Quantum Artificial Intelligence Research Articles

Automatic evolutionary design of quantum rule-based systems and applications to quantum reinforcement learning

Explainable artificial intelligence is a research topic whose relevance has increased in recent years, especially with the advent of large machine learning models. However, very few attempts have been proposed to improve interpretability in the case of quantum artificial intelligence, and many existing quantum machine learning models in the literature can be considered almost as black boxes. In this article, we argue that an appropriate semantic interpretation of a given quantum circuit that solves a problem can be of interest to the user not only to certify the correct behavior of the learned model, but also to obtain a deeper insight into the problem at hand and its solution. We focus on decision-making problems that can be formulated as classification tasks and propose a method for learning quantum rule-based systems to solve them using evolutionary optimization algorithms. The approach is tested to learn rules that solve control and decision-making tasks in reinforcement learning environments, to provide interpretable agent policies that help to understand the internal dynamics of an unknown environment. Our results conclude that the learned policies are not only highly explainable, but can also help detect non-relevant features of problems and produce a minimal set of rules.

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

BackgroundBiomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data.MethodWe propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware.ResultsWe demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation.ConclusionThe model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks.

Quantum-Inspired Neural Network Model of Optical Illusions

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.

Frontiers of Artificial Intelligence Versus Generative, Explainable and Quantum Technologies: An Overview

Generative AI can produce content that is similar to human ingenuity, revolutionizing a number of industries with lifelike outputs like music and images. Misinformation and intellectual property rights give rise to ethical concerns. Notwithstanding these difficulties, generative AI has the potential to spur additional innovation in a variety of sectors, subject to moral issues. The goal of explainable AI (XAI) is to improve AI systems’ accountability and transparency, which is essential for their inclusion into industries like banking and healthcare. However, in order to effectively explain complicated AI systems, strong XAI techniques are required. Quantum artificial intelligence (QAI) promises improvements in cryptography and optimization by using quantum physics to speed up AI systems. However, there are still difficulties in creating practical quantum computers and improving quantum AI algorithms. The present overview provides new insights into developing a platform to explore AI in various arenas of life sciences and technologies and social up-gradation.

Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting

Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

Revolutionizing Sustainable Energy Production with Quantum Artificial Intelligence: Applications in Autonomous Robotics and Data Management

The present commentary delineates the scope of quantum-based artificial intelligence (AI) in confronting the escalating need for eco-friendly sources of energy. Contemporary concerns regarding the detrimental impacts of conventional energy sources on the environment have led to the exploration of alternative and sustainable options. The amalgamation of quantum computing and AI is a potentially effective remedy for the existing energy crisis. The present analysis explores the theoretical underpinnings of quantum artificial intelligence and its palpable ramifications in the energy realm. It highlights the capacity of quantum AI to enhance energy efficiency, reduce waste, and minimize the detrimental impact of energy production on the environment. Experimental techniques, such as solar irradiation, wind velocity, and pumped-water energy storage, were utilized to demonstrate the effectiveness of quantum AI in predicting renewable energy generation. Several information models and machine learning techniques have been examined to identify models that yield accurate predictions of renewable energy generation. The results demonstrate that quantum AI can easily perform complex quantum sub-atomic simulations, making it a valuable tool in the fields of computational science and molecular physics. A quantum AI-powered automated robotic system is proposed to optimize energy management. The implementation of a monitoring system that monitors energy consumption levels, mechanizes the production and distribution processes of energy, and anticipates future energy demands in real time, renders it an indispensable device for both energy conglomerates and policymakers. This study ascertains that the amalgamation of quantum computing and artificial intelligence harbors substantial potential for sustainable energy generation, marked by streamlined energy production, minimal waste generation, and ecoconservation. The present investigation furnishes insightful observations pertaining to the potential benefits of quantum artificial intelligence for the purpose of ensuring sustainable energy production. Additionally, it presents a roadmap for future research in this area.

Visualizing Quantum Circuit Probability: Estimating Quantum State Complexity for Quantum Program Synthesis.

This work applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. The relations among the statistical, algorithmic, computational, and circuit complexities of states are reviewed. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality, and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis, and quantum artificial general intelligence can benefit by studying circuit probabilities.

Quantum field lens coding and classification algorithm to predict measurement outcomes

This study develops a method to implement a quantum field lens coding and classification algorithm for two quantum double-field (QDF) system models: 1- a QDF model, and 2- a QDF lens coding model by a DF computation (DFC). This method determines entanglement entropy (EE) by implementing QDF operators in a quantum circuit. The physical link between the two system models is a quantum field lens coding algorithm (QF-LCA), which is a QF lens distance-based, implemented on real N-qubit machines. This is with the possibility to train the algorithm for making strong predictions on phase transitions as the shared objective of both models. In both system models, QDF transformations are simulated by a DFC algorithm where QDF data are collected and analyzed to represent energy states and transitions, and determine entanglement based on EE. The method gives a list of steps to simulate and optimize any thermodynamic system on macro and micro-scale observations, as presented in this article:•The implementation of QF-LCA on quantum computers with EE measurement under a QDF transformation.•Validation of QF-LCA as implemented compared to quantum Fourier transform (QFT) and its inverse, QFT−1.•Quantum artificial intelligence (QAI) features by classifying QDF with strong measurement outcome predictions.

On fuzzy inference based supervisory control decision model with quantum artificial intelligence electromagnetic prediction models

On fuzzy inference based supervisory control decision model with quantum artificial intelligence electromagnetic prediction models

Quantum technology to expand soft computing

Soft computing was founded to unify and advance computing methods beyond the limits of binary variables, Boolean logic and Turing machines. David Deutsch invented a way to use massive quantum parallelism to massively improve the power of Turing machines in some applications. Here we give a roadmap to use quantum parallelism to massively improve the power of soft computing, focusing on tasks in optimization ranging from function optimization, finding a needle in a haystack, optimal control, energy technology and reinforcement learning (Quantum artificial general intelligence, AGI) in a path leading up to optimization of physical plants themselves put into quantum superpositions. The path begins with design and test of prototypes built on platforms such as Nuclear Magnetic Resonance, sparse quantum optics, superconductors (sQuID), quantum dots and new high speed electronics.

Emerging Technologies, STI Diaspora and Science Diplomacy in India: Towards a New Approach.

Utilizing the expertise and knowledge resources of the diaspora, particularly the scientific diaspora, has been part of the strategies of many countries. In the recent years, realizing the importance of the potential of the diaspora to contribute to national development and Science, Technology, and Innovation ecosystem, countries have used Science Diplomacy also to engage with the scientific diaspora. Science Diplomacy is hailed as an enabler and facilitator and is often seen in the context of international S&T collaboration or big science projects. But the use of Science Diplomacy for diaspora engagement calls for specific strategies and meaningful initiatives. India is one of the major developing countries that has given a major thrust to engaging with the scientific diaspora. India is also a leading player in the global Science Diplomacy arena. This article critically examines India's initiatives and strategies for engagement with the scientific diaspora. It points out that the Science Diplomacy dimension is missing in this. Using examples from other countries, recent thinking, and developments in Science Diplomacy, this study outlines an approach with some examples of strategies and initiatives for harnessing Science Diplomacy to enhance engagement with the scientific diaspora and create a win-win milieu for India and the diaspora. The approach takes into account the proposed and ongoing initiatives in emerging technologies in India, including quantum technologies and Artificial Intelligence. Such a framework will create a synergy among various programs and initiatives by using Science Diplomacy as a facilitator and catalyst. Under this framework, Diaspora is involved not only as experts and contributors to scientific advancements but also as stakeholders. This dual role of the STI Diaspora can bring a paradigm shift in traditional understanding and use of science diplomacy, particularly to engage and harness the potential of the STI Diaspora for Sustainable Development.

Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality

Transitioning from fossil fuels to renewable sources and developing sustainable energy materials for energy production and storage are critical factors in achieving climate neutrality. These can be realized through innovative strategies to provide viable, economically competitive, and scalable technologies ranging across various sectors. Quantum computing (QC) has the potential to revolutionize various domains of science and engineering, including macro-energy systems and sustainable energy materials design. Conventional approaches for renewable and sustainable energy systems solely rely on classical computing techniques that may not scale well with the increasing size and complexity of applications. Owing to the advancements in quantum hardware and algorithms, QC and quantum artificial intelligence make promising tools to handle renewable and sustainable energy systems even at larger scales. In this review, we discuss the prospects of QC for various areas of applications in energy sustainability to help address climate change. In addition to providing a brief background on the operations of quantum computers, the constituent segments of widely adopted QC-based techniques that improve the computational efficiency of quantum chemistry calculations for sustainable energy materials along with quantum artificial intelligence methods that can address complex optimization and machine learning problems arising in renewable energy systems are also introduced in this paper. We screen the presented quantum algorithms based on their performance on current quantum devices despite their promising potential. Furthermore, sustainable energy applications that may draw advantages from QC-based strategies are identified in this work while simultaneously setting realistic expectations over the potential improvements offered over classical techniques.

Quantum Propensity in Economics.

This paper describes an approach to economics that is inspired by quantum computing, and is motivated by the need to develop a consistent quantum mathematical framework for economics. The traditional neoclassical approach assumes that rational utility-optimisers drive market prices to a stable equilibrium, subject to external perturbations or market failures. While this approach has been highly influential, it has come under increasing criticism following the financial crisis of 2007/8. The quantum approach, in contrast, is inherently probabilistic and dynamic. Decision-makers are described, not by a utility function, but by a propensity function which specifies the probability of transacting. We show how a number of cognitive phenomena such as preference reversal and the disjunction effect can be modelled by using a simple quantum circuit to generate an appropriate propensity function. Conversely, a general propensity function can be quantized, via an entropic force, to incorporate effects such as interference and entanglement that characterise human decision-making. Applications to some common problems and topics in economics and finance, including the use of quantum artificial intelligence, are discussed.

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

On fuzzy inference based supervisory control decision model with quantum artificial intelligence electromagnetic prediction models

On fuzzy inference based supervisory control decision model with quantum artificial intelligence electromagnetic prediction models

Penyuluhan Machine Learning dan Quantum Artificial Intelligence di Era Industri 4.0

The Digital Era 4.0 has started since 2016 and two Southeast Asia countries such as Malaysia and Singapore have already adapted to the era; unfortunately, Indonesia has been struggling to adapt the era and, therefore, needs to catch up the digital competitiveness of its neighbouring countries. According to IMD World Digital Competitiveness 2020, Indonesia placed 56th of 63 countries in the digital competitiveness measurement. Despite its poor performance, Indonesia can catch up with other countries by starting from universities’ environment where Indonesia’s next generations study. Universities are prominent education institutions which prepare next generations for world digital competitiveness. According to BPS Indonesia, the unemployment of bachelor, master, and doctoral graduates reach a total number of 737.000, or 5,67% of 13 millions work force. One of the causes is the lack of technological knowledge, specifically, Artificial Intelligence (AI), from the graduates. Particularly, when they become business leaders, they are not fully prepared to create new job openings because mostly their mindsets are to find suitable jobs after study. The two webinars are results of collaboration between several universities which form NUNI (Jejaring Universitas Nusantara) whose purpose is to equip students with the knowledge of AI. Our method of counselling whose format is two webinars with both titles are Interpretable Machine Learning and Quantum Artificial Intelligence has gained appreciation in the form of average participation score which approaches excellent score (4,60 of 5,00). Additionally, these two webinars are publicly available in web blogs and Youtube videos.  

Engineering Philosophy of Quantum Artificial Intelligence

Engineering Philosophy of Quantum Artificial Intelligence

Use of chemical informatics, quantum chemistry modelling and artificial intelligence algorithms to predict molecular initiating events

Use of chemical informatics, quantum chemistry modelling and artificial intelligence algorithms to predict molecular initiating events

Experimental semi-autonomous eigensolver using reinforcement learning

The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.