What is absent from contemplative neuroscience? Rethinking limits within the study of consciousness, experience, and meditation

This is the author accepted manuscript. The final version is available from Imprint Academic via the URL in this recordIn conveying experiences of meditation, the question of what exceeds or should resist description has been a recurrent topic of commentary in a wide array of literature—including religious doctrine, meditation guides (secular and religious), and contextual accounts written by historians and social scientists. Yet, to date, this question has not significantly informed neuroscientific studies on the effects of meditation on brain and behaviour, in large part—but not wholly—because of the disregard for first-person accounts of experience that still characterizes neuroscience in general. By juxtaposing perspectives from nonneuroscientific accounts on the tensions and questions raised by what is and is not expressed or expressible in words, this article paves the way for a new set of possibilities in experimental contemplative neuroscience

Structure-(in)dependent interpretation of phrases in humans and LSTMs

In this study, we compared the performance of a long short-term memory (LSTM) neural network to the behavior of human participants on a language task that requires hierarchically structured knowledge. We show that humans interpret ambiguous noun phrases, such as second blue ball, in line with their hierarchical constituent structure. LSTMs, instead, only do so after unambiguous training, and they do not systematically generalize to novel items. Overall, the results of our simulations indicate that a model can behave hierarchically without relying on hierarchical constituent structure

Hierarchy in language interpretation: Evidence from behavioural experiments and computational modelling

It has long been recognised that phrases and sentences are organised hierarchically, but many computational models of language treat them as sequences of words without computing constituent structure. Against this background, we conducted two experiments which showed that participants interpret ambiguous noun phrases, such as second blue ball, in terms of their abstract hierarchical structure rather than their linear surface order. When a neural network model was tested on this task, it could simulate such “hierarchical” behaviour. However, when we changed the training data such that they were not entirely unambiguous anymore, the model stopped generalising in a human-like way. It did not systematically generalise to novel items, and when it was trained on ambiguous trials, it strongly favoured the linear interpretation. We argue that these models should be endowed with a bias to make generalisations over hierarchical structure in order to be cognitively adequate models of human language

Dynamical phase error in interacting topological quantum memories

A local Hamiltonian with topological quantum order (TQO) has a robust ground-state degeneracy that makes it an excellent quantum memory candidate. This memory can be corrupted however if part of the state leaves the protected ground-state manifold and returns later with a dynamically accrued phase error. Here we analyze how TQO suppresses this process and use this to quantify the degree to which spectral densities in different topological sectors are correlated. We provide numerical verification of our results by modeling an interacting p-wave superconducting wire

Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies

Quantum control, which refers to the active manipulation of physical systems described by the laws of quantum mechanics, constitutes an essential ingredient for the development of quantum technology. Here we apply differentiable programming (DP) and natural evolution strategies (NES) to the optimal transport of Majorana zero modes in superconducting nanowires, a key element to the success of Majorana-based topological quantum computation. We formulate the motion control of Majorana zero modes as an optimization problem for which we propose a new categorization of four different regimes with respect to the critical velocity of the system and the total transport time. In addition to correctly recovering the anticipated smooth protocols in the adiabatic regime, our algorithms uncover efficient but strikingly counterintuitive motion strategies in the nonadiabatic regime. The emergent picture reveals a simple but high-fidelity strategy that makes use of pulselike jumps at the beginning and the end of the protocol with a period of constant velocity in between the jumps, which we dub the jump-move-jump protocol. We provide a transparent semianalytical picture, which uses the sudden approximation and a reformulation of the Majorana motion in a moving frame, to illuminate the key characteristics of the jump-move-jump control strategy. We verify that the jump-move-jump protocol remains robust against the presence of interactions or disorder, and corroborate its high efficacy on a realistic proximity-coupled nanowire model. Our results demonstrate that machine learning for quantum control can be applied efficiently to quantum many-body dynamical systems with performance levels that make it relevant to the realization of large-scale quantum technology