A first-moment operator diagnostic reveals exponentially many inequivalent initialization distributions avoid barren plateaus in variational quantum algorithms, with numerics indicating distinct attained minima.
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Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
The paper decomposes dynamical Lie algebras of XY-mixer topologies and demonstrates warm-starting QAOA via pre-training on restricted generators to improve convergence on constrained optimization problems.
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
A simulation-derived phenomenological model optimizes the trade-off between quantum circuit size and iteration count to minimize total gate operations for a desired accuracy in noisy VQE algorithms.
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
citing papers explorer
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Exponentially many initializations to avoid barren plateaus
A first-moment operator diagnostic reveals exponentially many inequivalent initialization distributions avoid barren plateaus in variational quantum algorithms, with numerics indicating distinct attained minima.
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Local tensor-train surrogates for quantum learning models
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization
The paper decomposes dynamical Lie algebras of XY-mixer topologies and demonstrates warm-starting QAOA via pre-training on restricted generators to improve convergence on constrained optimization problems.
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Ravines in quantum cost landscapes: opportunities for improved VQA predictions
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
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Quantum computation at the edge of chaos
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
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Optimizing resource allocation for accuracy in noisy variational quantum algorithms
A simulation-derived phenomenological model optimizes the trade-off between quantum circuit size and iteration count to minimize total gate operations for a desired accuracy in noisy VQE algorithms.
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Mind the gaps: The fraught road to quantum advantage
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.