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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.
Machine learning assisted quantum state estimation
We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements.
Generative Machine Learning for Robust Free-Space Communication
Realistic free-space optical communications systems suffer from turbulent propagation of light through the atmosphere and detector noise at the receiver, which can significantly degrade the optical mode quality of the received state, increase cross-talk between modes, and correspondingly increase the symbol error ratio (SER) of the system.
Dispersion Characterization and Pulse Prediction with Machine Learning
In this work we demonstrate the efficacy of neural networks in the characterization of dispersive media.
Coherent Optical Communications Enhanced by Machine Intelligence
Additionally, we program the neural network system at the transmitter such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is shared with the network state of the receiver prior to the implementation of the communications.
