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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 +2 more
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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
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Quantum Machine Learning—Quo Vadis? [PDF]
EntropyThe 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
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Shadows of quantum machine learning [PDF]
Nature CommunicationsQuantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage.
Sofiene Jerbi +4 more
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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
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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 +4 more
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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
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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 +4 more
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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 +5 more
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Quantum Reinforcement Learning with Quantum Photonics
Photonics, 2021 Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their ...
Lucas Lamata
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