Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde

Goal-directed planning and plan recognition for the sustainable control of homes

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-92).The goal of this thesis is to design an autonomous control system for the sustainable control of buildings. The control system focusses on satisfying three goals to encourage and facilitate a more sustainable lifestyle for the future: sustainability, comfort, and convenience. First, the system must be sustainable, meaning it controls the home to minimize the energy required to meet the living requirements of the resident. Second, the home must also place the resident's comfort as first priority, and not sacrifice comfort for energy savings. A central challenge facing the goal of comfort is uncertainty. Uncertain weather conditions can result in violations of the resident's comfort if the control system does not explicitly consider these factors. The home must probabilistically guarantee to meet resident comfort and functional requirements even under uncertain conditions. Finally, the system must be convenient and not place undue burden on the resident. To accomplish these goals, we provide three solutions: (1) goal-directed optimal planning, which supports efficiency, (2) risk-sensitive planning, which addresses comfort, and (2) intent recognition, which supports ease of use. Goal-direction improves efficiency by specifying what energy consuming activities the users need and when, and enables peak demand to be reduced by specifying the flexibility that the user has with respect to when activities can be performed. Risk sensitive planning addresses user comfort by explicitly considering uncertain factors and planning to limit the risk of violating resident requirements. This solution uses a recently developed plan-executive called probabilistic Sulu (p-Sulu) that leverages a recent algorithm called iterative risk allocation (IRA) to robustly find an optimal control sequence for the home. The second challenge, plan recognition, accomplishes our third goal of convenience. To facilitate widespread adoption, the control system should require minimal user interaction. Plan recognition solves this problem by predicting a resident's schedule based on observations of the resident. p-Sulu can then optimally control the home according to this schedule to minimize energy use, while ensuring the house is comfortable while the resident is home, and saving energy while the resident is away. We present the concept design of a novel solution to plan recognition over timed concurrent constraint automata (TCCA) that provides the initial capabilities necessary to achieve this goal.by Wesley Graybill.M.Eng

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.