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On the Applicability of Quantum Machine Learning [PDF]

Entropy, 2023
In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE).
Sebastian Raubitzek, Kevin Mallinger
doaj   +5 more sources

Quantum Machine Learning—Quo Vadis? [PDF]

Entropy
The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience.
Andreas Wichert
doaj   +6 more sources

Shadows of quantum machine learning [PDF]

Nature Communications
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage.
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, Vedran Dunjko   +4 more
doaj   +8 more sources

Quantum Machine Learning: A Review and Case Studies [PDF]

Entropy, 2023
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware.
Amine Zeguendry, Zahi Jarir, Mohamed Quafafou   +2 more
doaj   +2 more sources

Machine Learning: Quantum vs Classical

IEEE Access, 2020
Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing.
Tariq M. Khan, Antonio Robles-Kelly
doaj   +3 more sources

Design and analysis of quantum machine learning: a survey

Connection Science
Machine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly.
Linshu Chen, Tao Li, Yuxiang Chen, Xiaoyan Chen, Marcin Wozniak, Neal Xiong, Wei Liang   +6 more
doaj   +3 more sources

An optimizing method for performance and resource utilization in quantum machine learning circuits [PDF]

Scientific Reports, 2022
Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers.
Tahereh Salehi, Mariam Zomorodi, Pawel Plawiak, Mina Abbaszade, Vahid Salari   +4 more
doaj   +2 more sources

Optimal Multi-Bit Toffoli Gate Synthesis

IEEE Access, 2023
Multi-bit Toffoli gates form an essential quantum gate class for quantum algorithms. They should be efficiently decomposed into elementary single- or multi-qubit quantum gates, such as CNOT, T, and Hadarmard, for a scalable implementation of a quantum ...
Young-Min Jun, In-Chan Choi
doaj   +1 more source

Progress towards Analytically Optimal Angles in Quantum Approximate Optimisation

Mathematics, 2022
The quantum approximate optimisation algorithm is a p layer, time variable split operator method executed on a quantum processor and driven to convergence by classical outer-loop optimisation.
Daniil Rabinovich, Richik Sengupta, Ernesto Campos, Vishwanathan Akshay, Jacob Biamonte   +4 more
doaj   +1 more source

Quantum Optical Experiments Modeled by Long Short-Term Memory

Photonics, 2021
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum
Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter   +5 more
doaj   +1 more source