Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport
IFM learns deterministic tangent velocity fields on CP^{d-1} via Pancharatnam phase-aligned paths, recovering marginal transport with endpoint and stability guarantees while showing empirical gains over Euclidean flow matching on quantum benchmarks.
-
Photonic-Implemented Efficient Deep Quantum Neural Network via Virtual-Driven Hilbert Space Expansion
A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.
-
Measurement-Based Quantum Diffusion Models
Measurement-based quantum diffusion models are introduced to recover pure and mixed quantum states via weak measurements, quantum score matching, and Petz recovery maps with error bounds, bridging to classical stochastic reversals.
-
Local-Time Riemannian Score Matching on the Quantum Pure-State Manifold
Score-based diffusion built intrinsically on the quantum pure-state manifold CP^{d-1}, trained with a local-time Gaussian teacher, matches pure-state ensembles far better than Euclidean baselines in the local-cluster regime, with gains shrinking on globally spread ensembles.