Quantum Computer Science Research Articles

Recent Advances for Quantum Neural Networks in Generative Learning.

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts. As such, QGLMs are receiving growing attention from the quantum physics and computer science communities, where various QGLMs that can be efficiently implemented on near-term quantum machines with potential computational advantages are proposed. In this paper, we review the current progress of QGLMs from the perspective of machine learning. Particularly, we interpret these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum variational autoencoders, as the quantum extension of classical generative learning models. In this context, we explore their intrinsic relations and their fundamental differences. We further summarize the potential applications of QGLMs in both conventional machine learning tasks and quantum physics. Last, we discuss the challenges and further research directions for QGLMs.

Quantum Computing: Fundamentals, Progress, and Implications

Abstract: Quantum computing, an interdisciplinary field blending quantum mechanics and computer science, holds promise to revolutionize computational capabilities. Unlike classical computers, which utilize bits to process information in a binary fashion, quantum computers deploy qubits, capable of existing in multiple states simultaneously due to superposition. This unique property, paired with entanglement, offers unparalleled processing power, positioning quantum computing to tackle problems deemed unsolvable by classical means. While heralded for potential applications in cryptography, drug discovery, and complex modeling, quantum computing faces challenges including decoherence, scalability, and error correction. This paper delves into the foundational principles of quantum computing, its current advancements, the vast applications it could revolutionize, and the significant challenges and societal implications it brings. Through comprehensive exploration, we aim to shed light on both the transformative potential of quantum computing and the hurdles that lay in its path.

ON THE FEATURES OF THE FUNCTIONING OF CONSCIOUSNESS IN THE CONTEXT OF QUANTUM INFORMATION

The article deals with the relationship of ideas about the quantum-physical foundations of the functioning of consciousness with quantum computer science. The fundamental question of the nature of consciousness as a unique phenomenon characterized by self-development, adaptive functions, nonlinearity and openness remains debatable. Modern research on the so-called “quantum concept of consciousness” opens up the prospect for subsequent discoveries of new, unsolved patterns. At the junction of quantum mechanics, psychophysics and neurobiology, an interdisciplinary field of knowledge is now being formed and dynamically developing. The comprehension of the methods and formalisms developed within its framework in application to the ontological aspect of the essential nature of consciousness is chosen as the subject of research in this work. The universal laws of quantum mechanics, the concepts of quantum entanglement and the nonlocality of the wave function are important tools for understanding the role of the observer's consciousness at the quantum-molecular level (nanoscale) of consciousness functioning. The article shows that the principles of quantum data processing have a certain superiority over traditional methods of information processing and can become a significant analogue in the development of the quantum concept of consciousness. The characteristic processes of the “work” of consciousness are revealed, which cannot be comprehended without reference to entropic processes and quantum jumps at bifurcation points, entailing stochastic choice, i.e. information generation. The hypothesis of the quantum concept of consciousness is considered in relation to the macroscopic nature of consciousness, its synergetic complexity. The main conclusion is connected with the idea that the modern understanding of consciousness is undergoing many changes due to the widespread introduction of new knowledge in the field of computer science, quantum physics, and neuroscience.

Computing the Associated Legendre Polynomials Through A Matrix-Eigenvalue Algorithm

In recent years, the Legendre and other Sturm-Liouville polynomials have been computed using matrix-eigenvalue algorithms. In the present work, we extend the Legendre matrix-eigenvalue algorithm to compute the associated Legendre polynomials, which have broad applications in a large variety of fields including quantum mechanics and computer science.

What the foundations of quantum computer science teach us about chemistry.

With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.

Complexity and Emergent Care

Postmodernity arises with the paradigm of Complexity Sciences (CC) with support for theories, models, and practices of holistic care. They break with prevalent postulates. Open to innovative theoretical development, experimentation, and research, they reconstruct knowledge with universalistic cognitive omnipresence. Founded on quantum physics, qualitative mathematics and computer science, Galena's scientific knowledge and theories are under review, although you close your eyes to certain findings. Nursing has the opportunity to overcome the linear limits imposed by the evident scientific rational structure, circumscribed to materiality, sheltered by the hegemonic medical model (MMH).

QUANTUM COMPUTING

The quantum computer, is a "supercomputer" that relies on the phenomena of quantum mechanics to perform operations on data. Object of suppositions, sometimes farfetched, quantum mechanics gave birth to the quantum computer, a machine capable of processing data tens of millions of times faster than a conventional computer. A quantum computer doesn't use the same memory as a conventional computer. Rather than a sequence of 0 and 1, it works with qubits or quantum bits. The quantum computer is a combination of two major scientific fields: quantum mechanics and computer science. Quantum mechanics, on which this computer is based, governs the movement of bodies in the atomic, molecular and corpuscular domains, is a theory whose logic is totally contrary to intuition and it is essential to use mathematics to fully grasp it. Quantum computing is the sub-domain of computer science that deals with quantum computers using quantum mechanical phenomena, as opposed to those of electricity exclusively, for so-called "classical" computing. The quantum phenomena used are quantum entanglement and superposition. The article examines some aspects related to the development, operation, advantages and difficulties, applications and future of the quantum computer.

Complejidad y cuidado emergente

La postmodernidad surge con el paradigma de las Ciencias de la Complejidad (CC) con apoyo a teorías, modelos y prácticas de cuidado holístico. Rompen con postulados prevalentes. Abiertas al desarrollo teórico innovador, la experimentación y la investigación, reconstruyen los conocimientos con omnipresencia cognoscitiva universalista. Fundadas en la física cuántica, las matemáticas cualitativas y la informática, los conocimientos y las teorías científicas galenas están en revisión, aunque cierras los ojos ante ciertos hallazgos. La Enfermería tiene la oportunidad de superar los límites lineales impuestos por la estructura racional científica, evidente, circunscripta a la materialidad, acobijadas por el modelo médico hegemónico (MMH).

Adaptive characterization of spatially inhomogeneous fields and errors in qubit registers

New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for calibration or decoherence mitigation on neighboring data qubits, but little work has addressed how such schemes may be efficiently implemented in order to maximize information utilization. Techniques from classical estimation and dynamic control, suitably adapted to the strictures of quantum measurement, provide an opportunity to extract augmented hardware performance through automation of low-level characterization and control. In this work, we present an adaptive learning framework, Noise Mapping for Quantum Architectures (NMQA), for scheduling of sensor–qubit measurements and efficient spatial noise mapping (prior to actuation) across device architectures. Via a two-layer particle filter, NMQA receives binary measurements and determines regions within the architecture that share common noise processes; an adaptive controller then schedules future measurements to reduce map uncertainty. Numerical analysis and experiments on an array of trapped ytterbium ions demonstrate that NMQA outperforms brute-force mapping by up to 20× (3×) in simulations (experiments), calculated as a reduction in the number of measurements required to map a spatially inhomogeneous magnetic field with a target error metric. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.

Quantum Programming Language: A Systematic Review of Research Topic and Top Cited Languages

Quantum programming is an emerging area developed in last 2 decades from the multidisciplinary research on quantum computing. Quantum computing combines the idea of Quantum Mechanics, Mathematics and Computer Science. This survey paper briefly gives an overview of state of the art in the field of quantum programming languages. This paper focuses on actual high-level quantum programming languages for quantum computers, their features and comparisons. We had not considered any available simulator in this paper.

QUANTUM INFORMATICS: OVERVIEW OF THE MAIN ACHIEVEMENTS

The urgency of conducting research in the field of quantum informatics is grounded. Promising areas of research are highlighted. For foreign and Russian publications and materials, a review of the main scientific results that characterize the current state of research in quantum computer science is made. It is noted that knowledge and funds are invested most intensively in the development of the architecture of a quantum computer and its elements. Despite the fact that today there is no information on the creation of a physical implementation of a quantum computer comparable in functionality to a classical digital computer, the development of quantum algorithms is one of the popular areas of research. An advantage of quantum algorithms is the fact that they reduce the time required to solve the problem due to the parallelization of operations by generating entangled quantum states and their subsequent use. This advantage (quantum acceleration) is most important when solving the problem of modeling the dynamics of complex systems and enumerated mathematical problems. (The general case of enumeration is the Grover scheme and its variants; the tasks of searching for hidden periods: Shor's scheme of using the fast quantum Fourier transform and its analogues.) The demand for cybersecurity developments (search for vulnerabilities in smart spaces, secure storage and use of big data, quantum cryptography) is noted. More than a dozen articles are devoted to quantum algorithms of key search, key distribution on optical fibers of various lengths, and the analysis of quantum resources necessary for conducting a cyber attack. In the field of artificial quantum intelligence, attention is paid, first of all, to the “search” for a model of a quantum neural network that is optimal from the point of view of using all the advantages presented by quantum computing and neural networks, as well as machine learning algorithms. Examples of the use of quantum computing in cognitive and social sciences for studying the decision-making mechanism with incomplete data are given. It is concluded that quantum informatics is promising for the simulation of complex natural and artificial phenomena and processes.

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM’s five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target’s dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target’s dynamics accordingly.

QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

Quantum Chemistry As a Tool for Materials Development

In the past, new materials have been developed by empirically correlating their chemical composition, and the manufacturing processes used to form them, with their properties. The rapid advancements in quantum theory, computer science, and computational power brought about tremendous developments in materials research. Nowadays quantum chemistry, or more generally computational chemistry, can be used as a tool for developments of new materials through a synergic, computational and experimental, work. In this presentation we will give an overview of how a combination of efficient quantum mechanic calculations and experimental data can lead to new materials development through catalyst rational design. In the first part, we will focus our attention on polymeric materials by Ziegler-Natta catalysis, and production of new materials through organometallic metathesis. Then, application quantum chemistry to the study of solar cell systems will be discussed.

Research progress on quantum informatics and quantum computation

Quantum informatics is an emerging interdisciplinary subject developed by the combination of quantum mechanics, information science, and computer science in the 1980s. The birth and development of quantum information science has far-reaching significance in science and technology.At present, the application of quantum information technology has become the direction of people’s efforts. The preparation, storage, purification and regulation, transmission, quantum coding and decoding of quantum state have become the hotspot of scientists and technicians, which have a profound impact on the national economy and the people’s livelihood, technology and defense technology. This paper first summarizes the background of quantum information science and quantum computer and the current situation of domestic and foreign research, and then introduces the basic knowledge and basic concepts of quantum computing. Finally, several quantum algorithms are introduced in detail, including Quantum Fourier transform, Deutsch-Jozsa algorithm, Shor’s quantum algorithm, quantum phase estimation.

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM's five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target's dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target's dynamics accordingly.

EVOLUTIONARY ALGORITHM INSPIRED BY THE METHODS OF QUANTUM COMPUTER SCIENCES FOR THE IMPROVEMENT OF A NEURAL MODEL OF THE ELECTRIC POWER EXCHANGE

The work contains results of research on the possibility to improve the neural model of the Electric Power Exchange (polish: Towarowa Giełda Energii Elektrycznej – TGEE) in MATLAB and Simulink environment using evolutionary algorithm inspired by quantum computer science. The developed artificial neural network was trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy [MWh] as the input quantities in each hour of the 24-hour day, and average prices [PLN/MWh] as output quantities. The obtained model of the exchange system was improved using the evolutionary algorithm, and further improvement in the accuracy of the model by supplementing the evolutionary algorithm using quantum solutions, related to the initial population, crossover and mutation operators, selection, etc. were proposed.

Quantum Latin squares and unitary error bases

In this paper we introduce quantum Latin squares, combinatorial quantum objects which generalize classical Latin squares, and investigate their applications in quantum computer science. Our main results are on applications to unitary error bases (UEBs), basic structures in quantum information which lie at the heart of procedures such as teleportation, dense coding and error correction. We present a new method for constructing a UEB from a quantum Latin square equipped with extra data. Developing construction techniques for UEBs has been a major activity in quantum computation, with three primary methods proposed: shift-and-multiply, Hadamard, and grouptheoretic. We show that our new approach simultaneously generalizes the shift-andmultiply and Hadamard methods. Furthermore, we explicitly construct a UEB using our technique which we prove cannot be obtained from any of these existing methods.

Quantum Latin squares and unitary error bases

In this paper we introduce quantum Latin squares, combinatorial quantum objects which generalize classical Latin squares, and investigate their applications in quantum computer science. Our main re...

Role of Bose-Einstein Condensate and Bioplasma in Shaping Consciousness

Quantum mechanics can contribute to the development of psychological knowledge by creating new models to explain the mechanism of perception and the nature of mental processes. This knowledge will in turn facilitate the combination of electronic and information systems with the human biological system in robots. Such an arrangement, in the form of mutually coupled systems, can contribute to humans’ becoming far more intelligent, acquiring better social skills, developing an universal management system, being provided with a wide range of language learning opportunities etc. It will pave the way for new educational systems, based on quantum computer science and artificial intelligence. In an electronic model of life the biological system is understood to be a system composed of piezoelectric and pyroelectric devices, protein semiconductors, nucleic acids and melanin. In this system control is effected through a network of electron, photon, phonon, spin and soliton information channels as well as bioplasma. Each of these channels can in itself be a carrier of information, or they can function collectively as a group in the bioplasma system.