Results 111 to 120 of about 4,386 (222)

Globally optimizing QAOA circuit depth for constrained optimization\n problems [PDF]

, 2021
Rebekah Herrman, Lorna Treffert, James Ostrowski, Phillip C. Lotshaw, Travis S. Humble, George Siopsis   +5 more
openalex   +1 more source

The Quantum Approximation Optimization Algorithm for MaxCut: A Fermionic View [PDF]

Farhi et al. recently proposed a class of quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA), for approximately solving combinatorial optimization problems. A level-p QAOA circuit consists of steps in which a classical Hamiltonian,
Hadfield, Stuart, Jiang, Zhang, Rieffel, Eleanor G., Wang, Zhihui   +3 more
core   +1 more source

Benchmarking the performance of portfolio optimization with QAOA [PDF]

, 2022
Sebastian Brandhofer, Daniel Braun, Vanessa Dehn, Gerhard Hellstern, Matthias Hüls, Yanjun Ji, Ilia Polian, Amandeep Singh Bhatia, Thomas Wellens   +8 more
openalex   +1 more source

QAOA on Hamiltonian Cycle problem

, 2023
I use QAOA to solve the Hamiltonian Circle problem. First, inspired by Lucas, I define the QUBO form of Hamiltonian Cycle and transform it to a quantum circuit by embedding the problem of $n$ vertices to an encoding of $(n-1)^2$ qubits. Then, I calcluate the spectrum of the cost hamiltonian for both triangle case and square case and justify my ...
openaire   +1 more source

Modified recursive QAOA for exact MAX-CUT solutions on bipartite graphs: closing the gap beyond QAOA limit

Journal of Physics A: Mathematical and Theoretical
Abstract Quantum approximate optimization algorithm (QAOA) is a quantum–classical hybrid algorithm proposed with the goal of approximately solving combinatorial optimization problems such as the MAX-CUT problem. It has been considered a potential candidate for achieving quantum advantage in the noisy intermediate-scale quantum era and
Eunok Bae, Hyukjoon Kwon, V Vijendran, Soojoon Lee   +3 more
openaire   +2 more sources

Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems

npj Quantum Information
The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $${\{{\gamma }_{i},{\beta }_{i}\}}_{i = 0}^{p-1}$$ { γ i , β i } i =
J. A. Montañez-Barrera, Kristel Michielsen   +1 more
doaj   +1 more source

Strategies for Noise-Resilient Quantum Approximate Optimization Algorithms: A Review and Classification of Error Mitigation

IEEE Access
The Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate for solving combinatorial optimization problems on near-term quantum hardware.
Kimchhor Chiv, Leanghok Hour, Sanghyeon Lee, Tara Kit, Tae-Kyung Kim, Youngsun Han   +5 more
doaj   +1 more source

The pricing of academic journals: A two-sided market perspective [PDF]

More and more academic journals adopt an open-access policy, by which articles are accessible free of charge, while publication costs are recovered through author fees. We study the consequences of this open access policy on a journal’s quality standard.
Doh-Shin Jeon, Jean-Charles Rochet
core  

Performance of parity QAOA for the signed Max-Cut problem

New Journal of Physics
The practical implementation of quantum optimization algorithms on noisy intermediate-scale quantum devices requires accounting for their limited connectivity. As such, the Parity architecture was introduced to overcome this limitation by encoding binary
Anita Weidinger, Glen Bigan Mbeng, Michael Fellner, Davit Khachatryan, Wolfgang Lechner   +4 more
doaj   +1 more source

Inter-Temperature Bandwidth Reduction in Cryogenic QAOA Machines

IEEE Computer Architecture Letters
The bandwidth limit between cryogenic and room-temperature environments is a critical bottleneck in superconducting noisy intermediate-scale quantum computers. This paper presents the first trial of algorithm-aware system-level optimization to solve this issue by targeting the quantum approximate optimization algorithm.
Yosuke Ueno, Yuna Tomida, Teruo Tanimoto, Masamitsu Tanaka, Yutaka Tabuchi, Koji Inoue, Hiroshi Nakamura   +6 more
openaire   +2 more sources