The complexity and diversity of modern computing challenges have made it difficult for traditional computers to efficiently handle tasks such as computer-aided design, design optimization, and combinatorial problems within a reasonable time frame. In applications such as bitcoin mining and robotic control, power consumption and circuit size are critical factors. To overcome these limitations, this dissertation examines alternative computing technologies such as quantum computers and hybrid memristive circuits. The research demonstrates that these technologies hold the potential to offer more efficient and effective solutions for particular problems, whether in terms of faster computation or reduced power consumption.
The complexity and diversity of modern computing challenges have made it difficult for traditional computers to efficiently handle tasks such as computer-aided design, design optimization, and combinatorial problems within a reasonable time frame. In applications such as bitcoin mining and robotic control, power consumption and circuit size are critical factors. To overcome these limitations, this dissertation examines alternative computing technologies such as quantum computers and hybrid memristive circuits. The research demonstrates that these technologies hold the potential to offer more efficient and effective solutions for particular problems, whether in terms of faster computation or reduced power consumption.
Over the last years there has been significant progress in the development of quantum computers. It has been demonstrated that they can accelerate the solution of various problems exponentially compared to today's classical computers, harnessing the properties of superposition and entanglement, two resources that have no classical analog. Since quantum computer platforms that are currently available comprise only a few tenths of qubits, as well as the access to a fabricated quantum computer is time limited for the majority of researchers, the use of quantum simulators is essential in developing and testing new quantum algorithms. Taking inspiration from previous work on developing a novel quantum simulator based on memristor crossbar circuits, in this work, a framework that automates the circuit design of emulated quantum gates is presented. The proposed design framework deals with the generation and programming of memristor crossbar configuration that incorporates the desirable quantum circuit, leading to a technology agnostic design tool. To such a degree, various quantum gates can be efficiently emulated on memristor crossbar configurations for various types of memristive devices, aiming to assist and accelerate the fabrication process of a memristor based quantum simulator.
Quantum computing, a paradigm shift with the potential to revolutionize fields ranging from cryptography to optimization, is rapidly evolving. This survey delves into the key quantum computing architectures—gate-based and quantum annealing—and their profound impact on circuit design and optimization. Gate-based systems, such as those utilizing superconducting qubits and trapped ions, offer versatile platforms capable of executing a broad array of quantum algorithms. However, they present unique challenges for circuit designers, including noise sensitivity, scalability, and the need for complex error correction circuits. Quantum annealing, exemplified by D-Wave systems, offers a more specialized approach to solving optimization problems, requiring distinct circuit design considerations focused on energy efficiency and coherence preservation.This paper reviews the foundational research on these architectures, compares their respective design challenges, and examines the optimization techniques that have emerged in response to these challenges. Gate count reduction, error correction, and hybrid quantum-classical methods are highlighted as key approaches for improving circuit efficiency and scalability in quantum systems. Furthermore, this survey emphasizes the importance of interdisciplinary collaboration between quantum physicists, electrical engineers, and computer scientists to address the complex challenges associated with quantum circuit design. By combining expertise from these fields, researchers can develop innovative solutions that bridge the gap between theoretical concepts and practical hardware implementations. Ultimately, this survey underscores the need for continued innovation at the intersection of quantum computing and electrical engineering, as researchers strive to overcome current limitations and develop practical, large-scale quantum systems that can harness the full potential of this revolutionary technology.
The growing complexity of Very Large Scale Integration (VLSI) circuit designs has significantly increased the demand for innovative approaches to enhance design efficiency and fabrication accuracy. Traditional computational methods face limitations in terms of scalability and optimization for complex VLSI systems. The advent of quantum computing presents a promising paradigm shift, offering exponential speedup in solving computationally intensive problems such as optimization, simulation, and data analysis in VLSI design and fabrication. This research investigates the potential of quantum computing algorithms to optimize VLSI circuit design and fabrication processes, addressing key challenges such as layout optimization, routing, and process variation. The problem lies in the increasing complexity and size of VLSI circuits, which often lead to inefficient designs, longer manufacturing times, and increased cost. Current methods such as classical algorithms and heuristic techniques struggle to achieve optimal solutions within reasonable time frames. Quantum algorithms, particularly those based on quantum annealing and variational quantum eigensolvers (VQE), have shown promise in solving combinatorial optimization problems with much higher efficiency. By leveraging quantum computing, the goal is to improve the optimization of circuit layouts, minimize power consumption, and reduce production costs in VLSI systems. The proposed method utilizes quantum computing techniques to model and solve optimization problems related to VLSI circuit design and fabrication. Simulations and comparisons with classical methods show significant improvements in design efficiency, reduced error rates, and faster optimization cycles. The outcomes of this study could transform the future of VLSI design and fabrication, leading to more efficient, cost-effective, and scalable solutions for semiconductor manufacturing.
Cryptocurrency mining processes always lead to a high energy consumption at considerably high production cost, which is nearly one-third of cryptocurrency (e.g. Bitcoin) price itself. As the core of mining process is based on SHA-256 cryptographic hashing function, by using the alternative quantum computers, hybrid quantum computers or more larger quantum computing devices like quantum annealers, it would be possible to reduce the mining energy consumption with a quantum hardware's low-energy-operation characteristics. Within this work we demonstrated the use of optimized quantum mining facilities which would replace the classical SHA-256 and high energy consuming classical hardware in near future.
This paper presents a collection of student-led review studies covering selected topics in modern quantum information technologies. The contributions explore a broad range of theoretical concepts, hardware architectures, and application domains, including quantum computing models, quantum sensing, photonic systems, and optimization techniques. The reviewed works examine both gate-based and alternative quantum computing paradigms, as well as hybrid quantum–classical approaches, highlighting their potential advantages and current technological limitations. Particular attention is given to the role of quantum algorithms and physical implementations in addressing computationally challenging problems, such as combinatorial optimization and simulation tasks. Overall, the paper provides a snapshot of the current landscape of quantum technologies from an interdisciplinary perspective, emphasizing ongoing challenges related to scalability, noise, and practical applicability, while outlining promising directions for future research and development.
This bachelor’s thesis examines the applicability of quantum computing to the complex optimization problems inherent in energy systems. The transition toward decentralized production and renewable energy sources has increased computational complexity to a degree that challenges the performance of traditional classical optimization methods. In particular, combinatorial challenges—such as unit commitment and power grid management—have been identified as areas where traditional algorithms reach their limits as the number of variables increases exponentially. The thesis introduces the physical and computational foundations of quantum computing, including superposition, entanglement, and quantum parallelism, and analyzes the theoretical advantage they offer relative to classical bit-based computing. The analytical focus is on quantum optimization methods, such as Quadratic Unconstrained Binary Optimization (QUBO), quantum annealing, and variational hybrid algorithms (QAOA). Additionally, the role of quantum simulations in material physics, such as the development of battery materials and catalysts, is examined. Based on the literature review, quantum computing offers promising opportunities to support energy efficiency and the green transition. However, the technological limitations of current hardware (the NISQ era), such as the low number of qubits and sensitivity to noise, still necessitate the extensive use of quantum simulators and classical-quantum hybrid models. This work synthesizes current research in the field and identifies critical research gaps that must be addressed before the full extent of quantum advantage can be realized in practical energy engineering solutions.