Exploring alternatives to quantum nonlocality



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Recording Details

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PIRSA Number: 
20120023

Abstract

In recent years, it has become increasingly well-known that nearly all the major no-go theorems in quantum foundations can be circumvented by violating a single assumption: the hidden variables (that determine the outcomes) are uncorrelated with the measurement settings. A hidden-variable theory that violates this assumption can be local, separable, non-contextual and have an epistemic quantum state. Such a theory would be particularly well-suited to relativistic contexts. Are such theories actually feasible? In this talk, we discuss some results on the two physical options to violate this assumption: superdeterminism and retrocausality. 

Developing an intuitive criticism by Bell, we show that superdeterministic models are conspiratorial in a mathematically well-defined sense in two separate ways. In the first approach, we use the concept of quantum nonequilibrium to show that superdeterministic models require finetuning so that the measurement statistics do not depend on the details of how the measurement settings are chosen. In the second approach, we show (without using quantum non-equilibrium) that an arbitrarily large amount of superdeterministic correlation is needed for such models to be consistent. Along the way, we discuss an apparent paradox involving nonlocal signalling in a local superdeterministic model. 

Next, we use retrocausality to build a local, separable, psi-epistemic hidden-variable model of Bell correlations with pilot-waves in physical space. We generalise the model to describe a relativistic Bell scenario where one of the wings experiences time-dilation effects. We show, by discussing the difficulties faced by other hidden-variable approaches in describing this scenario, that the relativistic properties of the model play an important role here (otherwise ornamental in the standard Bell scenario). We also discuss the technical difficulties in applying quantum field theory to recover the model's predictions.