3,126 research outputs found
Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
Quantum systems interacting with an unknown environment are notoriously
difficult to model, especially in presence of non-Markovian and
non-perturbative effects. Here we introduce a neural network based approach,
which has the mathematical simplicity of the
Gorini-Kossakowski-Sudarshan-Lindblad master equation, but is able to model
non-Markovian effects in different regimes. This is achieved by using recurrent
neural networks for defining Lindblad operators that can keep track of memory
effects. Building upon this framework, we also introduce a neural network
architecture that is able to reproduce the entire quantum evolution, given an
initial state. As an application we study how to train these models for quantum
process tomography, showing that recurrent neural networks are accurate over
different times and regimes.Comment: 10 pages, 8 figure
Strategic adoption of logistics and supply chain management
© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to develop a thorough understanding of the adoption of logistics and supply chain management (SCM) in practice, particularly at a strategic level, through an investigation of the four perspectives taxonomy of the relationship between logistics and SCM. Design/methodology/approach: Based on a comprehensive literature review, three specific research questions are proposed. The empirical work addresses these questions and comprised three phases: focussed interviews, a questionnaire survey and focus groups. Findings: The findings provide a usage profile of the four perspectives and indicate a divergence between the understanding and adoption of logistics and SCM principles and concepts at a strategic level in firms. The findings also identify the critical success factors (CSFs) and inhibitors to success in addressing this divergence. Research limitations/implications: The insights generated using the authors’ methodologically pluralist research design could be built upon to include case studies, grounded theory and action research. Replicating the research in other geographical areas could facilitate international comparisons. Practical implications: The findings allow practitioners to compare their perspectives on the relationship between logistics and SCM with those of their peers. The CSFs and inhibitors to success provide a rational basis for realising the strategic potential of logistics and SCM in practice. Originality/value: New insights are generated into practitioner perspectives vis-à -vis logistics vs SCM. A fresh understanding of those factors which drive and hinder the adoption of strategic SCM is also developed and presented
Learning hard quantum distributions with variational autoencoders
Studying general quantum many-body systems is one of the major challenges in
modern physics because it requires an amount of computational resources that
scales exponentially with the size of the system.Simulating the evolution of a
state, or even storing its description, rapidly becomes intractable for exact
classical algorithms. Recently, machine learning techniques, in the form of
restricted Boltzmann machines, have been proposed as a way to efficiently
represent certain quantum states with applications in state tomography and
ground state estimation. Here, we introduce a new representation of states
based on variational autoencoders. Variational autoencoders are a type of
generative model in the form of a neural network. We probe the power of this
representation by encoding probability distributions associated with states
from different classes. Our simulations show that deep networks give a better
representation for states that are hard to sample from, while providing no
benefit for random states. This suggests that the probability distributions
associated to hard quantum states might have a compositional structure that can
be exploited by layered neural networks. Specifically, we consider the
learnability of a class of quantum states introduced by Fefferman and Umans.
Such states are provably hard to sample for classical computers, but not for
quantum ones, under plausible computational complexity assumptions. The good
level of compression achieved for hard states suggests these methods can be
suitable for characterising states of the size expected in first generation
quantum hardware.Comment: v2: 9 pages, 3 figures, journal version with major edits with respect
to v1 (rewriting of section "hard and easy quantum states", extended
discussion on comparison with tensor networks
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