量子图像去噪:通过玻尔兹曼机,QUBO和量子退火的框架

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Frontiers in Computer Science Pub Date : 2023-10-19 DOI:10.3389/fcomp.2023.1281100
Phillip Kerger, Ryoji Miyazaki
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引用次数: 0

摘要

我们研究了一个通过受限玻尔兹曼机(rbm)进行二值图像去噪的框架,该框架引入了一个适合量子退火的二次无约束二值优化(QUBO)形式的去噪目标。通过平衡由训练的RBM学习到的分布和噪声图像衍生的惩罚项来实现去噪目标。假设目标分布已经很好地逼近,我们推导出惩罚参数的统计最优选择,并进一步提出一个经验支持的修正,使该方法对理想假设具有鲁棒性。我们还表明,在附加的假设下,通过我们的方法获得的去噪图像在期望上比有噪图像更接近无噪图像。当我们将该模型作为图像去噪模型时,它可以应用于任何二进制数据。由于QUBO公式非常适合在量子退加工机上实现,我们在D-Wave Advantage机器上测试了该模型,并通过经典启发式近似QUBO解决方案对当前量子退加工机过于庞大的数据进行了测试。
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Quantum image denoising: a framework via Boltzmann machines, QUBO, and quantum annealing
We investigate a framework for binary image denoising via restricted Boltzmann machines (RBMs) that introduces a denoising objective in quadratic unconstrained binary optimization (QUBO) form well-suited for quantum annealing. The denoising objective is attained by balancing the distribution learned by a trained RBM with a penalty term for derivations from the noisy image. We derive the statistically optimal choice of the penalty parameter assuming the target distribution has been well-approximated, and further suggest an empirically supported modification to make the method robust to that idealistic assumption. We also show under additional assumptions that the denoised images attained by our method are, in expectation, strictly closer to the noise-free images than the noisy images are. While we frame the model as an image denoising model, it can be applied to any binary data. As the QUBO formulation is well-suited for implementation on quantum annealers, we test the model on a D-Wave Advantage machine, and also test on data too large for current quantum annealers by approximating QUBO solutions through classical heuristics.
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
期刊最新文献
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