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arXiv 2209.02016quant-ph
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用于因果假设检验的可扩展量子门实现

A scalable quantum gate-based implementation for causal hypothesis testing

Akash Kundu, Tamal Acharya, Aritra Sarkar

AI总结:

本文基于既有因果假设检验形式体系,在 IBM Qiskit 上实现可扩展量子门算法,修正错误概率并验证量子加速。

摘要:

在本工作中,我们研究用于加速因果推断的量子计算算法。具体而言,我们考虑《Nat Commun》10, 1472 (2019) 中提出的因果假设检验形式体系。我们开发了一种量子电路实现,并用其证明先前工作中引入的错误概率需要修正。在理论描述之后,实际场景被构建为 IBM Qiskit 上的一种可扩展量子门算法。我们给出嵌入因果假设的预言机构造,并评估相关的门复杂度。此外,我们在模拟器平台上的实验验证了所预测的加速效果。我们讨论了该框架在生物信息学和通用人工智能中因果推断用例的应用。

英文摘要:

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [\textit{Nat Commun} 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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