Teaching the Hardware Implementation of Cybesecurity Encryption Algorithms on FPGA Using Hands-On Projects

Cybersecurity is an important concept in today’s age of information and is of major interest to keep information secure, helping to protect sensitive information in the presence of untrusted third-parties. This has presented the need for an implemented hardware variant of secure algorithms with small footprint to help add protection while reducing processing time/overhead on a standard processor. In this work we present two hands-on projects that are designed specifically to teach these two concepts using project-based learning techniques in an innovative cooperative learning environment. The learning environment served to combine both student-peer learning and jigsaw strategies. The technical contents of the first project teach students the process and methodologies of designing and testing the hardware implementation of a block cipher encryption, the Advanced Encryption Standard, on a field-programmable gate array. The second project builds on the first by introducing the hardware implementation of hash message authentication codes through the Whirlpool hash function in three different operating modes. The objective of this work is to present an innovative teaching environment for these hands-on encryption algorithm-based projects using cooperative learning rather than a traditional mode of lecturing with given homework assignments. This environment encouraged students to think thoroughly, out-of-the-box, gain problem-solving skills, and improve their communication of technical concepts to peers through the delivery of student-led lectures. The assessment of student learning is accomplished by a mixture of presentations with peer evaluations, instructor evaluations, and thorough grading of project reports. End-of-course evaluations were positive regarding the learning environment and technical skills gained by students. For this work one assigned hands-on project for students working in groups resulted in unique per-group implementations, where in the second project, this led to different project perspectives and additions beyond a standard assigned project, enhanced by student-peer teaching. Students effectively learned and comprehended many different implementations of a widely used encryption and authentication algorithm via our modified teaching techniques

Artificial Intelligence and Machine Learning for Quantum Technologies

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade

Generative Adversarial Game with Tailored Quantum Feature Maps for Enhanced Classification

In the burgeoning field of quantum machine learning, the fusion of quantum computing and machine learning methodologies has sparked immense interest, particularly with the emergence of noisy intermediate-scale quantum (NISQ) devices. These devices hold the promise of achieving quantum advantage, but they grapple with limitations like constrained qubit counts, limited connectivity, operational noise, and a restricted set of operations. These challenges necessitate a strategic and deliberate approach to crafting effective quantum machine learning algorithms. This dissertation revolves around an exploration of these challenges, presenting innovative strategies that tailor quantum algorithms and processes to seamlessly integrate with commercial quantum platforms. A pivotal proposition within this framework is the creation of a resource-efficient quantum generative adversarial network (QGAN). This adaptation of QGANs, which are typically used to synthesize data according to specific probability distributions, ensures optimal performance even within environments with limited resources. Furthermore, our research delves deeply into the development of a data encoding procedure specifically designed for NISQ devices. This encoding process, responsible for translating classical data into quantum states to enable quantum algorithm processing, plays a critical role in quantum machine learning. Our goal is to establish an encoding approach that optimally utilizes quantum resources while mitigating the impact of noise and inherent limitations in NISQ devices. Another key aspect of our study is the seamless alignment of the devised algorithms with the existing architectures of NISQ devices. Given the pivotal role of these devices in contemporary quantum technology, ensuring compatibility is of utmost importance. This not only facilitates immediate applications but also establishes a robust framework for accommodating future technological advancements. Through an extensive analysis of these critical dimensions, our objective is to make a substantial contribution to the practical implementation of quantum machine learning algorithms on commercial quantum platforms. We aim to navigate the intricate landscape of NISQ technologies adeptly, thereby facilitating the seamless integration of quantum machine learning into real-world applications. Ultimately, this research endeavor aspires to drive advancements at the nexus of quantum computing and machine learning

When could NISQ algorithms start to create value in discrete manufacturing ?

Are quantum advantages in discrete manufacturing achievable in the near term? As manufacturing-relevant NISQ algorithms, we identified Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization as well as Derivative Quantum Circuits (DQC) for solving non-linear PDEs. While there is evidence for QAOA's outperformance, this requires post-NISQ circuit depths. In the case of QA, there is up to now no unquestionable evidence for advantage compared to classical computation. Yet different protocols could lead to finding such instances. Together with a well-chosen quantum feature map, DQC are a promising concept. Further investigations for higher dimensional problems and improvements in training could follow.Comment: 39 pages (thesis

Exponential data encoding for quantum supervised learning

Reliable quantum supervised learning of a multivariate function mapping depends on the expressivity of the corresponding quantum circuit and measurement resources. We introduce exponential-data-encoding strategies that are hardware-efficient and optimal amongst all non-entangling Pauli-encoded schemes, which is sufficient for a quantum circuit to express general functions having very broad Fourier frequency spectra using only exponentially few encoding gates. We show that such an encoding strategy not only reduces the quantum resources, but also exhibits practical resource advantage during training in contrast with known efficient classical strategies when polynomial-depth training circuits are also employed. When computation resources are constrained, we numerically demonstrate that even exponential-data-encoding circuits with single-layer training modules can generally express functions that lie outside the classically-expressible region, thereby supporting the practical benefits of such a resource advantage. Finally, we illustrate the performance of exponential encoding in learning the potential-energy surface of the ethanol molecule and California's housing pricesComment: 21 pages, 13 figure