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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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0111 · Apr 202019922001200920172026
2 results for “t-designs”
Quantum neural networks form Gaussian processesquant-ph

Quantum neural networks converge to Gaussian processes as they grow.

problem Understanding the convergence of quantum neural networks to Gaussian processes.
method Analyzing Haar random unitary and orthogonal deep QNNs, considering input states, measurement observables, and non-independence of unitary matrix entries.
result Quantum neural networks outputs converge to Gaussian processes in the limit of large Hilbert space dimension.
quantum neural networksGaussian processesHaar random unitaryconcentration of measuret-designs
2023-05-17abs ↗pdf ↗
Low-Rank Matrix Estimation From Rank-One Projections by Unlifted Convex Optimizationmath.ST

Convex optimization method recovers low-rank matrices from rank-one projections efficiently.

problem Recovering low-rank matrices from limited rank-one projections.
method Unlifted convex optimization with subgradient method.
result The estimator succeeds with high probability if the number of measurements exceeds r2(d1+d2)r^2 (d_1+d_2) up to logarithmic factors.
matrix estimationlow-rank recoveryconvex optimizationrank-one projectionssubgradient method
2020-04-06abs ↗pdf ↗