9,346 research outputs found
A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering
Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD
Tiny Classifier Circuits: Evolving Accelerators for Tabular Data
A typical machine learning (ML) development cycle for edge computing is to
maximise the performance during model training and then minimise the
memory/area footprint of the trained model for deployment on edge devices
targeting CPUs, GPUs, microcontrollers, or custom hardware accelerators. This
paper proposes a methodology for automatically generating predictor circuits
for classification of tabular data with comparable prediction performance to
conventional ML techniques while using substantially fewer hardware resources
and power. The proposed methodology uses an evolutionary algorithm to search
over the space of logic gates and automatically generates a classifier circuit
with maximised training prediction accuracy. Classifier circuits are so tiny
(i.e., consisting of no more than 300 logic gates) that they are called "Tiny
Classifier" circuits, and can efficiently be implemented in ASIC or on an FPGA.
We empirically evaluate the automatic Tiny Classifier circuit generation
methodology or "Auto Tiny Classifiers" on a wide range of tabular datasets, and
compare it against conventional ML techniques such as Amazon's AutoGluon,
Google's TabNet and a neural search over Multi-Layer Perceptrons. Despite Tiny
Classifiers being constrained to a few hundred logic gates, we observe no
statistically significant difference in prediction performance in comparison to
the best-performing ML baseline. When synthesised as a Silicon chip, Tiny
Classifiers use 8-18x less area and 4-8x less power. When implemented as an
ultra-low cost chip on a flexible substrate (i.e., FlexIC), they occupy 10-75x
less area and consume 13-75x less power compared to the most hardware-efficient
ML baseline. On an FPGA, Tiny Classifiers consume 3-11x fewer resources.Comment: 14 pages, 16 figure
Differentiable Quantum Architecture Search
Quantum architecture search (QAS) is the process of automating architecture
engineering of quantum circuits. It has been desired to construct a powerful
and general QAS platform which can significantly accelerate current efforts to
identify quantum advantages of error-prone and depth-limited quantum circuits
in the NISQ era. Hereby, we propose a general framework of differentiable
quantum architecture search (DQAS), which enables automated designs of quantum
circuits in an end-to-end differentiable fashion. We present several examples
of circuit design problems to demonstrate the power of DQAS. For instance,
unitary operations are decomposed into quantum gates, noisy circuits are
re-designed to improve accuracy, and circuit layouts for quantum approximation
optimization algorithm are automatically discovered and upgraded for
combinatorial optimization problems. These results not only manifest the vast
potential of DQAS being an essential tool for the NISQ application
developments, but also present an interesting research topic from the
theoretical perspective as it draws inspirations from the newly emerging
interdisciplinary paradigms of differentiable programming, probabilistic
programming, and quantum programming.Comment: 9.1 pages + Appendix, 5 figure
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