272 research outputs found
Transfer learning in hybrid classical-quantum neural networks
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-the-art classical network and to embed a select set of highly informative features into a quantum processor. We present several proof-of-concept examples of the convenient application of quantum transfer learning for image recognition and quantum state classification. We use the crossplatform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by IBM and Rigetti
Contextuality and inductive bias in quantum machine learning
Generalisation in machine learning often relies on the ability to encode
structures present in data into an inductive bias of the model class. To
understand the power of quantum machine learning, it is therefore crucial to
identify the types of data structures that lend themselves naturally to quantum
models. In this work we look to quantum contextuality -- a form of
nonclassicality with links to computational advantage -- for answers to this
question. We introduce a framework for studying contextuality in machine
learning, which leads us to a definition of what it means for a learning model
to be contextual. From this, we connect a central concept of contextuality,
called operational equivalence, to the ability of a model to encode a linearly
conserved quantity in its label space. A consequence of this connection is that
contextuality is tied to expressivity: contextual model classes that encode the
inductive bias are generally more expressive than their noncontextual
counterparts. To demonstrate this, we construct an explicit toy learning
problem -- based on learning the payoff behaviour of a zero-sum game -- for
which this is the case. By leveraging tools from geometric quantum machine
learning, we then describe how to construct quantum learning models with the
associated inductive bias, and show through our toy problem that they
outperform their corresponding classical surrogate models. This suggests that
understanding learning problems of this form may lead to useful insights about
the power of quantum machine learning.Comment: comments welcom
The influence of entrepreneurial bricolage and design thinking on opportunity development
DATA AVAILABITY STATEMENT: Data are available upon request from the corresponding author, C.M. Joynt. It is held securely in the repository of the
university and is only accessible by the researchers involved
in the study.BACKGROUND: Entrepreneurial activity in an efficiency-driven economy is fundamental to
economic growth, yet its sustainability and opportunities are concerning. Both entrepreneurial
bricolage and design thinking could enhance opportunity development, but their effectiveness
and incorporation into an integrated approach to opportunity advancement require further
investigation.
AIM: This study explores design thinking and entrepreneurial bricolage as facilitating
constructs for entrepreneurial opportunity development, employing the design-centred
entrepreneurship perspective and the conceptual framework offered by various authors; it
investigates the effectiveness of the theoretical frameworks mentioned; and lastly it explores
the potential of amalgamating these frameworks into a more comprehensive structure for
entrepreneurial opportunity development.
SETTING: The sample consisted of entrepreneurs in South Africa.
METHODS: Fourteen semi-structured interviews with founders of small and medium
entrepreneurial ventures in various South African industry sectors were conducted.
RESULTS: Current frameworks pertaining to bricolage and design thinking proficiencies were
appropriate for opportunity development and could be effectively integrated. However, some
contributory factors should be included, such as organisational culture, business partners and
a non-linear rather than a methodical approach.
CONCLUSION: Entrepreneurial bricolage has a significant influence on developing and establishing
opportunities. The value of design thinking was confirmed with a specific focus on a human-centred approach, creativity and innovation. However, contradictory to design thinking
authors, entrepreneurs described the design thinking process as non-linear and disordered.
CONTRIBUTION: This study provides empirical evidence to enrich the understanding of the
elusive entrepreneurial opportunity development process by integrating the design-centred
entrepreneurship framework with the entrepreneurial bricolage perspective into a single,
more comprehensive framework.https://sajesbm.co.za/index.php/sajesbmAccountingGordon Institute of Business Science (GIBS)SDG-09: Industry, innovation and infrastructur
The influence of entrepreneurial bricolage and design thinking on opportunity development
Background: Entrepreneurial activity in an efficiency-driven economy is fundamental to economic growth, yet its sustainability and opportunities are concerning. Both entrepreneurial bricolage and design thinking could enhance opportunity development, but their effectiveness and incorporation into an integrated approach to opportunity advancement require further investigation.
Aim: This study explores design thinking and entrepreneurial bricolage as facilitating constructs for entrepreneurial opportunity development, employing the design-centred entrepreneurship perspective and the conceptual framework offered by various authors; it investigates the effectiveness of the theoretical frameworks mentioned; and lastly it explores the potential of amalgamating these frameworks into a more comprehensive structure for entrepreneurial opportunity development.
Setting: The sample consisted of entrepreneurs in South Africa.
Methods: Fourteen semi-structured interviews with founders of small and medium entrepreneurial ventures in various South African industry sectors were conducted.
Results: Current frameworks pertaining to bricolage and design thinking proficiencies were appropriate for opportunity development and could be effectively integrated. However, some contributory factors should be included, such as organisational culture, business partners and a non-linear rather than a methodical approach.
Conclusion: Entrepreneurial bricolage has a significant influence on developing and establishing opportunities. The value of design thinking was confirmed with a specific focus on a human-centred approach, creativity and innovation. However, contradictory to design thinking authors, entrepreneurs described the design thinking process as non-linear and disordered.
Contribution: This study provides empirical evidence to enrich the understanding of the elusive entrepreneurial opportunity development process by integrating the design-centred entrepreneurship framework with the entrepreneurial bricolage perspective into a single, more comprehensive framework
Machine learning and the physical sciences
Machine learning encompasses a broad range of algorithms and modeling tools
used for a vast array of data processing tasks, which has entered most
scientific disciplines in recent years. We review in a selective way the recent
research on the interface between machine learning and physical sciences. This
includes conceptual developments in machine learning (ML) motivated by physical
insights, applications of machine learning techniques to several domains in
physics, and cross-fertilization between the two fields. After giving basic
notion of machine learning methods and principles, we describe examples of how
statistical physics is used to understand methods in ML. We then move to
describe applications of ML methods in particle physics and cosmology, quantum
many body physics, quantum computing, and chemical and material physics. We
also highlight research and development into novel computing architectures
aimed at accelerating ML. In each of the sections we describe recent successes
as well as domain-specific methodology and challenges
Convex optimization of programmable quantum computers
A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be non-universal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semi-definite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits
The Born supremacy: quantum advantage and training of an Ising Born machine
The search for an application of near-term quantum devices is widespread.
Quantum Machine Learning is touted as a potential utilisation of such devices,
particularly those which are out of the reach of the simulation capabilities of
classical computers. In this work, we propose a generative Quantum Machine
Learning Model, called the Ising Born Machine (IBM), which we show cannot, in
the worst case, and up to suitable notions of error, be simulated efficiently
by a classical device. We also show this holds for all the circuit families
encountered during training. In particular, we explore quantum circuit learning
using non-universal circuits derived from Ising Model Hamiltonians, which are
implementable on near term quantum devices.
We propose two novel training methods for the IBM by utilising the Stein
Discrepancy and the Sinkhorn Divergence cost functions. We show numerically,
both using a simulator within Rigetti's Forest platform and on the Aspen-1 16Q
chip, that the cost functions we suggest outperform the more commonly used
Maximum Mean Discrepancy (MMD) for differentiable training. We also propose an
improvement to the MMD by proposing a novel utilisation of quantum kernels
which we demonstrate provides improvements over its classical counterpart. We
discuss the potential of these methods to learn `hard' quantum distributions, a
feat which would demonstrate the advantage of quantum over classical computers,
and provide the first formal definitions for what we call `Quantum Learning
Supremacy'. Finally, we propose a novel view on the area of quantum circuit
compilation by using the IBM to `mimic' target quantum circuits using classical
output data only.Comment: v3 : Close to journal published version - significant text structure
change, split into main text & appendices. See v2 for unsplit version; v2 :
Typos corrected, figures altered slightly; v1 : 68 pages, 39 Figures.
Comments welcome. Implementation at
https://github.com/BrianCoyle/IsingBornMachin
Quantum circuits with many photons on a programmable nanophotonic chip
Growing interest in quantum computing for practical applications has led to a
surge in the availability of programmable machines for executing quantum
algorithms. Present day photonic quantum computers have been limited either to
non-deterministic operation, low photon numbers and rates, or fixed random gate
sequences. Here we introduce a full-stack hardware-software system for
executing many-photon quantum circuits using integrated nanophotonics: a
programmable chip, operating at room temperature and interfaced with a fully
automated control system. It enables remote users to execute quantum algorithms
requiring up to eight modes of strongly squeezed vacuum initialized as two-mode
squeezed states in single temporal modes, a fully general and programmable
four-mode interferometer, and genuine photon number-resolving readout on all
outputs. Multi-photon detection events with photon numbers and rates exceeding
any previous quantum optical demonstration on a programmable device are made
possible by strong squeezing and high sampling rates. We verify the
non-classicality of the device output, and use the platform to carry out
proof-of-principle demonstrations of three quantum algorithms: Gaussian boson
sampling, molecular vibronic spectra, and graph similarity
Solving a Higgs optimization problem with quantum annealing for machine learning
The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics
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