Results 31 to 40 of about 3,959 (191)
Quantum computational phase transition in combinatorial problems
npj Quantum Information, 2022 Quantum Approximate Optimization algorithm (QAOA) aims to search for approximate solutions to discrete optimization problems with near-term quantum computers.
Bingzhi Zhang, Akira Sone, Quntao Zhuang
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JuliQAOA: Fast, Flexible QAOA Simulation
Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, 2023 We introduce JuliQAOA, a simulation package specifically built for the Quantum Alternating Operator Ansatz (QAOA). JuliQAOA does not require a circuit-level description of QAOA problems, or another package to simulate such circuits, instead relying on a more direct linear algebra implementation.
John K. Golden +4 more
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Mean-Field Approximate Optimization Algorithm
PRX Quantum, 2023 The quantum approximate optimization algorithm (QAOA) is suggested as a promising application on early quantum computers. Here a quantum-inspired classical algorithm, the mean-field approximate optimization algorithm (mean-field AOA), is developed by ...
Aditi Misra-Spieldenner +5 more
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The QAOA with Few Measurements
, 2022 The Quantum Approximate Optimization Algorithm (QAOA) was originally developed to solve combinatorial optimization problems, but has become a standard for assessing the performance of quantum computers. Fully descriptive benchmarking techniques are often prohibitively expensive for large numbers of qubits ($n \gtrsim 10$), so the QAOA often serves in ...
Polloreno, Anthony M., Smith, Graeme
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QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
, 2020 In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms.
Al-Ars, Zaid +2 more
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Digitized-counterdiabatic quantum approximate optimization algorithm
Physical Review Research, 2022 The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems.
P. Chandarana +6 more
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Coreset Clustering on Small Quantum Computers
, 2020 Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over ...
Anschuetz, Eric R. +3 more
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Similarity-based parameter transferability in the quantum approximate optimization algorithm
Frontiers in Quantum Science and Technology, 2023 The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization.
Alexey Galda +10 more
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Quantum machine learning: a classical perspective [PDF]
, 2018 Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Ben-David S +15 more
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Physical Review Research, 2022 The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems.
Linghua Zhu +6 more
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