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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Recio-Armengol, S
14 Pith papers cite this work. Polarity classification is still indexing.
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A learning-based framework constructs logical operations for arbitrary quantum codes and co-designs non-additive encodings with noise models and desired gate sets via VarEFTQC.
Block-product paired non-Gaussian fermionic states allow efficient classical additive-error approximation of transition amplitudes, overlaps, and high-weight correlators under free-fermionic dynamics using multivariate Pfaffian polynomials.
A pre-training diagnostic map based on spectral correlation resemblance to IQP circuits and excess structural complexity identifies suitable datasets like turbulence data for quantum generative models, yielding competitive low-resource performance.
Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
Analytical lower bounds and Lipschitz concentration results show when Gaussian-initialized IQP QCBMs avoid exponential gradient concentration under MMD training.
Parity-moment supervision improves forward-KL fit and unseen high-value-state recovery over coordinate-wise MSE in a controlled 12-qubit IQP Born-machine benchmark.
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
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.
Compositional quantum circuits with symmetry-induced invariant losses produce trainable equivariant quantum GNNs that generalize on max-clique problems and improve hybrid recursive search accuracy and scalability.
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
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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Learning Logical Operations for Arbitrary Quantum Error Correction Codes
A learning-based framework constructs logical operations for arbitrary quantum codes and co-designs non-additive encodings with noise models and desired gate sets via VarEFTQC.
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Classical simulation of free-fermionic dynamics and quantum chemistry with magic input
Block-product paired non-Gaussian fermionic states allow efficient classical additive-error approximation of transition amplitudes, overlaps, and high-weight correlators under free-fermionic dynamics using multivariate Pfaffian polynomials.
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Toward Generative Quantum Utility via Correlation-Complexity Map
A pre-training diagnostic map based on spectral correlation resemblance to IQP circuits and excess structural complexity identifies suitable datasets like turbulence data for quantum generative models, yielding competitive low-resource performance.
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Spectral Born machines: classically trainable quantum generative models for discrete data
Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.
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Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
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Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Analytical lower bounds and Lipschitz concentration results show when Gaussian-initialized IQP QCBMs avoid exponential gradient concentration under MMD training.
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Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Parity-moment supervision improves forward-KL fit and unseen high-value-state recovery over coordinate-wise MSE in a controlled 12-qubit IQP Born-machine benchmark.
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A unified quantum computing quantum Monte Carlo framework through structured state preparation
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
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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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Compositional Quantum Heuristics for Max-Clique Detection
Compositional quantum circuits with symmetry-induced invariant losses produce trainable equivariant quantum GNNs that generalize on max-clique problems and improve hybrid recursive search accuracy and scalability.
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Spectral methods: crucial for machine learning, natural for quantum computers?
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
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A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
- An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment