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Found 19 papers, 10 papers with code
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines
We find improvements ranging from 5% to 72% against attacks with Boltzmann machines on the MNIST dataset.
Unfolding as Quantum Annealing
High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Quantum Physics
An Artificial Spiking Quantum Neuron
Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing.
Vulnerability of quantum classification to adversarial perturbations
Furthermore, this leads to a trade-off between the security of the classification algorithm against adversarial attacks and quantum advantages we expect for high-dimensional problems.
Quantum Physics
Open source software in quantum computing
Open source software is becoming crucial in the design and testing of quantum algorithms.
Quantum Physics Mathematical Software Programming Languages
Bayesian Deep Learning on a Quantum Computer
Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.
Automated discovery of characteristic features of phase transitions in many-body localization
We identify a new "order parameter" for the disorder driven many-body localization (MBL) transition by leveraging artificial intelligence.
Quantum Physics Disordered Systems and Neural Networks
Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
We study the quantum features of the TN states, including quantum entanglement and fidelity.
Identifying Quantum Phase Transitions with Adversarial Neural Networks
Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture.
Quantum Machine Learning
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
Quantum Enhanced Inference in Markov Logic Networks
An MLN is essentially a first-order logic template to generate Markov networks.
Single-shot Adaptive Measurement for Quantum-enhanced Metrology
We construct a formal framework for AQEM by modeling the procedure as a decision-making process, and we derive the imprecision and the Cram\'{e}r-Rao lower bound with explicit dependence on the feedback policy.
A Physical Metaphor to Study Semantic Drift
By using term distances as a measure of semantic relatedness vs. their PageRank values indicating social importance and applied as variable `term mass', gravitation as a metaphor to express changes in the semantic content of a vector field lends a new perspective for experimentation.
Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation.
Inductive supervised quantum learning
In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances.
Monitoring Term Drift Based on Semantic Consistency in an Evolving Vector Field
Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis.
Algorithm 950: Ncpol2sdpa---Sparse Semidefinite Programming Relaxations for Polynomial Optimization Problems of Noncommuting Variables
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables.
Mathematical Software Optimization and Control Computational Physics Quantum Physics G.4
Somoclu: An Efficient Parallel Library for Self-Organizing Maps
Somoclu is a massively parallel tool for training self-organizing maps on large data sets written in C++.
