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Found 9 papers, 5 papers with code
Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical Components
To provide a more fine-grained characterisation of the impact of quantum components on the performance of neural networks, we propose a framework where classical neural network layers are gradually replaced by quantum layers that have the same type of input and output while keeping the flow of information between layers unchanged, different from most current research in quantum neural network, which favours an end-to-end quantum model.
Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
Conventionally, the design of quantum machine learning algorithms relies on the ``quantisation" of classical learning algorithms, such as using quantum linear algebra to implement important subroutines of classical algorithms, if not the entire algorithm, seeking to achieve quantum advantage through possible run-time accelerations brought by quantum computing.
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
