Search

Quantum computers are here, and the search for applications and use of these allow us to overcome the limits that today's hardware information processing gives us is constantly going on. Quantum machine learning is one of the many emerging fields that use quantum computers to process information. In this paper, we present a method and a set of experiments where we see the potential and capacity of the Noisy intermediate-scale quantum hardware for the execution of different models having as the basis in some of them the quantum algorithm corresponding to the Quantum Fourier Transform. With this, we demonstrate the effectiveness of how this algorithm can enhance the performance of quantum computations in quantum machine learning models in near-term applications. We used the systems offered by IBM Quantum and the cross-platform Python library for quantum differentiable programming Pennylane by Xanadu Quantum Technologies Inc. [ABSTRACT FROM AUTHOR]

Due to the increasing computing power of the graphics processing units (GPU), they are becoming more and more popular when solving general purpose algorithms. As the simulation of quantum computers results on a problem with exponential complexity, it is advisable to perform a parallel computation, such as the one provided by the SIMD multiprocessors present in recent GPUs. In this paper, we focus on an important quantum algorithm, the quantum Fourier transform (QTF), in order to evaluate different parallelization strategies on a novel GPU architecture. Our implementation makes use of the new CUDA software/hardware architecture developed recently by NVIDIA. [ABSTRACT FROM AUTHOR]