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On the Significance of Intermediate Latents: Distinguishing Quantum Causal Scenarios with Indistinguishable Classical Analogs
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Abstract
The use of graphical models to represent causal hypotheses has enabled revolutionary progress in the study of the foundations of quantum theory. Here we consider directed acyclic graphs each of which contains both nodes representing observed variables as well as nodes representing latent or hidden variables. When comparing distinct causal structure, a natural question to ask is if they can explain distinct sets of observable distributions or not. Statisticians have developed a great variety of tools for resolving such questions under the assumption that latent nodes be interpreted classically. Here we highlight how the change to a quantum interpretation of the latent nodes induces distinctions between causal scenarios that would be classically indistinguishable. We especially concentrate on quantum scenarios containing latent nodes with at least one latent parent, a.k.a. possessing intermediate latents. This initial survey demonstrates that many such quantum processes can be operationally distinguished by considerations related to monogamy of nonlocality, especially when computationally aided by a hierarchy of semidefinite relaxations which we tailor for the study such scenarios. We conclude by clarifying the challenges that prevent the generalization of this work, calling attention to open problems regarding observational (in)equivalence of quantum causal structures with intermediate latents.
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We introduce Inflation, a Python library for assessing whether an observed probability distribution is compatible with a causal explanation. This is a central problem in both theoretical and applied sciences, which has recently witnessed significant advances from the area of quantum nonlocality, namely, in the development of inflation techniques. Inflation is an extensible toolkit that is capable of solving pure causal compatibility problems and optimization over (relaxations of) sets of compatible correlations in both the classical and quantum paradigms. The library is designed to be modular and with the ability of being ready-to-use, while keeping an easy access to low-level objects for custom modifications.
Causality is a seminal concept in science: Any research discipline, from sociology and medicine to physics and chemistry, aims at understanding the causes that could explain the correlations observed among some measured variables. While several methods exist to characterize classical causal models, no general construction is known for the quantum case. In this work, we present quantum inflation, a systematic technique to falsify if a given quantum causal model is compatible with some observed correlations. We demonstrate the power of the technique by reproducing known results and solving open problems for some paradigmatic examples of causal networks. Our results may find applications in many fields: from the characterization of correlations in quantum networks to the study of quantum effects in thermodynamic and biological processes.
Classical and quantum physics impose different constraints on the joint probability distributions of observed variables in a causal structure. These differences mean that certain correlations can be certified as nonclassical, which has both foundational and practical importance. Rather than working with the probability distribution itself, it can instead be convenient to work with the entropies of the observed variables. In the Bell causal structure with two inputs and outputs per party, a technique that uses entropic inequalities is known that can always identify nonclassical correlations. Here we consider the analog of this technique in the generalization of this scenario to more outcomes. We identify a family of nonclassical correlations in the Bell scenario with two inputs and three outputs per party whose nonclassicality cannot be detected through the direct analog of the previous technique. We also show that use of Tsallis entropy instead of Shannon entropy does not help in this case. Furthermore, we give evidence that natural extensions of the technique also do not help. More precisely, our evidence suggests that even if we allow the observed correlations to be postprocessed according to a natural class of nonclassicality nongenerating operations, entropic inequalities for either the Shannon or Tsallis entropies cannot detect the nonclassicality, and hence that entropic inequalities are generally not sufficient to detect nonclassicality in the Bell causal structure.
The possibility of Bell inequality violations in quantum theory had a profound impact on our understanding of the correlations that can be shared by distant parties. Generalizing the concept of Bell nonlocality to networks leads to novel forms of correlations, the characterization of which is, however, challenging. Here, we investigate constraints on correlations in networks under the natural assumptions of no-signaling and independence of the sources. We consider the triangle network with binary outputs, and derive strong constraints on correlations even though the parties receive no input, i.e., each party performs a fixed measurement. We show that some of these constraints are tight, by constructing explicit local models (i.e. where sources distribute classical variables) that can saturate them. However, we also observe that other constraints can apparently not be saturated by local models, which opens the possibility of having nonlocal (but non-signaling) correlations in the triangle network with binary outputs.
A central question for causal inference is to decide whether a set of correlations fit a given causal structure. In general, this decision problem is computationally infeasible and hence several approaches have emerged that look for certificates of compatibility. Here we review several such approaches based on entropy. We bring together the key aspects of these entropic techniques with unified terminology, filling several gaps and establishing new connections regarding their relation, all illustrated with examples. We consider cases where unobserved causes are classical, quantum and post-quantum and discuss what entropic analyses tell us about the difference. This has applications to quantum cryptography, where it can be crucial to eliminate the possibility of classical causes. We discuss the achievements and limitations of the entropic approach in comparison to other techniques and point out the main open problems.
Reichenbach's principle asserts that if two observed variables are found to be correlated, then there should be a causal explanation of these correlations. Furthermore, if the explanation is in terms of a common cause, then the conditional probability distribution over the variables given the complete common cause should factorize. The principle is generalized by the formalism of causal models, in which the causal relationships among variables constrain the form of their joint probability distribution. In the quantum case, however, the observed correlations in Bell experiments cannot be explained in the manner Reichenbach's principle would seem to demand. Motivated by this, we introduce a quantum counterpart to the principle. We demonstrate that under the assumption that quantum dynamics is fundamentally unitary, if a quantum channel with input A and outputs B and C is compatible with A being a complete common cause of B and C, then it must factorize in a particular way. Finally, we show how to generalize our quantum version of Reichenbach's principle to a formalism for quantum causal models, and provide examples of how the formalism works.
Bayesian networks provide a powerful tool for reasoning about probabilistic
causation, used in many areas of science. They are, however, intrinsically
classical. In particular, Bayesian networks naturally yield the Bell
inequalities. Inspired by this connection, we generalise the formalism of
classical Bayesian networks in order to investigate non-classical correlations
in arbitrary causal structures. Our framework of `generalised Bayesian
networks' replaces latent variables with the resources of any generalised
probabilistic theory, most importantly quantum theory, but also, for example,
Popescu-Rohrlich boxes. We obtain three main sets of results. Firstly, we prove
that all of the observable conditional independences required by the classical
theory also hold in our generalisation; to obtain this, we extend the classical
d-separation theorem to our setting. Secondly, we find that the
theory-independent constraints on probabilities can go beyond these conditional
independences. For example we find that no probabilistic theory predicts
perfect correlation between three parties using only bipartite common causes.
Finally, we begin a classification of those causal structures, such as the Bell
scenario, that may yield a separation between classical, quantum and
general-probabilistic correlations.
We prove generic versions of the no-cloning and no-broadcasting theorems, applicable to essentially any non-classical finite-dimensional probabilistic model that satisfies a no-signaling criterion. This includes quantum theory as well as models supporting "super-quantum" correlations that violate the Bell inequalities to a larger extent than quantum theory. The proof of our no-broadcasting theorem is significantly more natural and more self-contained than others we have seen: we show that a set of states is broadcastable if, and only if, it is contained in a simplex whose vertices are cloneable, and therefore distinguishable by a single measurement. This necessary and sufficient condition generalizes the quantum requirement that a broadcastable set of states commute.
An active area of research in the fields of machine learning and statistics
is the development of causal discovery algorithms, the purpose of which is to
infer the causal relations that hold among a set of variables from the
correlations that these exhibit. We apply some of these algorithms to the
correlations that arise for entangled quantum systems. We show that they cannot
distinguish correlations that satisfy Bell inequalities from correlations that
violate Bell inequalities, and consequently that they cannot do justice to the
challenges of explaining certain quantum correlations causally. Nonetheless, by
adapting the conceptual tools of causal inference, we can show that any attempt
to provide a causal explanation of nonsignalling correlations that violate a
Bell inequality must contradict a core principle of these algorithms, namely,
that an observed statistical independence between variables should not be
explained by fine-tuning of the causal parameters. We demonstrate the need for
such fine-tuning for most of the causal mechanisms that have been proposed to
underlie Bell correlations, including superluminal causal influences,
superdeterminism (that is, a denial of freedom of choice of settings), and
retrocausal influences which do not introduce causal cycles.
Bell's Theorem witnesses that the predictions of quantum theory cannot be
reproduced by theories of local hidden variables in which observers can choose
their measurements independently of the source. Working out an idea of
Branciard, Rosset, Gisin and Pironio, we consider scenarios which feature
several sources, but no choice of measurement for the observers. Every Bell
scenario can be mapped into such a \emph{correlation scenario}, and Bell's
Theorem then discards those local hidden variable theories in which the sources
are independent. However, most correlation scenarios do not arise from Bell
scenarios, and we describe examples of (quantum) nonlocality in some of these
scenarios, while posing many open problems along the way. Some of our scenarios
have been considered before by mathematicians in the context of causal
inference.
We investigate general probabilistic theories in which every mixed state has a purification, unique up to reversible channels on the purifying system. We show that the purification principle is equivalent to the existence of a reversible realization of every physical process, namely that every physical process can be regarded as arising from a reversible interaction of the system with an environment, which is eventually discarded. From the purification principle we also construct an isomorphism between transformations and bipartite states that possesses all structural properties of the Choi Jamiolkowski isomorphism in quantum mechanics. Such an isomorphism allows one to prove most of the basic features of quantum mechanics, like e.g. existence of pure bipartite states giving perfect correlations in independent experiments, no information without disturbance, no joint discrimination of all pure states, no cloning, teleportation, no programming, no bit commitment, complementarity between correctable channels and deletion channels, characterization of entanglement-breaking channels as measure-and-prepare channels, and others, without resorting to the mathematical framework of Hilbert spaces.
We formulate information-theoretic Bell inequalities, which apply to any
pair of widely separated physical systems. If local realism holds, the
two systems must carry information consistent with the inequalities. Two
spin-s particles in a state of zero total spin violate these information
Bell inequalities.
We are interested in the problem of characterizing the correlations that arise when performing local measurements on separate quantum systems. In a previous work [Phys. Rev. Lett. 98, 010401 (2007)], we introduced an infinite hierarchy of conditions necessarily satisfied by any set of quantum correlations. Each of these conditions could be tested using semidefinite programming. We present here new results concerning this hierarchy. We prove in particular that it is complete, in the sense that any set of correlations satisfying every condition in the hierarchy has a quantum representation in terms of commuting measurements. Although our tests are conceived to rule out non-quantum correlations, and can in principle certify that a set of correlations is quantum only in the asymptotic limit where all tests are satisfied, we show that in some cases it is possible to conclude that a given set of correlations is quantum after performing only a finite number of tests. We provide a criterion to detect when such a situation arises, and we explain how to reconstruct the quantum states and measurement operators reproducing the given correlations. Finally, we present several applications of our approach. We use it in particular to bound the quantum violation of various Bell inequalities.
Understanding the interface between quantum and relativistic theories is crucial for fundamental and practical advances, especially given that key physical concepts such as causality take different forms in these theories. Bell’s no-go theorem reveals limits on classical processes, arising from relativistic causality principles. Considering whether similar fundamental limits exist on quantum processes, we derive no-go theorems for quantum experiments realizable in classical background spacetimes. We account for general processes allowed by quantum theory, including those with indefinite causal order (ICO), which have also been the subject of recent experiments. Our first theorem implies that realizations of ICO processes that do not violate relativistic causality must involve the nonlocalization of systems in spacetime. The second theorem shows that for any such realization of an ICO process, there exists a more fine-grained description in terms of a definite and acyclic causal order process. This enables a general reconciliation of quantum and relativistic notions of causality and, in particular, applies to experimental realizations of the quantum switch, a prominent ICO process. By showing what is impossible to achieve in classical spacetimes, these no-go results also offer insights into how causality and information processing may differ in future quantum experiments in relativistic regimes beyond classical spacetimes.
In this paper we provide a complete algebraic characterization of the model
implied by a Bayesian network with latent variables when the observed variables
are discrete. We show that it is algebraically equivalent to the so-called
nested Markov model, meaning that the two are the same up to inequality
constraints on the joint probabilities. The nested Markov model is therefore
the best possible approximation to the latent variable model whilst avoiding
inequalities, which are extremely complicated in general. Latent variable
models also suffer from difficulties of unidentifiable parameters and
non-regular asymptotics; in contrast the nested Markov model is fully
identifiable, represents a curved exponential family of known dimension, and
can easily be fitted using an explicit parameterization.
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers’ questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
Directed acyclic graph (DAG) models, also called Bayesian networks, impose
conditional independence constraints on a multivariate probability
distribution, and are widely used in probabilistic reasoning, machine learning
and causal inference. If latent variables are included in such a model, then
the set of possible marginal distributions over the remaining (observed)
variables is generally complex, and not represented by any DAG. Larger classes
of graphical models, such as ancestral graphs and acyclic directed mixed graphs
(ADMGs), have been introduced to overcome this; however, in this paper we show
that these classes of graphs are not rich enough to fully represent the range
of models which can arise as margins of DAG models.
We introduce a new class of hyper-graphs, called mDAGs, and a latent
projection operation to obtain an mDAG from the margin of a DAG. We show that
each distinct marginal of a DAG model is represented by at least one mDAG, and
provide graphical results towards characterizing when two such marginal models
are the same. Finally we show that mDAGs correctly capture the marginal
structure of causally-interpreted DAGs under interventions on the observed
variables.
The correlations that can be observed between a set of variables depend on
the causal structure underpinning them. Causal structures can be modeled using
directed acyclic graphs, where nodes represent variables and edges denote
functional dependencies. In this work, we describe a general algorithm for
computing information-theoretic constraints on the correlations that can arise
from a given interaction pattern, where we allow for classical as well as
quantum variables. We apply the general technique to two relevant cases: First,
we show that the principle of information causality appears naturally in our
framework and go on to generalize and strengthen it. Second, we derive bounds
on the correlations that can occur in a networked architecture, where a set of
few-body quantum systems is distributed among a larger number of parties.
If a photon of definite polarization encounters an excited atom, there is typically some nonvanishing probability that the atom will emit a second photon by stimulated emission. Such a photon is guaranteed to have the same polarization as the original photon. But is it possible by this or any other process to amplify a quantum state, that is, to produce several copies of a quantum system (the polarized photon in the present case) each having the same state as the original? If it were, the amplifying process could be used to ascertain the exact state of a quantum system: in the case of a photon, one could determine its polarization by first producing a beam of identically polarized copies and then measuring the Stokes parameters1. We show here that the linearity of quantum mechanics forbids such replication and that this conclusion holds for all quantum systems.
The concept of quantum transition is critically examined from the perspective of the modern quantum theory of measurement. Historically rooted in the famous quantum jump of the Old Quantum Theory, the transition idea survives today in experimental jargon due to (1) the notion of uncontrollable disturbance of a system by measurement operations and (2) the wave-packet reduction hypothesis in several forms. Explicit counterexamples to both (1) and (2) are presented in terms of quantum measurement theory. It is concluded that the idea of transition, or quantum jump, can no longer be rationally comprehended within the framework of contemporary physical theory.
In the conventional approach to quantum mechanics, indeterminism is an axiom and nonlocality is a theorem. We consider inverting the logical order, making nonlocality an axiom and indeterminism a theorem. Nonlocal superquantum correlations, preserving relativistic causality, can violate the CHSH inequality more strongly than any quantum correlations.
A recent proposal to achieve faster-than-light communication by means of an EPR-type experimental set-up is examined. We demonstrate that such superluminal communication is not possible. The crucial role of the linearity of the quantum mechanical evolution laws in preventing causal anomalies is stressed.
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A decompositional framework for process theories in spacetime
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