Refining quantum algorithms for each era of quantum computing

Many important physical and mathematical problems do not allow analytical treatment, and are difficult to solve numerically on classical computers. Quantum computing is anticipated to perform vastly better for some of these problems. Examples include simulating other quantum mechanical systems like molecules, some optimisation tasks (e.g. combinatorial optimisation and data fitting), and number-theoretical problems like prime factorisation. Being able to efficiently find answers to these questions would enable the accelerated development in other fields of science, like chemistry and engineering. This work uses analytical and numerical methods to tackle several sub-problems in the field of quantum computing, and provides tools and algorithms that allow more efficient utilisation of the (anticipated) hardware capabilities. The considered problems span a wide range of possible quantum device capacities, from their classical simulation, via near-term intermediate scale (NISQ) and early fault-tolerant hardware, all the way to fully error corrected platforms. Covered topics include an exploration of the problem to automatically generate an efficient implementation of any arbitrary quantum algorithm using the available resources, more accurate techniques for simulating electrons in molecules, a method for extracting information about energy levels in a system from minimal data, and an effort to prepare defined states in a controlled manner. The challenge of modelling perfect or noisy quantum computers themselves using conventional computers is also addressed through the development of an easy-to-use interface to a powerful quantum emulator. Each one of the discussed contributions represents an advance of the theoretical capabilities towards the goal of utilising quantum hardware — which is rapidly being developed alongside theoretical efforts — to its full potential

Cybersecurity and Quantum Computing: friends or foes?

L'abstract è presente nell'allegato / the abstract is in the attachmen

Advanced machine-learning techniques in drug discovery

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery

Quantum Search Algorithms for Constraint Satisfaction and Optimization Problems Using Grover\u27s Search and Quantum Walk Algorithms with Advanced Oracle Design

The field of quantum computing has emerged as a powerful tool for solving and optimizing combinatorial optimization problems. To solve many real-world problems with many variables and possible solutions for constraint satisfaction and optimization problems, the required number of qubits of scalable hardware for quantum computing is the bottleneck in the current generation of quantum computers. In this dissertation, we will demonstrate advanced, scalable building blocks for the quantum search algorithms that have been implemented in Grover\u27s search algorithm and the quantum walk algorithm. The scalable building blocks are used to reduce the required number of qubits in the design. The proposed architecture effectively scales and optimizes the number of qubits needed to solve large problems with a limited number of qubits. Thus, scaling and optimizing the number of qubits that can be accommodated in quantum algorithm design directly reflect on performance. Also, accuracy is a key performance metric related to how accurately one can measure quantum states. The search space of quantum search algorithms is traditionally created by using the Hadamard operator to create superposition. However, creating superpositions for problems that do not need all superposition states decreases the accuracy of the measured states. We present an efficient quantum circuit design that the user has control over to create the subspace superposition states for the search space as needed. Using only the subspace states as superposition states of the search space will increase the rate of correct solutions. In this dissertation, we will present the implementation of practical problems for Grover\u27s search algorithm and quantum walk algorithm in logic design, logic puzzles, and machine learning problems such as SAT, MAX-SAT, XOR-SAT, and like SAT problems in EDA, and mining frequent patterns for association rule mining

Measurements, Models, Systems and Design

531 s.

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field