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Minor-Embedding in Adiabatic Quantum Computation: I. The Parameter Setting Problem

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arxiv 0804.4884 v1 pith:YE5KF54A submitted 2008-04-30 quant-ph

classification quant-ph
keywords problemadiabaticisingparameterquantumsettinggraphhamiltonian
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abstract

We show that the NP-hard quadratic unconstrained binary optimization (QUBO) problem on a graph $G$ can be solved using an adiabatic quantum computer that implements an Ising spin-1/2 Hamiltonian, by reduction through minor-embedding of $G$ in the quantum hardware graph $U$. There are two components to this reduction: embedding and parameter setting. The embedding problem is to find a minor-embedding $G^{emb}$ of a graph $G$ in $U$, which is a subgraph of $U$ such that $G$ can be obtained from $G^{emb}$ by contracting edges. The parameter setting problem is to determine the corresponding parameters, qubit biases and coupler strengths, of the embedded Ising Hamiltonian. In this paper, we focus on the parameter setting problem. As an example, we demonstrate the embedded Ising Hamiltonian for solving the maximum independent set (MIS) problem via adiabatic quantum computation (AQC) using an Ising spin-1/2 system. We close by discussing several related algorithmic problems that need to be investigated in order to facilitate the design of adiabatic algorithms and AQC architectures.

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Cited by 2 Pith papers

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

  1. Quantum Portfolio Optimization: An Extensive Benchmark

    quant-ph 2025-09 conditional novelty 6.0 of 10

    On a new 260-instance real-world benchmark, classical MIP and heuristics clearly outperform quantum annealing and QAOA for a volatility-minimizing portfolio optimization variant.

  2. Solving the compute crisis with physics-based ASICs

    cs.ET 2025-07 unverdicted novelty 4.0 of 10

    A coalition of academic and industry researchers argues that chips exploiting natural physical dynamics, rather than enforcing digital abstractions, could dramatically cut AI computing costs.

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