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Quantum Approximate Optimization With Parallelizable Gates
IEEE Transactions on Quantum Engineering, 2020 The quantum approximate optimization algorithm (QAOA) has been introduced as a heuristic digital quantum computing scheme to find approximate solutions of combinatorial optimization problems. We present a scheme to parallelize this approach for arbitrary
Wolfgang Lechner
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Alignment between initial state and mixer improves QAOA performance for constrained optimization
npj Quantum Information, 2023 Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in ...
Zichang He +6 more
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Bayesian Optimization for QAOA
IEEE Transactions on Quantum Engineering, 2023 The quantum approximate optimization algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems.
Simone Tibaldi +3 more
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Hybrid quantum-classical algorithms for approximate graph coloring [PDF]
Quantum, 2022 We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) to MAX-$k$-CUT, the problem of finding an approximate $k$-vertex coloring of a graph.
Sergey Bravyi +3 more
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Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware [PDF]
Quantum, 2022 Quantum computers may provide good solutions to combinatorial optimization problems by leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often presented as an algorithm for noisy hardware.
Johannes Weidenfeller +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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IEEE Transactions on Quantum Engineering, 2023 The advent of quantum computing can potentially revolutionize how complex problems are solved. This article proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and ...
Reza Mahroo, Amin Kargarian
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npj Quantum Information, 2023 We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p
Danylo Lykov +5 more
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Fermionic quantum approximate optimization algorithm
Physical Review Research, 2023 Quantum computers are expected to accelerate solving combinatorial optimization problems, including algorithms such as Grover adaptive search and quantum approximate optimization algorithm (QAOA). However, many combinatorial optimization problems involve
Takuya Yoshioka +3 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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