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.
Recio-Armengol, S
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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.
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
Parity supervision improves exact KL fit and recovery of unseen high-value states in IQP Born machines beyond MSE training or max-entropy controls via parity-moment evidence transfer.
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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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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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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Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Parity supervision improves exact KL fit and recovery of unseen high-value states in IQP Born machines beyond MSE training or max-entropy controls via parity-moment evidence transfer.
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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.