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Computer Science - Machine Learning

Mixed-signal analog/digital circuits can emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog circuits are sensitive to process-induced variation among transistors in a chip ("device mismatch"). For neuromorphic implementation of Spiking Neural Networks (SNNs), mismatch causes parameter variation between identically-configured neurons and synapses. Each chip therefore exhibits a different distribution of neural parameters, causing deployed networks to respond differently between chips. Current solutions to mitigate mismatch based on per-chip calibration or on-chip learning entail increased design complexity, area and cost, making deployment of neuromorphic devices expensive and difficult. Here we present a supervised learning approach that addresses this challenge by maximizing robustness to mismatch and other common sources of noise. Our method trains SNNs to perform temporal classification tasks by mimicking a pre-trained dynamical system, using a local learning rule adapted from non-linear control theory. We demonstrate our method on two tasks requiring memory, and measure the robustness of our approach to several forms of noise and mismatch. We show that our approach is more robust than several common alternatives for training SNNs. Our method provides a viable way to robustly deploy pre-trained networks on mixed-signal neuromorphic hardware, without requiring per-device training or calibration.

Quantum Physics

Despite the increasing interest, the research field which studies the concepts of work and heat at quantum level has suffered from two main drawbacks: first, the difficulty to properly define and measure the work, heat and internal energy variation in a quantum system and, second, the lack of experiments. Here, we report for the first time the full experimental characterization of the dissipated heat, work and internal energy variation in a two-level quantum system interacting with an engineered environment. We use the IBMQ quantum computer to implement the driven system's dynamics in a dissipative environment. The experimental data allow us to construct quasi-probability distribution functions from which we recover the correct averages of work, heat and internal energy variation in the dissipative processes. Interestingly, by increasing the environment coupling strength, we observe a reduction of the pure quantum features of the energy exchange processes that we interpret as the emergence of the classical limit. This makes the present approach a privileged tool to study, understand and exploit quantum effects in energy exchanges.
Comment: 10 pages, 5 figures

Condensed Matter - Superconductivity, High Energy Physics - Theory, and Quantum Physics

From the sixties a deep and surprising connection has followed the development of superconductivity and quantum field theory. The Anderson-Higgs mechanism and the similarities between the Dirac and Bogoliubov-de Gennes equations are the most intriguing examples. In this last analogy, the massive Dirac particle is identified with a quasiparticle excitation and the fermion mass energy with the superconducting gap energy. Here we follow further this parallelism and show that it predicts an outstanding phenomenon: the superconducting Schwinger effect (SSE). As in the quantum electrodynamics Schwinger effect, where an electron-positron couple is created from the vacuum by an intense electric field, we show that an electrostatic field can generate two coherent excitations from the superconducting ground-state condensate. Differently from the dissipative thermal excitation, these form a new macroscopically coherent and dissipationless state. We discuss how the superconducting state is weakened by the creation of this kind of excitations. In addition to shed a different light and suggest a method for the experimental verification of the Schwinger effect, our results pave the way to the understanding and exploitation of the interaction between superconductors and electric fields.
Comment: 12 pages, 3 figures

Computer Science - Robotics and Computer Science - Artificial Intelligence

Formalisms inspired by Quantum theory have been used in Cognitive Science for decades. Indeed, Quantum-Like (QL) approaches provide descriptive features that are inherently suitable for perception, cognition, and decision processing. A preliminary study on the feasibility of a QL robot perception model has been carried out for a robot with limited sensing capabilities. In this paper, we generalize such a model for multi-sensory inputs, creating a multidimensional world representation directly based on sensor readings. Given a 3-dimensional case study, we highlight how this model provides a compact and elegant representation, embodying features that are extremely useful for modeling uncertainty and decision. Moreover, the model enables to naturally define query operators to inspect any world state, which answers quantifies the robot's degree of belief on that state.
Comment: Paper submitted to the 17th International Symposium on Experimental Robotics, Malta, Nov. 9-12, 2020

Computer Science - Robotics and Quantum Physics

Formalisms based on quantum theory have been used in Cognitive Science for decades due to their descriptive features. A quantum-like (QL) approach provides descriptive features such as state superposition and probabilistic interference behavior. Moreover, quantum systems dynamics have been found isomorphic to cognitive or biological systems dynamics. The objective of this paper is to study the feasibility of a QL perception model for a robot with limited sensing capabilities. We introduce a case study, we highlight its limitations, and we investigate and analyze actual robot behaviors through simulations, while actual implementations based on quantum devices encounter errors for unbalanced situations. In order to investigate QL models for robot behavior, and to study the advantages leveraged by QL approaches for robot knowledge representation and processing, we argue that it is preferable to proceed with simulation-oriented techniques rather than actual realizations on quantum backends.
Comment: Paper submitted to 29th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2020, Naples, Italy