cuTN-QSVM: cuTensorNet-accelerated Quantum Support Vector Machine with cuQuantum SDK
KC Chen, TY Li, YY Wang, S See, CC Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper investigates the application of Quantum Support Vector Machines (QSVMs) with
an emphasis on the computational advancements enabled by NVIDIA's cuQuantum SDK …
an emphasis on the computational advancements enabled by NVIDIA's cuQuantum SDK …
cuQuantum SDK: A high-performance library for accelerating quantum science
H Bayraktar, A Charara, D Clark… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
We present the NVIDIA cuQuantum SDK [1], a state-of-the-art library of composable
primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices …
primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices …
[PDF][PDF] Hybrid Quantum Technologies for Quantum Support Vector Machines. Information 2024, 15, 72
Quantum computing has rapidly gained prominence for its unprecedented computational
efficiency in solving specific problems when compared to classical computing counterparts …
efficiency in solving specific problems when compared to classical computing counterparts …
[HTML][HTML] Hybrid Quantum Technologies for Quantum Support Vector Machines
Quantum computing has rapidly gained prominence for its unprecedented computational
efficiency in solving specific problems when compared to classical computing counterparts …
efficiency in solving specific problems when compared to classical computing counterparts …
Practical application improvement to Quantum SVM: theory to practice
JE Park, B Quanz, S Wood, H Higgins… - arXiv preprint arXiv …, 2020 - arxiv.org
Quantum machine learning (QML) has emerged as an important area for Quantum
applications, although useful QML applications would require many qubits. Therefore our …
applications, although useful QML applications would require many qubits. Therefore our …
ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks
M Kashif, M Shafique - arXiv preprint arXiv:2402.09146, 2024 - arxiv.org
In this paper, we present a novel framework for enhancing the performance of
Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers …
Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers …
Simulating quantum computers using OpenCL
A Kelly - arXiv preprint arXiv:1805.00988, 2018 - arxiv.org
Quantum computing is an emerging technology, promising a paradigm shift in computing,
and allowing for speedups in many different problems. However, quantum devices are still in …
and allowing for speedups in many different problems. However, quantum devices are still in …
Parallelizing quantum-classical workloads: Profiling the impact of splitting techniques
Quantum computers are the next evolution of computing hardware. Quantum devices are
being exposed through the same familiar cloud platforms used for classical computers, and …
being exposed through the same familiar cloud platforms used for classical computers, and …
Rosnet: A block tensor algebra library for out-of-core quantum computing simulation
S Sanchez-Ramirez, J Conejero… - 2021 IEEE/ACM …, 2021 - ieeexplore.ieee.org
With the advent of more powerful Quantum Computers, the need for larger Quantum
Simulations has boosted. As the amount of resources grows exponentially with size of the …
Simulations has boosted. As the amount of resources grows exponentially with size of the …
State of practice: evaluating GPU performance of state vector and tensor network methods
The frontier of quantum computing (QC) simulation on classical hardware is quickly reaching
the hard scalability limits for computational feasibility. Nonetheless, there is still a need to …
the hard scalability limits for computational feasibility. Nonetheless, there is still a need to …