Playlist: 23 videos
Quantum Physics and Statistical Causal Models
0:48:26
Sally Shrapnel (University of Queensland)
https://simons.berkeley.edu/talks/causal-discovery-quantum-context
Quantum Physics and Statistical Causal Models
This talk introduces quantum causal modelling and describes existing quantum causal discovery methods. Important distinctions between familiar classical causal concepts and their quantum counterparts will be discussed, and example use cases for quantum causal discovery presented. Some potential opportunities for classical causal discovery experts to contribute to this fledgling field will also be outlined.
Visit talk page
https://simons.berkeley.edu/talks/causal-discovery-quantum-context
Quantum Physics and Statistical Causal Models
This talk introduces quantum causal modelling and describes existing quantum causal discovery methods. Important distinctions between familiar classical causal concepts and their quantum counterparts will be discussed, and example use cases for quantum causal discovery presented. Some potential opportunities for classical causal discovery experts to contribute to this fledgling field will also be outlined.
0:37:30
Michael Weissman (University of Illinois Urbana-Champaign)
https://simons.berkeley.edu/talks/why-born-probabilities-na-also-fine-kind-topic-though-fun
Quantum Physics and Statistical Causal Models
The origin of the Born probability rule in unitary (Many Worlds) quantum mechanics is obscure. Many formal arguments claim to show it is the only consistent rule but all explicitly or implicitly assume that the probabilities must be expressible as products of probabilities of events. I.e. the probability that Schroedinger's cat is alive does not change after the experiment depending on whether it has quantum fleas. This rule is needed for the world to make sense but does not flow naturally from quantum dynamics. Mallah* has proposed a many-computations approach in which the rule could follow naturally if the quantum state we usually describe is superimposed on a background of quantum noise following only the unitary dynamics. I suggest that the background is expected, and our low-entropy component remains the only mystery. https://arxiv.org/abs/0709.0544
Visit talk page
https://simons.berkeley.edu/talks/why-born-probabilities-na-also-fine-kind-topic-though-fun
Quantum Physics and Statistical Causal Models
The origin of the Born probability rule in unitary (Many Worlds) quantum mechanics is obscure. Many formal arguments claim to show it is the only consistent rule but all explicitly or implicitly assume that the probabilities must be expressible as products of probabilities of events. I.e. the probability that Schroedinger's cat is alive does not change after the experiment depending on whether it has quantum fleas. This rule is needed for the world to make sense but does not flow naturally from quantum dynamics. Mallah* has proposed a many-computations approach in which the rule could follow naturally if the quantum state we usually describe is superimposed on a background of quantum noise following only the unitary dynamics. I suggest that the background is expected, and our low-entropy component remains the only mystery. https://arxiv.org/abs/0709.0544
0:49:51
Rob Spekkens (Perimeter Institute)
https://simons.berkeley.edu/talks/adjudicating-between-different-causal-accounts-bell-inequality-violations
Quantum Physics and Statistical Causal Models
A longstanding objective in the field of quantum foundations is to obtain a satisfactory realist interpretation of quantum theory. A particular take on this question (inspired in part by the framework of causal inference) is that such realism is achieved just in case one can provide a satisfactory causal explanation of statistical correlations. Bell’s theorem, however, demonstrates that certain quantum correlations resist explanation under seemingly natural assumptions about the causal structure and the nature of the parameters in the causal model. There are consequently two types of proposal for how to provide a causal account of a Bell experiment: (i) those that are parametrically conservative and structurally radical, such as causal models where the parameters are conditional probability distributions (termed 'classical causal models') but where one posits inter-lab causal influences or superdeterminism, and (ii) those that are parametrically radical and structurally conservative, such as models where the labs are taken to be connected only by a common cause but where conditional probabilities are replaced by conditional density operators (a quantum generalization of the notion of a causal model). In this talk, I will describe a proposal for how to adjudicate between these causal models. On the theoretical side, this is achieved by appealing to a methodological principle endorsed by Leibniz and Einstein concerning the interplay between empirical observations and the ontological theory that aims to explain them. On the experimental side, this is achieved by comparing the relative predictive power of the causal models using a train-and-test approach to statistical model selection.
Visit talk page
https://simons.berkeley.edu/talks/adjudicating-between-different-causal-accounts-bell-inequality-violations
Quantum Physics and Statistical Causal Models
A longstanding objective in the field of quantum foundations is to obtain a satisfactory realist interpretation of quantum theory. A particular take on this question (inspired in part by the framework of causal inference) is that such realism is achieved just in case one can provide a satisfactory causal explanation of statistical correlations. Bell’s theorem, however, demonstrates that certain quantum correlations resist explanation under seemingly natural assumptions about the causal structure and the nature of the parameters in the causal model. There are consequently two types of proposal for how to provide a causal account of a Bell experiment: (i) those that are parametrically conservative and structurally radical, such as causal models where the parameters are conditional probability distributions (termed 'classical causal models') but where one posits inter-lab causal influences or superdeterminism, and (ii) those that are parametrically radical and structurally conservative, such as models where the labs are taken to be connected only by a common cause but where conditional probabilities are replaced by conditional density operators (a quantum generalization of the notion of a causal model). In this talk, I will describe a proposal for how to adjudicate between these causal models. On the theoretical side, this is achieved by appealing to a methodological principle endorsed by Leibniz and Einstein concerning the interplay between empirical observations and the ontological theory that aims to explain them. On the experimental side, this is achieved by comparing the relative predictive power of the causal models using a train-and-test approach to statistical model selection.