Quantum algorithms: an overview
By Ashley Montanaro. npj Quantum Information, 2015
1,066 citations recorded by the source corpus.
Quantum computers are designed to outperform standard computers by running quantum algorithms. Areas in which quantum algorithms can be applied include cryptography, search and optimisation, simulation of quantum systems and solving large systems of linear equations. Here we briefly survey some known quantum algorithms, with an emphasis on a broad overview of their applications rather than their technical details. We include a discussion of recent developments and near-term applications of quantum algorithms.
Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics
View Paper: https://www.semanticscholar.org/paper/165d124ce07718d533e0774445d9fc0608aa2884
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Carlo Ciliberto
- Daniel A. Lidar
- Massimiliano Pontil
- G. Quiroz
- Lina Tewala
- Nic Ezzell
- Bibek Pokharel
- John Preskill
In Atomic and Molecular Physics, and Optics
- G. Quiroz
- Lina Tewala
- Nic Ezzell
- Bibek Pokharel
- Massimiliano Pontil
- Mark Herbster
- Daniel A. Lidar
- John Preskill
Citations in the corpus
- Dynamical decoupling for superconducting qubits: A performance survey (2022, background): “…era, quantum computing research has two main near-term goals: to examine the promise of quantum computers via demonstrations of quantum algorithms [1–3] and to understand how quantum error correction and other noise mitigation methods can pave a path towards fault-tolerant quantum computers [4,…”
- Quantum Computing in the NISQ era and beyond (2018, background): “And it is a compelling challenge to understand better what problems are classically hard but quantumly easy [4, 5].”
- Distributed Quantum Computing: a Survey (2022, neutral): “There exists several quantum algorithms known or expected to outperform classical algorithms for problems spanning different areas, including cryptography, search and optimization, simulation of quantum systems and learning [50].”
- Cryo-CMOS Circuits and Systems for Quantum Computing Applications (2018, neutral): “optimizations [3].”
- Quantum machine learning: a classical perspective (2017, methodology): “ there is now a number of resources covering quantum machine learning in the broader sense of the term [Adc+15; Bia+16]. For an introduction to quantum algorithms we refer to the reviews of Montanaro [Mon16] and Bacon [BV10], while for machine learning to the books by Bishop [Bis+06] and Murphy [Mur”