DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis | Paper Citation Record

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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This paper cites Hence� θ[a(θ)] =C•0 and centring removes exactly thek= 0 contribution.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Hence� θ[a(θ)] =C•0 and centring removes exactly thek= 0 contribution

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This paper cites The accessible spectrum and its construction via difference sets and Minkowski sums is standard in the QFM literature [14, 15, 19, 20].

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis The accessible spectrum and its construction via difference sets and Minkowski sums is standard in the QFM literature [14, 15, 19, 20]

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This paper cites From (43), Cov[a] ωµis large when the rowsC ω,•andC µ,•have aligned phases on a shared set of centred harmonics.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis From (43), Cov[a] ωµis large when the rowsC ω,•andC µ,•have aligned phases on a shared set of centred harmonics

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This paper cites (67) This isolates a universal torus objectM(θ) (depending only on the character map and the parameter space manifold structure) from the architecture-dependent mapping encoded byC.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis (67) This isolates a universal torus objectM(θ) (depending only on the character map and the parameter space manifold structure) from the architecture-dependent mapping encoded byC

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This paper cites Throughout, the trainable blockW ℓis taken to be a depth-drepetition of a fixed ans¨ atz pattern as defined in [10] and also used in [21].

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Throughout, the trainable blockW ℓis taken to be a depth-drepetition of a fixed ans¨ atz pattern as defined in [10] and also used in [21]

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This paper cites For our circuits where we re-encode on each qubit and each layer,ω max =nL.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis For our circuits where we re-encode on each qubit and each layer,ω max =nL

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This paper cites As in the correlation-matrix comparisons of Figures 4–6, we report both normalised Frobenius errors and cosine similarities.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis As in the correlation-matrix comparisons of Figures 4–6, we report both normalised Frobenius errors and cosine similarities

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This paper cites Architectures employing non-commuting feature maps or qualitatively different data-loading schemes may not admit the same finite harmonic description without modification.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Architectures employing non-commuting feature maps or qualitatively different data-loading schemes may not admit the same finite harmonic description without modification

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Concretely, second-order quantities control the linearised training dynamics around a parameter point (e.g

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis sampling, conditioning, and lattice effects)

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This paper cites The dependence of data-space kernels on the input design viaVinK(θ) =VH(θ)V †, is therefore a relevant consideration.

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis The dependence of data-space kernels on the input design viaVinK(θ) =VH(θ)V †, is therefore a relevant consideration

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis As noted in the limitations, this focus leaves open the systematic role of higher-order statistics

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis conservation laws, permutation invariances, and problem-specific equivariances)

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Quantum machine learning,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Schuld and F

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Variational quantum algorithms,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Supervised learning with quantum-enhanced feature spaces,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Evaluating analytic gradients on quantum hardware,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Barren plateaus in quantum neural network training landscapes,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis A Review of Barren Plateaus in Variational Quantum Computing,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Barren Plateaus in Variational Quantum Computing

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Noise-induced barren plateaus in variational quantum algorithms,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Representation Learning via Quantum Neural Tangent Kernels,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Quantum Lazy Training

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Data re-uploading for a universal quantum classifier

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Effect of data encoding on the expressive power of variational quantum-machine-learning models,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis The Heisenberg Representation of Quantum Computers

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Improved Simulation of Stabilizer Circuits

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Simulation of qubit quantum circuits via Pauli propagation,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Multidimensional Fourier series with quantum circuits,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Constrained and Vanishing Expressivity of Quantum Fourier Models

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Spectral Bias in Variational Quantum Machine Learning,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Quantum tangent kernel,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Katznelson,An Introduction to Harmonic Analysis

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Neural tangent kernel: Convergence and generalization in neural networks,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Random Features for Large-Scale Kernel Machines,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis On the Spectral Bias of Neural Networks,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis The Born Ultimatum: Conditions for Classical Surrogation of Quantum Generative Models with Correlators,

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis A Unified Theory of Quantum Neural Network Loss Landscapes

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Stabilizer Codes and Quantum Error Correction

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis The Clifford group, stabilizer states, and linear and quadratic operations over GF(2)

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Quantum Fourier models and encoder-accessible harmonics 31

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Proofs for coefficient statistics 33

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Pauli propagation and node expansion 34

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

Reference 84

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis �� Trainable parameter vector on the�-torus;�is the number of trainable parameters

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Note the similarity in structure to the circuit’s correlation matrix in Figure 4

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis �� (Difference-frequency expansion)�LetS(x) = exp(�ix�G)with commuting Hermitian generators G= (G 1,...,Gd)and joint eigenbasis��λj��j

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Unresolved cited work

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis �� (Path set and redundancy)�Let Ω (ℓ)be the difference set of layerℓ

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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis Single-qubit Pauli encoder.LetS(x) =e −ixZ/2

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