Quantum Artificial Life in an IBM Quantum Computer

Information processing at the speed of light

In recent years, quantum computing has made significant strides, particularly in light-based technology. The introduction of quantum photonic chips has ushered in an era marked by scalability, stability, and cost-effectiveness, paving the way for innovative possibilities within compact footprints. This article provides a comprehensive exploration of photonic quantum computing, covering key aspects such as encoding information in photons, the merits of photonic qubits, and essential photonic device components including light squeezers, quantum light sources, interferometers, photodetectors, and waveguides. The article also examines photonic quantum communication and internet, and its implications for secure systems, detailing implementations such as quantum key distribution and long-distance communication. Emerging trends in quantum communication and essential reconfigurable elements for advancing photonic quantum internet are discussed. The review further navigates the path towards establishing scalable and fault-tolerant photonic quantum computers, highlighting quantum computational advantages achieved using photons. Additionally, the discussion extends to programmable photonic circuits, integrated photonics and transformative applications. Lastly, the review addresses prospects, implications, and challenges in photonic quantum computing, offering valuable insights into current advancements and promising future directions in this technology.

Simulation of Interaction-Induced Chiral Topological Dynamics on a Digital Quantum Computer

Chiral edge states are highly sought after as paradigmatic topological states relevant to both quantum information processing and dissipationless electron transport. Using superconducting transmon-based quantum computers, we demonstrate chiral topological propagation that is induced by suitably designed interactions, instead of flux or spin-orbit coupling. Also different from conventional 2D realizations, our effective Chern lattice is implemented on a much smaller equivalent 1D spin chain, with sequences of entangling gates encapsulating the required time-reversal breaking. By taking advantage of the quantum nature of the platform, we circumvented difficulties from the limited qubit number and gate fidelity in present-day noisy intermediate-scale quantum era quantum computers, paving the way for the quantum simulation of more sophisticated topological states on very rapidly developing quantum hardware.

The Effectiveness of Teacher Support for Students’ Learning of Artificial Intelligence Popular Science Activities

The burgeoning of new technologies is increasingly affecting people’s lives. One new technology that is heatedly discussed is artificial intelligence (AI) in education. To allow students to understand the impact of emerging technologies on people’s future lives from a young age, some popular science activities are being progressively introduced into elementary school curricula. Popular science activities are informal education programs and practices of universal education. However, two issues need to be discussed in the implementation of these activities. First, because these informal curricula are usually short in duration, the question of whether they only serve to generate motivation or actually enhance learning outcomes requires examination. Second, the role of teacher support in popular science activities and its impact on students’ learning results need to be further investigated. To this end, this study aims to explore the effectiveness of popular AI science activities in informal curricula on students’ AI achievement and the interrelationship between students’ learning outcomes in popular AI science activities with and without teacher support. A 6-h-long AI popular science activity was conducted with 22 fifth- and sixth-grade students in elementary school. This study was conducted using a one-group pretest and posttest design, and the data collection tools included AI achievement pre- and posttests and an artifact scoring rubric. The results showed that with regard to learning outcomes, popular science activities were helpful for cognitive enhancement of AI concepts, but more time was needed for skills to improve. Additionally, this study found that students’ learning performance was different with and without teacher support. Activities with teacher support can enhance students’ learning outcomes, but students become accustomed to relying on their teachers. In contrast, activities without teacher support seem to be more effective in fostering students’ independent computational thinking and problem-solving abilities.

Quantum Brain Networks: A Perspective

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.

Comparison of the similarity between two quantum images

Comparing the similarity between digital images is an important subroutine in various image processing algorithms. In this study, we present three quantum algorithms for comparing the similarity between two quantum images. These algorithms are applied to binary, grey and color images for the first time. Without considering the image preparation, the proposed algorithms achieve exponential acceleration than the existing quantum and classical methods in all three cases. At the end of this paper, an experiment based on the real quantum computer of IBMQ and simulations verify the effectiveness of the algorithms.

Fundamental Physics and Computation: The Computer-Theoretic Framework

The central goal of this manuscript is to survey the relationships between fundamental physics and computer science. We begin by providing a short historical review of how different concepts of computer science have entered the field of fundamental physics, highlighting the claim that the universe is a computer. Following the review, we explain why computational concepts have been embraced to interpret and describe physical phenomena. We then discuss seven arguments against the claim that the universe is a computational system and show that those arguments are wrong because of a misunderstanding of the extension of the concept of computation. Afterwards, we address a proposal to solve Hempel’s dilemma using the computability theory but conclude that it is incorrect. After that, we discuss the relationship between the proposals that the universe is a computational system and that our minds are a simulation. Analysing these issues leads us to proposing a new physical principle, called the principle of computability, which claims that the universe is a computational system (not restricted to digital computers) and that computational power and the computational complexity hierarchy are two fundamental physical constants. On the basis of this new principle, a scientific paradigm emerges to develop fundamental theories of physics: the computer-theoretic framework (CTF). The CTF brings to light different ideas already implicit in the work of several researchers and provides a new view on the universe based on computer theoretic concepts that expands the current view. We address different issues regarding the development of fundamental theories of physics in the new paradigm. Additionally, we discuss how the CTF brings new perspectives to different issues, such as the unreasonable effectiveness of mathematics and the foundations of cognitive science.

Self-replication of a quantum artificial organism driven by single-photon pulses

Imitating the transition from inanimate to living matter is a longstanding challenge. Artificial life has achieved computer programs that self-replicate, mutate, compete and evolve, but lacks self-organized hardwares akin to the self-assembly of the first living cells. Nonequilibrium thermodynamics has achieved lifelike self-organization in diverse physical systems, but has not yet met the open-ended evolution of living organisms. Here, I look for the emergence of an artificial-life code in a nonequilibrium physical system undergoing self-organization. I devise a toy model where the onset of self-replication of a quantum artificial organism (a chain of lambda systems) is owing to single-photon pulses added to a zero-temperature environment. I find that spontaneous mutations during self-replication are unavoidable in this model, due to rare but finite absorption of off-resonant photons. I also show that the replication probability is proportional to the absorbed work from the photon, thereby fulfilling a dissipative adaptation (a thermodynamic mechanism underlying lifelike self-organization). These results hint at self-replication as the scenario where dissipative adaptation (pointing towards convergence) coexists with open-ended evolution (pointing towards divergence).

Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction

COVID-19 is a respiratory tract infection that can range from being mild to fatal. In India, the countrywide lockdown has been imposed since 24th march 2020, and has got multiple extensions with different guidelines for each phase. Among various models of epidemiology, we use the SIR(D) model to analyze the extent to which this multi-phased lockdown has been active in ‘flattening the curve’ and lower the threat. Analyzing the effect of lockdown on the infection may provide a better insight into the evolution of epidemic while implementing the quarantine procedures as well as improving the healthcare facilities. For accurate modelling, incorporating various parameters along with sophisticated computational facilities are required. Parallel to SIRD modelling, we tend to compare it with the Ising model and derive a quantum circuit that incorporates the rate of infection and rate of recovery, etc as its parameters. The probabilistic plots obtained from the circuit qualitatively resemble the shape of the curve for the spread of Coronavirus. We also demonstrate how the curve flattens when the lockdown is imposed. This kind of quantum computational approach can be useful in reducing space and time complexities of a huge amount of information related to the epidemic.

Experimental realization of controlled quantum teleportation of arbitrary qubit states via cluster states

Controlled quantum teleportation involves a third party as a controller for the teleportation of state. Here, we present the novel protocols for controlling teleportation of the arbitrary two-qubit and three-qubit states through five-qubit and seven-qubit cluster states respectively. In these schemes, Alice sends the arbitrary qubit states to the remote receiver Bob through the cluster states as quantum channels under the control of Charlie. Bob can recover the mentioned states by making appropriate unitary operations, and we point out that the efficiency in our schemes is 100%. In the process of our analysis, we find the classical communication cost in our protocols is remarkably reduced when compared to the previous protocols. We perform the experimental realization of the above protocols on "IBM 16 Melbourne" quantum computer and "IBM quantum simulator" and we calculate the fidelity. We also examine the security analysis against Charlie, and these schemes which we considered here are secure against Charlie's attacks.

Quantum machine learning and quantum biomimetics: A perspective

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing ‘intelligent’ quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.

Reinforcement learning for semi-autonomous approximate quantum eigensolver

The characterization of an operator by its eigenvectors and eigenvalues allows us to know its action over any quantum state. Here, we propose a protocol to obtain an approximation of the eigenvectors of an arbitrary Hermitian quantum operator. This protocol is based on measurement and feedback processes, which characterize a reinforcement learning protocol. Our proposal is composed of two systems, a black box named environment and a quantum state named agent. The role of the environment is to change any quantum state by a unitary matrix U ˆ E = e − i τ  ˆ E where  ˆ E is a Hermitian operator, and τ is a real parameter. The agent is a quantum state which adapts to some eigenvector of  ˆ E by repeated interactions with the environment, feedback process, and semi-random rotations. With this proposal, we can obtain an approximation of the eigenvectors of a random qubit operator with average fidelity over 90% in less than 10 iterations, and surpass 98% in less than 300 iterations. Moreover, for the two-qubit cases, the four eigenvectors are obtained with fidelities above 89% in 8000 iterations for a random operator, and fidelities of 99% for an operator with the Bell states as eigenvectors. This protocol can be useful to implement semi-autonomous quantum devices which should be capable of extracting information and deciding with minimal resources and without human intervention.

The finer scale of consciousness: quantum theory

Consciousness is a multidisciplinary problem that has puzzled all human beings since the origin of human life. Being defined in various pointcuts by philosophers, biologists, physicists, and neuroscientists, the definitive explanation of consciousness is still suspending. The nature of consciousness has taken great evolution by centering on the behavioral and neuronal correlates of perception and cognition, for example, the theory of Neural Correlates of Consciousness, the Global Workspace Theory, the Integrated Information Theory. While tremendous progress has been achieved, they are not enough if we are to understand even basic facts-how and where does the consciousness emerge. The Quantum mechanics, a thriving branch of physics, has an inseparable relationship with consciousness (e.g., observer effect) since Planck created this subject and its derived quantum consciousness theory can perfectly fill this gap. In this review, we briefly introduce some consciousness hypotheses derived from quantum mechanics and focus on the framework of orchestrated objective reduction (Orch-OR), including its principal points and practicality.

The Problem of Meaning in AI and Robotics: Still with Us after All These Years

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches.