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By Scott Aaronson. Nature Physics, 2015

338 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/96b7e0ba9b7d13f0dde49d42ab94b58aac3a8e3e

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Citations in the corpus

Quantum algorithms: an overview (2015, neutral): “Whether this is a reasonable definition of ‘solution’ depends on the application, and again may depend on whether the input is produced algorithmically or is provided explicitly as arbitrary data.(65)”
Hierarchical quantum classifiers (2018, background): “The algorithm is exponentially faster than the best known classical alternative, although this comes with some caveats [7].”
Quantum Generative Adversarial Networks for learning and loading random distributions (2019, background): “Notably, QAE and HHL – given a well-conditioned matrix and a suitable classical right-hand-side [5] – are both compatible with approximate state preparation as these algorithms are stable to small errors in the input state, i.”
Quantum machine learning (2016, neutral): “A challenge facing the application of many of these methods for big data is the fact that the training set of classical data must be loaded into the quantum computer, a step that can dominate the cost of the algorithm in some cases [70].”
Quanvolutional neural networks: powering image recognition with quantum circuits (2019, methodology): “For instance, decoding methods that require more than one measurement of the quantum system quickly become problematic; referencing again Aaronson 2015, (23) any potential “quantum speedup” disappears for algorithms that require large numbers of quantum measurements.”
Quantum technology for military applications (2021, background): “The solution is a quantum memory or quantum RAM [52, 53].”
Quantum Computing in the NISQ era and beyond (2018, background): “” Much of the literature on the subject builds on quantum algorithms that speed up linear algebra and related tasks [37, 38], and I’ll address such applications in the ensuing subsections.”
A quantum-inspired classical algorithm for recommendation systems (2018, background): “However, in part because of caveats exposited by Aaronson [1], it is not clear whether any known QML algorithm gives a new exponential speedup over classical algorithms, for practically relevant instances of a machine learning problem.”
Adiabatic Quantum Computing (2016, background): “Quantum machine learning is currently an exciting and rapidly moving frontier in the context of the circuit model (Lloyd et al., 2014; Rebentrost et al., 2014; Wiebe et al., 2014), though it must be evaluated carefully (Aaronson, 2015).”