设计消散以缓解贫瘠高原

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Antonio Sannia, Francesco Tacchino, Ivano Tavernelli, Gian Luca Giorgi, Roberta Zambrini
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引用次数: 0

摘要

变量量子算法是在噪声量子计算机上解决优化问题的一种强大方法,具有从化学到机器学习的广泛潜在应用。然而,它们在实际应用中的性能关键取决于量子电路训练的有效性,而这种有效性可能会受到贫瘠高原等现象的严重限制。一般来说,耗散对量子算法是有害的,而噪声本身实际上也会诱发贫瘠高原,在此,我们将介绍如何在每个单元量子电路层之后加入适当设计的马尔可夫损耗,从而实现量子模型的可训练性。我们确定了损耗过程所需的形式,并确定其优化是有效的。我们在一个合成和实际量子化学例子中对我们建议的通用性进行了基准测试,证明了它在不同领域的有效性和潜在影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Engineered dissipation to mitigate barren plateaus

Engineered dissipation to mitigate barren plateaus

Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.

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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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