Quantum Fourier generative models are trained classically at over 1000-qubit scale using log-likelihood loss from Parseval's identity and deployed on superconducting hardware for fast sampling that preserves multi-modal structure.
Available: https://arxiv.org/abs/2602.11042
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
fields
quant-ph 6years
2026 6verdicts
UNVERDICTED 6representative citing papers
Develops an invariant-based framework connecting Pauli Lie algebras to transvection-generated Clifford subgroups for quantum reachability and dynamics analysis.
Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.
Derives lower bound on gradient variance and probabilistic concentration bounds for Gaussian-initialized IQP QCBMs trained via MMD loss.
An IQP Born machine with Mixture-of-IQP architecture and Pearson-Stabilized Correlation Kernel is trained on calorimeter images at 64 qubits and compiled to a single IQP circuit, reporting MAE_rho of 0.069 versus baseline 0.100.
Sparse qubit connectivity raises compiled depth in noisy IQP circuits, requiring lower effective noise to remain outside the classically simulatable regime compared to fully connected layouts.
citing papers explorer
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Quantum Fourier Generative Models Trainable at Large Scale
Quantum Fourier generative models are trained classically at over 1000-qubit scale using log-likelihood loss from Parseval's identity and deployed on superconducting hardware for fast sampling that preserves multi-modal structure.
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From Pauli Strings to Quantum Dynamics: A Unified Characterization
Develops an invariant-based framework connecting Pauli Lie algebras to transvection-generated Clifford subgroups for quantum reachability and dynamics analysis.
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Qudit extension of parameterized IQP circuits: A generative quantum machine learning approach to integer data
Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.
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Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Derives lower bound on gradient variance and probabilistic concentration bounds for Gaussian-initialized IQP QCBMs trained via MMD loss.
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An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment
An IQP Born machine with Mixture-of-IQP architecture and Pearson-Stabilized Correlation Kernel is trained on calorimeter images at 64 qubits and compiled to a single IQP circuit, reporting MAE_rho of 0.069 versus baseline 0.100.
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The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits
Sparse qubit connectivity raises compiled depth in noisy IQP circuits, requiring lower effective noise to remain outside the classically simulatable regime compared to fully connected layouts.