Search Results for author:
Found 7 papers, 0 papers with code
SMC Is All You Need: Parallel Strong Scaling
In the general framework of Bayesian inference, the target distribution can only be evaluated up-to a constant of proportionality.
Demonstration of machine-learning-enhanced Bayesian quantum state estimation
Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations.
Deep learning for enhanced free-space optical communications
Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of coherent light used in ON-OFF keying free-space optical communication.
Dimension-adaptive machine-learning-based quantum state reconstruction
We introduce an approach for performing quantum state reconstruction on systems of $n$ qubits using a machine-learning-based reconstruction system trained exclusively on $m$ qubits, where $m\geq n$.
Data-Centric Machine Learning in Quantum Information Science
We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science.
Improving application performance with biased distributions of quantum states
We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity.
On the experimental feasibility of quantum state reconstruction via machine learning
We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states.
