Discovering Quantum Causal Models

Shrapnel, Sally

doi:10.1093/bjps/axx044

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…However, the interventionist schemes cannot be directly applied to the quantum case. The dilemma is presented as a choice between relinquishing one of two assumptions: the Causal Markov Condition or faithfulness (no-fine-tuning) [16]. Instead of trying to modify one of the existing assumptions, another probably better approach to avoid such dilemma is reformulating causal models in a way that makes direct use of the quantum formalism and providing a quantum interventionist framework for Bayesian inference as well as causal inference [17].…”

Section: Discussionmentioning

confidence: 99%

Quantum observation scheme universally identifying causalities from correlations

Zhang,

Hou,

Song

2019

Preprint

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It has long been recognized as a difficult problem to determine whether the observed statistical correlation between two classical variables arise from causality or from common causes. Recent research has shown that in quantum theoretical framework, the mechanisms of entanglement and quantum coherence provide an advantage in tackling this problem. In some particular cases, quantum common causes and quantum causality can be effectively distinguished using observations only. However, these solutions do not apply to all cases. There still exist enormous cases in which quantum common causes and quantum causality can not be distinguished. In this paper, along the line of considering unitary transformation as causality in the quantum world, we formally show quantum common causes and quantum causality are universally separable. Based on the analysis, we further provide a general method to discriminate the two.

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Section: Discussionmentioning

confidence: 99%

Quantum observation scheme universally identifying causalities from correlations

Zhang,

Hou,

Song

2019

Preprint

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“…6 Of course, non-metaphysical notions of causation also have this advantage but they still need to be generalized to non-causal cases. 7 See for example Shrapnel [13] for a philosophical discussion of this case and Costa and Shrapnel [19] and Shrapnel [20] for a suggestion for QCMs. 8 For example, in the quantum causal models developed by Costa & Shrapnel [19], the nodes of causal models are treated very differently in the quantum case compared to the classical causal case (such as that of Pearl [21]).…”

Section: Endnotesmentioning

confidence: 99%

Network explanations and explanatory directionality

Jansson

2020

Phil. Trans. R. Soc. B

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Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

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“…More recently, these problems have inspired research into studies of quantum correlations and causal order [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64]. While it is possible to account for Bell violations using classical causal models, this will entail solutions based on, for instance, superluminal causal influences [46,[65][66][67], retrocausality [11,22,41,68], or super-determinism [69].…”

Section: Introductionmentioning

confidence: 99%

“…Experiments proposed by Chaves, Lemos and Pienaar appear to confirm retrocausal effects, but only if one constrains theories to those with a limited dimensionality [32,36,38,39]. Quantum causal models that are based on conditional density operators rather than conditional probabilities have been developed to circumvent some of these issues [54,56,57,71]. However, it is not clear how to present a unified treatment combining realism with causality.…”

Section: Introductionmentioning

confidence: 99%

A quantum phase-space equivalence leads to hidden causal loops in a model for measurement consistent with macroscopic realism

Reid¹,

Drummond²

2022

Preprint

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We analyze the measurement problem by simulating the dynamics of amplitudes associated with backward and forward propagating stochastic equations in a realistic, objective model. By deriving a theorem based on conditional probabilities at the boundaries, we obtain trajectories equivalent to quantum mechanics. The joint densities of complementary variables give the correct quantum probability distribution. We model a measurement on a single-mode system via parametric amplification, showing how a system prepared in a superposition of eigenstates evolves to produce distinct macroscopic outcomes consistent with Born's rule. The amplified variable corresponds to a backward propagating trajectory. Sampling is carried out according to a future boundary condition determined by the measurement setting. A distinctive feature is the existence of vacuum noise associated with an eigenstate. This noise remains constant at the level of the quantum vacuum throughout the dynamics and is not macroscopically measurable. The precise fluctuations are specified retrocausally, and originate from past and future boundary conditions. Where the separation of eigenstates greatly exceeds the vacuum, we argue consistency with macroscopic realism and causality: the macroscopic outcome of the measurement can be considered determined prior to the measurement. This leads to hybrid macro-causal and micro-retrocausal relations. The states inferred for coupled trajectories conditioned on a measured outcome are not quantum states. They are defined more precisely than allowed by the uncertainty principle, although they approach eigenstates with amplification in the limit of a macroscopic superposition. A full collapse into an eigenstate is simulated by coupling to a second mode, and occurs with loss of information. The model permits Einstein-Podolsky-Rosen correlations and Bell non-locality.

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Discovering Quantum Causal Models

Cited by 8 publications

References 44 publications

Quantum observation scheme universally identifying causalities from correlations

Quantum observation scheme universally identifying causalities from correlations

Network explanations and explanatory directionality

A quantum phase-space equivalence leads to hidden causal loops in a model for measurement consistent with macroscopic realism

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