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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. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this work we present a Bayesian optimization procedure to fulfil this optimization task, and
Simone Tibaldi +3 more
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The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size [PDF]
Quantum, 2022 The Quantum Approximate Optimization Algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization problems whose performance can only improve with the number of layers $p$.
Edward Farhi +3 more
doaj +1 more source
New Journal of Physics, 2023 The variational preparation of complex quantum states using the quantum approximate optimization algorithm (QAOA) is of fundamental interest, and becomes a promising application of quantum computers.
Zheng-Hang Sun +3 more
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Reinforcement learning assisted recursive QAOA. [PDF]
EPJ Quantum TechnolAbstractIn recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance.
Patel YJ, Jerbi S, Bäck T, Dunjko V.
europepmc +5 more sources
Reachability Deficits in Quantum Approximate Optimization of Graph Problems [PDF]
Quantum, 2021 The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. Here we show that the $density$ of problem constraints versus problem variables acts as a performance indicator.
V. Akshay +3 more
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Solution of SAT problems with the adaptive-bias quantum approximate optimization algorithm
Physical Review Research, 2023 The quantum approximate optimization algorithm (QAOA) is a promising method for solving certain classical combinatorial optimization problems on near-term quantum devices. When employing the QAOA to 3-SAT and Max-3-SAT problems, the quantum cost exhibits
Yunlong Yu +4 more
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Classical Optimizers for Noisy Intermediate-Scale Quantum Devices [PDF]
, 2020 We present a collection of optimizers tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate ...
De Jong, W +4 more
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Graph neural network initialisation of quantum approximate optimisation [PDF]
Quantum, 2022 Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term.
Nishant Jain +3 more
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Hamiltonian-Oriented Homotopy QAOA
, 2023 The classical homotopy optimization approach has the potential to deal with highly nonlinear landscape, such as the energy landscape of QAOA problems. Following this motivation, we introduce Hamiltonian-Oriented Homotopy QAOA (HOHo-QAOA), that is a heuristic method for combinatorial optimization using QAOA, based on classical homotopy optimization. The
Kundu, Akash +2 more
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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 Golden +4 more
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