Quantum Causal Inference in the Presence of Hidden Common Causes: an Entropic Approach

• URL: http://arxiv.org/abs/2104.13227v1
• Date: Sat, 24 Apr 2021 22:45:50 GMT
• Title: Quantum Causal Inference in the Presence of Hidden Common Causes: an Entropic Approach
• Authors: Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob
• Abstract summary: We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links. This approach can lay the foundations of identifying originators of malicious activity on future multi-node quantum networks.
• Score: 34.77250498401055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum causality is an emerging field of study which has the potential to greatly advance our understanding of quantum systems. One of the most important problems in quantum causality is linked to this prominent aphorism that states correlation does not mean causation. A direct generalization of the existing causal inference techniques to the quantum domain is not possible due to superposition and entanglement. We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. For this purpose, we leverage the concept of conditional density matrices to develop a scalable algorithmic approach for inferring causality in the presence of latent confounders (common causes) in quantum systems. We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links, where it is validated that the input before noise as a latent confounder is the cause of the noisy outputs. We also demonstrate that the proposed approach outperforms the results of classical causal inference even when the variables are classical by exploiting quantum dependence between variables through density matrices rather than joint probability distributions. Thus, the proposed approach unifies classical and quantum causal inference in a principled way. This successful inference on a synthetic quantum dataset can lay the foundations of identifying originators of malicious activity on future multi-node quantum networks.

Related papers

• Power Characterization of Noisy Quantum Kernels [52.47151453259434]
  We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
  arXiv  Detail & Related papers  (2024-01-31T01:02:16Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Realization of algorithmic identification of cause and effect in quantum correlations [13.164461672508711]
  A quantum perspective of correlations holds the promise of overcoming the limitation by Reichenbach's principle. We have devised an algorithm capable of identifying any two-qubit statistical correlations generated by the two basic causal structures under an observational scenario. Our result paves the way for studying quantum causality in general settings.
  arXiv  Detail & Related papers  (2023-04-20T10:06:38Z)
• Information causality in multipartite scenarios [1.0312968200748118]
  We report a new formulation of the information causality principle in multipartite scenarios. We prove that such inequalities hold for all quantum resources, and forbid some stronger-than-quantum ones.
  arXiv  Detail & Related papers  (2022-12-11T20:32:21Z)
• Quantifying Quantum Causal Influences [0.8399688944263843]
  We propose the quantum version of the most common causality quantifier, the average causal effect (ACE), measuring how much a target quantum system is changed by interventions on its presumed cause. Not only it offers an innate manner to quantify causation in two-qubit gates but also in alternative quantum computation models such as the measurement-based version. Considering quantum teleportation, we show that any pure entangled state offers an advantage in terms of causal effects as compared to separable states.
  arXiv  Detail & Related papers  (2022-10-09T17:17:42Z)
• Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy [67.02951777522547]
  We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs) We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution. Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
  arXiv  Detail & Related papers  (2022-05-10T08:05:59Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Efficient Distinction between Quantum Direct and Common Causes and its Experimental Verification [15.082156478846654]
  We introduce a quantity named Causal Determinant' to efficiently identify the quantum causal structures between two quantum systems. According to the causal determinant, the quantum direct cause imposed by an arbitrary unitary operator can be perfectly discriminated with the quantum common cause.
  arXiv  Detail & Related papers  (2022-03-05T14:12:37Z)
• Experimental violations of Leggett-Garg's inequalities on a quantum computer [77.34726150561087]
  We experimentally observe the violations of Leggett-Garg-Bell's inequalities on single and multi-qubit systems. Our analysis highlights the limits of nowadays quantum platforms, showing that the above-mentioned correlation functions deviate from theoretical prediction as the number of qubits and the depth of the circuit grow.
  arXiv  Detail & Related papers  (2021-09-06T14:35:15Z)
• Quantum Entropic Causal Inference [30.939150842529052]
  We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links.
  arXiv  Detail & Related papers  (2021-02-23T15:51:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.