Quantum Algorithms for Weighted Constrained Sampling and Weighted Model Counting
<|reference_start|>Quantum Algorithms for Weighted Constrained Sampling and Weighted Model Counting: We consider the problems of weighted constrained sampling and weighted model counting, where we are given a propositional formula and a weight for each world. The first problem consists of sampling worlds with a probability proportional to their weight given that the formula is satisfied. The latter is the problem of computing the sum of the weights of the models of the formula. Both have applications in many fields such as probabilistic reasoning, graphical models, statistical physics, statistics and hardware verification. In this article, we propose QWCS and QWMC, quantum algorithms for performing weighted constrained sampling and weighted model counting, respectively. Both are based on the quantum search/quantum model counting algorithms that are modified to take into account the weights. In the black box model of computation, where we can only query an oracle for evaluating the Boolean function given an assignment, QWCS requires $O(2^{\frac{n}{2}}+1/\sqrt{\text{WMC}})$ oracle calls, where where $n$ is the number of Boolean variables and $\text{WMC}$ is the normalized between 0 and 1 weighted model count of the formula, while a classical algorithm has a complexity of $\Omega(1/\text{WMC})$. QWMC takes $\Theta(2^{\frac{n}{2}})$ oracle calss, while classically the best complexity is $\Theta(2^n)$, thus achieving a quadratic speedup.<|reference_end|>
@article{riguzzi2024quantum,
    title={Quantum Algorithms for Weighted Constrained Sampling and Weighted Model
  Counting},
    author={Fabrizio Riguzzi},
    journal={arXiv preprint arXiv:2407.12816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12816},
    primaryClass={quant-ph cs.AI}
}