13,359 research outputs found
Many Physical Design Problems are Sparse QCQPs
Physical design refers to mathematical optimization of a desired objective
(e.g. strong light--matter interactions, or complete quantum state transfer)
subject to the governing dynamical equations, such as Maxwell's or
Schrodinger's differential equations. Computing an optimal design is
challenging: generically, these problems are highly nonconvex and finding
global optima is NP hard. Here we show that for linear-differential-equation
dynamics (as in linear electromagnetism, elasticity, quantum mechanics, etc.),
the physical-design optimization problem can be transformed to a sparse-matrix,
quadratically constrained quadratic program (QCQP). Sparse QCQPs can be tackled
with convex optimization techniques (such as semidefinite programming) that
have thrived for identifying global bounds and high-performance designs in
other areas of science and engineering, but seemed inapplicable to the design
problems of wave physics. We apply our formulation to prototypical photonic
design problems, showing the possibility to compute fundamental limits for
large-area metasurfaces, as well as the identification of designs approaching
global optimality. Looking forward, our approach highlights the promise of
developing bespoke algorithms tailored to specific physical design problems.Comment: 9 pages, 4 figures, plus references and Supplementary Material
Trainable Variational Quantum-Multiblock ADMM Algorithm for Generation Scheduling
The advent of quantum computing can potentially revolutionize how complex
problems are solved. This paper proposes a two-loop quantum-classical solution
algorithm for generation scheduling by infusing quantum computing, machine
learning, and distributed optimization. The aim is to facilitate employing
noisy near-term quantum machines with a limited number of qubits to solve
practical power system optimization problems such as generation scheduling. The
outer loop is a 3-block quantum alternative direction method of multipliers
(QADMM) algorithm that decomposes the generation scheduling problem into three
subproblems, including one quadratically unconstrained binary optimization
(QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate
optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The
proposed T-QAOA translates interactions of quantum-classical machines as
sequential information and uses a recurrent neural network to estimate
variational parameters of the quantum circuit with a proper sampling technique.
T-QAOA determines the QUBO solution in a few quantum-learner iterations instead
of hundreds of iterations needed for a quantum-classical solver. The outer
3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to
the original problem. The conditions under which the proposed QADMM is
guaranteed to converge are discussed. Two mathematical and three generation
scheduling cases are studied. Analyses performed on quantum simulators and
classical computers show the effectiveness of the proposed algorithm. The
advantages of T-QAOA are discussed and numerically compared with QAOA which
uses a stochastic gradient descent-based optimizer.Comment: 11 page
On the Mechanism of Building Core Competencies: a Study of Chinese Multinational Port Enterprises
This study aims to explore how Chinese multinational port enterprises (MNPEs) build
their core competencies. Core competencies are firms’special capabilities and sources
to gain sustainable competitive advantage (SCA) in marketplace, and the concept led
to extensive research and debates. However, few studies include inquiries about the
mechanisms of building core competencies in the context of Chinese MNPEs.
Accordingly, answers were sought to three research questions:
1. What are the core competencies of the Chinese MNPEs?
2. What are the mechanisms that the Chinese MNPEs use to build their core
competencies?
3. What are the paths that the Chinese MNPEs pursue to build their resources bases?
The study adopted a multiple-case study design, focusing on building mechanism of
core competencies with RBV. It selected purposively five Chinese leading MNPEs
and three industry associations as Case Companies.
The study revealed three main findings. First, it identified three generic core
competencies possessed by Case Companies, i.e., innovation in business models and
operations, utilisation of technologies, and acquisition of strategic resources. Second,
it developed the conceptual framework of the Mechanism of Building Core
Competencies (MBCC), which is a process of change of collective learning in
effective and efficient utilization of resources of a firm in response to critical events.
Third, it proposed three paths to build core competencies, i.e., enhancing collective
learning, selecting sustainable processes, and building resource base.
The study contributes to the knowledge of core competencies and RBV in three ways:
(1) presenting three generic core competencies of the Chinese MNPEs, (2) proposing
a new conceptual framework to explain how Chinese MNPEs build their core
competencies, (3) suggesting a solid anchor point (MBCC) to explain the links among
resources, core competencies, and SCA. The findings set benchmarks for Chinese
logistics industry and provide guidelines to build core competencies
Discovering the hidden structure of financial markets through bayesian modelling
Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole.
We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors.
Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces
A simple procedure for fault detectors design in SISO systems
In this work, we present a novel approach for fault detectors design and implementation in the case of actuator faults. Both the design and the implementation are focused on simplicity. The fault detector is based in an output observer that estimates the fault signal followed by a decision mechanism that detects the presence of a fault from the estimation. The observer consists of two transfer functions fed by the process manipulated variable and the sensor measurement. For the synthesis of the fault detector, we just need an input–output model of the process and two tuning parameters; one used in the observer, and the other in the decision mechanism. We present simple rules for the design considering the trade-off between the detection time, the minimum detectable fault and the false alarm rate. Our implementation method uses standard tools available in industrial control systems and we have applied it to a real two-tank system setup. The main contribution of this work is the simplicity of the design and implementation of the fault detector, making it suitable for process industry and for being managed by not experts in control systems. Another contribution is the a priori design based in intuitive engineering performance indices
Towards a non-equilibrium thermodynamic theory of ecosystem assembly and development
Non-equilibrium thermodynamics has had a significant historic influence on the development
of theoretical ecology, even informing the very concept of an ecosystem. Much of this influence
has manifested as proposed extremal principles. These principles hold that systems will tend
to maximise certain thermodynamic quantities, subject to the other constraints they operate
under. A particularly notable extremal principle is the maximum entropy production principle
(MaxEPP); that systems maximise their rate of entropy production. However, these principles
are not robustly based in physical theory, and suffer from treating complex ecosystems in
an extremely coarse manner. To address this gap, this thesis derives a limited but physically
justified extremal principle, as well as carrying out a detailed investigation of the impact of
non-equilibrium thermodynamic constraints on the assembly of microbial communities. The extremal
principle we obtain pertains to the switching between states in simple bistable systems,
with switching paths that generate more entropy being favoured. Our detailed investigation
into microbial communities involved developing a novel thermodynamic microbial community
model, using which we found the rate of ecosystem development to be set by the availability
of free-energy. Further investigation was carried out using this model, demonstrating the way
that trade-offs emerging from fundamental thermodynamic constraints impact the dynamics of
assembling microbial communities. Taken together our results demonstrate that theory can be
developed from non-equilibrium thermodynamics, that is both ecologically relevant and physically
well grounded. We find that broad extremal principles are unlikely to be obtained, absent
significant advances in the field of stochastic thermodynamics, limiting their applicability to
ecology. However, we find that detailed consideration of the non-equilibrium thermodynamic
mechanisms that impact microbial communities can broaden our understanding of their assembly
and functioning.Open Acces
A suite of quantum algorithms for the shortestvector problem
Crytography has come to be an essential part of the cybersecurity infrastructure that provides a safe environment for communications in an increasingly connected world. The advent of quantum computing poses a threat to the foundations of the current widely-used cryptographic model, due to the breaking of most of the cryptographic algorithms used to provide confidentiality, authenticity, and more. Consequently a new set of cryptographic protocols have been designed to be secure against quantum computers, and are collectively known as post-quantum cryptography (PQC). A forerunner among PQC is lattice-based cryptography, whose security relies upon the hardness of a number of closely related mathematical problems, one of which is known as the shortest vector problem (SVP).
In this thesis I describe a suite of quantum algorithms that utilize the energy minimization principle to attack the shortest vector problem. The algorithms outlined span the gate-model and continuous time quantum computing, and explore methods of parameter optimization via variational methods, which are thought to be effective on near-term quantum computers. The performance of the algorithms are analyzed numerically, analytically, and on quantum hardware where possible. I explain how the results obtained in the pursuit of solving SVP apply more broadly to quantum algorithms seeking to solve general real-world problems; minimize the effect of noise on imperfect hardware; and improve efficiency of parameter optimization.Open Acces
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