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Quantum Science and Technology is a peer-reviewed journal published by IOP Publishing, covering Quantum Information and Cryptography, Quantum Computing Algorithms and Architecture, Quantum Mechanics and Applications. Over its lifetime it has published 1,429 papers, which have received 28,415 citations.
| Publisher | IOP Publishing |
|---|---|
| Reference style | Numbered (IOP) Numbered — [1], [2] in the text [1] Smith A, Jones B and Lee C 2023 A representative article title Quantum Science and Technology 12 45–58
Formats any DOI in Quantum Science and Technology style. No sign-up. |
| Publishes research in | Quantum Information and Cryptography Quantum Computing Algorithms and Architecture Quantum Mechanics and Applications Quantum and electron transport phenomena Quantum many-body systems |
| ISSN | 2058-9565 |
| Citation impact (2-yr) | 3.91 |
| h-index | 76 |
| i10-index | 573 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | iopscience.iop.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract Hybrid quantum–classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regarded as machine learning models with remarkable expressive power. This Review presents the components of these models and discusses their application to a variety of data-driven tasks, such as supervised learning…
Universal fault-tolerant quantum computers will require error-free execution of long sequences of quantum gate operations, which is expected to involve millions of physical qubits. Before the full power of such machines will be available, near-term quantum devices will provide several hundred qubits and limited error correction. Still, there is a realistic prospect to run useful…
Abstract Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x , to map x to a lower dimensional point y such that x can likely be recovered from y . The structure of the underlying autoencoder network can…
Abstract We present t | ket ⟩, a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t | ket ⟩ is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has…
Abstract Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning,…
