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Found 8 papers, 0 papers with code
Efficient Gradient Estimation of Variational Quantum Circuits with Lie Algebraic Symmetries
Hybrid quantum-classical optimization and learning strategies are among the most promising approaches to harnessing quantum information or gaining a quantum advantage over classical methods.
Quantum Shadow Gradient Descent for Quantum Learning
We propose a new technique for generating quantum shadow samples (QSS), which generates quantum shadows as opposed to classical shadows used in existing works.
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression
We derive an elegant upper bound on the 0-1 loss in terms of the MMSE error and show that the sign of the MMSE is a PAC learner for any concept class containing it.
Expected Worst Case Regret via Stochastic Sequential Covering
We show that for a hypothesis class of VC-dimension $\mathsf{VC}$ and $i. i. d.$ generated features of length $T$, the cardinality of the stochastic global sequential covering can be upper bounded with high probability (whp) by $e^{O(\mathsf{VC} \cdot \log^2 T)}$.
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts.
Toward Physically Realizable Quantum Neural Networks
Current solutions for QNNs pose significant challenges concerning their scalability, ensuring that the postulates of quantum mechanics are satisfied and that the networks are physically realizable.
On Agnostic PAC Learning using $\mathcal{L}_2$-polynomial Regression and Fourier-based Algorithms
We develop a framework using Hilbert spaces as a proxy to analyze PAC learning problems with structural properties.
Learning k-qubit Quantum Operators via Pauli Decomposition
Our approach is based on the Pauli decomposition of quantum operators and a technique that we name Quantum Shadow Sampling (QSS) to reduce the sample complexity exponentially.
Quantum Physics Data Structures and Algorithms
