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Found 7 papers, 3 papers with code
Towards quantum enhanced adversarial robustness in machine learning
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge.
GASP -- A Genetic Algorithm for State Preparation
Results achieved by GASP outperform Qiskit's exact general circuit synthesis method on a variety of states such as Gaussian states and W-states, and consistently show the method reduces the number of gates required for the quantum circuits to generate these quantum states to the required accuracy.
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
A kernel-based quantum random forest for improved classification
The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation.
Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years.
A scalable and fast artificial neural network syndrome decoder for surface codes
Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing.
Architectural design for a topological cluster state quantum computer
The development of a large scale quantum computer is a highly sought after goal of fundamental research and consequently a highly non-trivial problem.
Quantum Physics
