Quantum Genetic Algorithms for Computer Scientists

Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena

Quantum and Classical Multilevel Algorithms for (Hyper)Graphs

Combinatorial optimization problems on (hyper)graphs are ubiquitous in science and industry. Because many of these problems are NP-hard, development of sophisticated heuristics is of utmost importance for practical problems. In recent years, the emergence of Noisy Intermediate-Scale Quantum (NISQ) computers has opened up the opportunity to dramaticaly speedup combinatorial optimization. However, the adoption of NISQ devices is impeded by their severe limitations, both in terms of the number of qubits, as well as in their quality. NISQ devices are widely expected to have no more than hundreds to thousands of qubits with very limited error-correction, imposing a strict limit on the size and the structure of the problems that can be tackled directly. A natural solution to this issue is hybrid quantum-classical algorithms that combine a NISQ device with a classical machine with the goal of capturing “the best of both worlds”. Being motivated by lack of high quality optimization solvers for hypergraph partitioning, in this thesis, we begin by discussing classical multilevel approaches for this problem. We present a novel relaxation-based vertex similarity measure termed algebraic distance for hypergraphs and the coarsening schemes based on it. Extending the multilevel method to include quantum optimization routines, we present Quantum Local Search (QLS) – a hybrid iterative improvement approach that is inspired by the classical local search approaches. Next, we introduce the Multilevel Quantum Local Search (ML-QLS) that incorporates the quantum-enhanced iterative improvement scheme introduced in QLS within the multilevel framework, as well as several techniques to further understand and improve the effectiveness of Quantum Approximate Optimization Algorithm used throughout our work

An integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits

Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, a highly detailed system characterization required to understand the underlying errors is an arduous process and impractical with increasing chip size. Open-loop optimal control techniques allow for the improvement of gates but are limited by the models they are based on. To rectify the situation, we provide a new integrated open-source tool-set for Control, Calibration and Characterization ($C^3$), capable of open-loop pulse optimization, model-free calibration, model fitting and refinement. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget, all based on a high-performance, feature-rich simulator. We illustrate our methods using fixed-frequency superconducting qubits for which we learn model parameters to an accuracy of $<1\%$ and derive a coherence limited cross-resonance (CR) gate that achieves $99.6\%$ fidelity without need for calibration.Comment: Source code available at http://q-optimize.org; added reference

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Quantum-Inspired Machine Learning: a Survey

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.Comment: 56 pages, 13 figures, 8 table

Multilevel Combinatorial Optimization Across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore's law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave's quantum annealer and IBM's gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution

Multilevel Combinatorial Optimization Across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the Post Moore\u27s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave\u27s quantum annealer and IBM\u27s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size and observe results comparable to state-of-the-art solvers

Self-adaptive fitness in evolutionary processes

Most optimization algorithms or methods in artificial intelligence can be regarded as evolutionary processes. They start from (basically) random guesses and produce increasingly better results with respect to a given target function, which is defined by the process's designer. The value of the achieved results is communicated to the evolutionary process via a fitness function that is usually somewhat correlated with the target function but does not need to be exactly the same. When the values of the fitness function change purely for reasons intrinsic to the evolutionary process, i.e., even though the externally motivated goals (as represented by the target function) remain constant, we call that phenomenon self-adaptive fitness. We trace the phenomenon of self-adaptive fitness back to emergent goals in artificial chemistry systems, for which we develop a new variant based on neural networks. We perform an in-depth analysis of diversity-aware evolutionary algorithms as a prime example of how to effectively integrate self-adaptive fitness into evolutionary processes. We sketch the concept of productive fitness as a new tool to reason about the intrinsic goals of evolution. We introduce the pattern of scenario co-evolution, which we apply to a reinforcement learning agent competing against an evolutionary algorithm to improve performance and generate hard test cases and which we also consider as a more general pattern for software engineering based on a solid formal framework. Multiple connections to related topics in natural computing, quantum computing and artificial intelligence are discovered and may shape future research in the combined fields.Die meisten Optimierungsalgorithmen und die meisten Verfahren in Bereich künstlicher Intelligenz können als evolutionäre Prozesse aufgefasst werden. Diese beginnen mit (prinzipiell) zufällig geratenen Lösungskandidaten und erzeugen dann immer weiter verbesserte Ergebnisse für gegebene Zielfunktion, die der Designer des gesamten Prozesses definiert hat. Der Wert der erreichten Ergebnisse wird dem evolutionären Prozess durch eine Fitnessfunktion mitgeteilt, die normalerweise in gewissem Rahmen mit der Zielfunktion korreliert ist, aber auch nicht notwendigerweise mit dieser identisch sein muss. Wenn die Werte der Fitnessfunktion sich allein aus für den evolutionären Prozess intrinsischen Gründen ändern, d.h. auch dann, wenn die extern motivierten Ziele (repräsentiert durch die Zielfunktion) konstant bleiben, nennen wir dieses Phänomen selbst-adaptive Fitness. Wir verfolgen das Phänomen der selbst-adaptiven Fitness zurück bis zu künstlichen Chemiesystemen (artificial chemistry systems), für die wir eine neue Variante auf Basis neuronaler Netze entwickeln. Wir führen eine tiefgreifende Analyse diversitätsbewusster evolutionärer Algorithmen durch, welche wir als Paradebeispiel für die effektive Integration von selbst-adaptiver Fitness in evolutionäre Prozesse betrachten. Wir skizzieren das Konzept der produktiven Fitness als ein neues Werkzeug zur Untersuchung von intrinsischen Zielen der Evolution. Wir führen das Muster der Szenarien-Ko-Evolution (scenario co-evolution) ein und wenden es auf einen Agenten an, der mittels verstärkendem Lernen (reinforcement learning) mit einem evolutionären Algorithmus darum wetteifert, seine Leistung zu erhöhen bzw. härtere Testszenarien zu finden. Wir erkennen dieses Muster auch in einem generelleren Kontext als formale Methode in der Softwareentwicklung. Wir entdecken mehrere Verbindungen der besprochenen Phänomene zu Forschungsgebieten wie natural computing, quantum computing oder künstlicher Intelligenz, welche die zukünftige Forschung in den kombinierten Forschungsgebieten prägen könnten