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Different strategies for optimization with the quantum adiab atic algorithm

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it
abstract

We present the results of a numerical study, with 20 qubits, of the performance of the Quantum Adiabatic Algorithm on randomly generated instances of MAX 2-SAT with a unique assignment that maximizes the number of satisfied clauses. The probability of obtaining this assignment at the end of the quantum evolution measures the success of the algorithm. Here we report three strategies which consistently increase the success probability for the hardest instances in our ensemble: decreasing the overall evolution time, initializing the system in excited states, and adding a random local Hamiltonian to the middle of the evolution.

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quant-ph 3

representative citing papers

A Quantum Approximate Optimization Algorithm

quant-ph · 2014-11-14 · accept · novelty 9.0

A p-layer alternating-operator ansatz on n qubits yields approximation ratios that increase with p, achieving ≥0.6924 for MaxCut on 3-regular graphs at p=1 and approaching 1 in the p→∞ adiabatic limit.

Multi-tasking through quantum annealing

quant-ph · 2026-03-10 · unverdicted · novelty 5.0

MTQA embeds multiple NP-hard problems such as minimum vertex cover and graph partitioning into spatially distinct regions on quantum hardware, delivering comparable solution quality to single-task annealing with reduced time-to-solution.

citing papers explorer

Showing 3 of 3 citing papers.

  • A Quantum Approximate Optimization Algorithm quant-ph · 2014-11-14 · accept · none · ref 4

    A p-layer alternating-operator ansatz on n qubits yields approximation ratios that increase with p, achieving ≥0.6924 for MaxCut on 3-regular graphs at p=1 and approaching 1 in the p→∞ adiabatic limit.

  • Multi-tasking through quantum annealing quant-ph · 2026-03-10 · unverdicted · none · ref 45 · internal anchor

    MTQA embeds multiple NP-hard problems such as minimum vertex cover and graph partitioning into spatially distinct regions on quantum hardware, delivering comparable solution quality to single-task annealing with reduced time-to-solution.

  • Associative memory on qutrits by means of quantum annealing quant-ph · 2019-06-20 · unverdicted · none · ref 20 · internal anchor

    Numerical simulations on two and three qutrits show increased associative memory capacity when qubits are replaced by qutrits in a quantum annealing protocol.