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Found 5 papers, 1 papers with code
Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms.
Exploiting Inductive Biases in Video Modeling through Neural CDEs
We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and mask propagation.
Variable Length Embeddings
In this work, we introduce a novel deep learning architecture, Variable Length Embeddings (VLEs), an autoregressive model that can produce a latent representation composed of an arbitrary number of tokens.
Practical Black Box Hamiltonian Learning
We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system.
Adaptive shot allocation for fast convergence in variational quantum algorithms
Variational Quantum Algorithms (VQAs) are a promising approach for practical applications like chemistry and materials science on near-term quantum computers as they typically reduce quantum resource requirements.
