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2021
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13 pages
1 file
We study the relation of causal influence between input systems of a reversible evolution and its output systems, in the context of operational probabilistic theories. We analyse two different definitions that are borrowed from the literature on quantum theory -- where they are equivalent. One is the notion based on signalling, and the other one is the notion used to define the neighbourhood of a cell in a quantum cellular automaton. The latter definition, that we adopt in the general scenario, turns out to be strictly weaker than the former: it is possible for a system to have causal influence on another one without signalling to it. Remarkably, the counterexample comes from classical theory, where the proposed notion of causal influence determines a redefinition of the neighbourhood of a cell in cellular automata. We stress that, according to our definition, it is impossible anyway to have causal influence in the absence of an interaction, e.g.~in a Bell-like scenario. We study va...
Quantum, 2020
The theory of cellular automata in operational probabilistic theories is developed. We start introducing the composition of infinitely many elementary systems, and then use this notion to define update rules for such infinite composite systems. The notion of causal influence is introduced, and its relation with the usual property of signalling is discussed. We then introduce homogeneity, namely the property of an update rule to evolve every system in the same way, and prove that systems evolving by a homogeneous rule always correspond to vertices of a Cayley graph. Next, we define the notion of locality for update rules. Cellular automata are then defined as homogeneous and local update rules. Finally, we prove a general version of the wrapping lemma, that connects CA on different Cayley graphs sharing some small-scale structure of neighbourhoods.
2020
Any measurement is intended to provide information on a system, namely knowledge about its state. However, we learn from quantum theory that it is generally impossible to extract information without disturbing the state of the system or its correlations with other systems. In this paper we address the issue of the interplay between information and disturbance for a general operational probabilistic theory. The traditional notion of disturbance considers the fate of the system state after the measurement. However, the fact that the system state is left untouched ensures that also correlations are preserved only in the presence of local discriminability. Here we provide the definition of disturbance that is appropriate for a general theory. Moreover, since in a theory without causality information can be gathered also on the effect, we generalise the notion of no-information test. We then prove an equivalent condition for no-information without disturbance---atomicity of the identity-...
2021
The paper is concentrated on the special changes of the conception of causalityfrom quantum mechanics to quantum information meaning as a background the revolution implemented by the former to classical physics and science after Max Born’s probabilistic reinterpretation of wave function. Those changes can be enumerated so: (1) quantum information describes the general case of the relation of two wave functions, and particularly, the causal amendment of a single one; (2) it keeps the physical description to be causal by the conservation of quantum information and in accordance with Born’s interpretation; (3) it introduces inverse causality, “backwards in time”, observable “forwards in time” as the fundamentally random probability density distribution of all possible measurements of any physical quantity in quantum mechanics; (4) it involves a kind of “bidirectional causality” unifying (4.1) the classical determinism of cause and effect, (4.2) the probabilistic causality of quantum me...
arXiv: Quantum Physics, 2019
It is known that the classical framework of causal models is not general enough to allow for causal reasoning about quantum systems. Efforts have been devoted towards generalization of the classical framework to the quantum case, with the aim of providing a framework in which cause-effect relations between quantum systems, and their connection with empirically observed data, can be rigorously analyzed. Building on the results of Allen et al., Phys. Rev. X 7, 031021 (2017), we present a fully-fledged framework of quantum causal models. The approach situates causal relations in unitary transformations, in analogy with an approach to classical causal models that assumes underlying determinism and situates causal relations in functional dependences between variables. We show that for any quantum causal model, there exists a corresponding unitary circuit, with appropriate causal structure, such that the quantum causal model is returned when marginalising over latent systems, and vice ver...
Objectivity does not necessarily require deterministic causation, unlike in certain interpretations of Kant's epistemology. But the very structure of the probabilities used by a physical theory is capable of bearing the trace of a constitution of objectivity in Kant’s sense. Accordingly, an examination of the differences between classical and quantum probabilities is performed. It is shown that quantum probabilities carry the mark of the contextuality of the phenomena to which they apply. Conversely, certain conditions that have the form of Bell’s inequalities carry the mark of decontextualization. In other terms, quantum theories include both a sign of the limit of objectivity, and a method to make an objective use of this limit.
Foundations of Physics, 2020
The formalism of general probabilistic theories provides a universal paradigm that is suitable for describing various physical systems including classical and quantum ones as particular cases. Contrary to the usual no-restriction hypothesis, the set of accessible meters within a given theory can be limited for different reasons, and this raises a question of what restrictions on meters are operationally relevant. We argue that all operational restrictions must be closed under simulation, where the simulation scheme involves mixing and classical post-processing of meters. We distinguish three classes of such operational restrictions: restrictions on meters originating from restrictions on effects; restrictions on meters that do not restrict the set of effects in any way; and all other restrictions. We fully characterize the first class of restrictions and discuss its connection to convex effect subalgebras. We show that the restrictions belonging to the second class can impose severe...
). We consider two entropic quantities, which we term measurement and mixing entropy. In classical and quantum theory, they are equal, being given by the Shannon and von Neumann entropies respectively; in general, however, they are very different. In particular, while measurement entropy is easily seen to be concave, mixing entropy need not be. In fact, as we show, mixing entropy is not concave whenever the state space is a non-simplicial polytope. Thus, the condition that measurement and mixing entropies coincide is a strong constraint on possible theories. We call theories with this property monoentropic. Measurement entropy is subadditive, but not in general strongly subadditive. Equivalently, if we define the mutual information between two systems A and B by the usual formula I(A:B) = H(A) + H(B) - H(AB) where H denotes the measurement entropy and AB is a non-signaling composite of A and B, then it can happen that I(A:BC) < I(A:B). This is relevant to information causality in the sense of Pawlowski et al.: we show that any monoentropic non-signaling theory in which measurement entropy is strongly subadditive, and also satisfies a version of the Holevo bound, is informationally causal, and on the other hand we observe that Popescu-Rohrlich boxes, which violate information causality, also violate strong subadditivity. We also explore the interplay between measurement and mixing entropy and various natural conditions on theories that arise in quantum axiomatics.
Intervention theories of causality define a relationship as causal if appropriately specified interventions to manipulate a putative cause tend to produce changes in the putative effect. Interventionist causal theories are commonly formalized by using directed graphs to represent causal relationships, local probability models to quantify the relationship between cause and effect, and a special kind of conditioning operator to represent the effects of interventions. Such a formal model represents a family of joint probability distributions, one for each allowable intervention policy. This paper interprets the von Neumann formalization of quantum theory as an interventionist theory of causality, describes its relationship to interventionist theories popular in the artificial intelligence literature, and presents a new family of graphical models that extends causal Bayesian networks to quantum systems.
2007
There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell's first statement of his notorious theorem in 1966. As is well known, its focus has mainly been the so-called Einstein-Podolsky-Rosen ("EPR") thought experiment, and the ensuing observed correlations in real EPR like experiments. But although implicitly the discussion goes as far back as Bell's work, it is only in the last two decades that it has become recognizably and explicitly a debate about causal inference in the quantum realm. The bulk of this paper is devoted to a review of three influential arguments in the philosophical literature that aim to show that causal models for the EPR correlations are impossible, due to Bas Van Fraassen, Daniel Hausman and Huw Price. I contend that all these arguments are inconclusive since they contain premises or presuppositions that are false, unwarranted, or at least controversial. Five different causal models are outlined that seem perfectly viable for the EPR correlations. These models are then employed to illustrate various difficulties with the premises and presuppositions underlying Van Fraassen's, Hausman's and Price's arguments. In all cases it is argued that the difficulties cut deep against these authors' own theories of causation and causal inference. My conclusions are that causal models for the EPR correlations certainly remain viable, that philosophical work is still required to assess their relative virtues, and that in any case the mere theoretical conceivability and empirical possibility of these models sheds deep doubts over Van Fraassen's, Hausman's and (important elements in) Price's theories of causation and causal inference. * I want to thank the audience at my talk to the Causality and Probability in the Sciences conference at the University of Kent, as well as two anonymous referees for helpful questions and comments. Thanks also to Iñaki San Pedro for his feedback, and his help with the production of the paper. Research towards this paper has been funded by project HUM2005-07187-C03-01 of the Spanish Ministry of Education and Science. 10 The qualification "in the past of A and B" is anachronistic, and very much my own. Reichenbach thought that open forks could be used to define the direction of time, so to say of an open fork that it is oriented towards the future (or, as I say above, that the screener off must lie in the common past of A and B) would just amount, in Reichenbach's theory, to the trivial truism that an open fork is oriented as an open fork. 11 And, conversely, if C screens off A from B, and C lies in the past of A and B, then C, A, B form an open fork.
International Journal of Theoretical Physics, 2016
We provide mathematicaly rigorous justification of using term probability in connection to the so called non-signalling theories, known also as Popescu's and Rohrlich's box worlds. No only do we prove correctness of these models (in the sense that they describe composite system of two independent subsystems) but we obtain new properties of non-signalling boxes and expose new tools for further investigation. Moreover, it allows strightforward generalization to more complicated systems.
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