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Quantum Adiabatic Evolution Algorithms versus Simulated Annealing

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arxiv quant-ph/0201031 v1 pith:AX3M3XTK submitted 2002-01-08 quant-ph

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keywords adiabaticannealingexamplesquantumsimulatedbitscostevolution
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We explain why quantum adiabatic evolution and simulated annealing perform similarly in certain examples of searching for the minimum of a cost function of n bits. In these examples each bit is treated symmetrically so the cost function depends only on the Hamming weight of the n bits. We also give two examples, closely related to these, where the similarity breaks down in that the quantum adiabatic algorithm succeeds in polynomial time whereas simulated annealing requires exponential time.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Log-concavity and tunneling: adiabatic quantum optimization for convex functions (with a spike)

    quant-ph 2026-06 unverdicted novelty 7.0 of 10

    Establishes discrete log-concavity of ground states for convex potentials and extends Reichardt's HWS tunneling analysis to quadratic spikes via new spectral gap bounds.

  2. Improving adiabatic quantum factorization via chopped random-basis optimization

    quant-ph 2025-05 conditional novelty 4.0 of 10

    Applying CRAB schedule optimization to adiabatic factorization Hamiltonians raises final-state fidelity for integers 21 to 2479, with a performance threshold near the quantum speed limit, and the improvement survives ...

  3. Designing Minimalistic Variational Quantum Ansatz Inspired by Algorithmic Cooling

    quant-ph 2025-01 conditional novelty 4.0 of 10

    The paper introduces the Heat Exchange (HE) ansatz, a variational circuit built from XX+YY interactions with bath qubits, and reports improved MaxCut approximations and sub-1% Heisenberg ground-state errors in small n...

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