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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

1. Quantum Organics in the 21st Century

Orus, Roman. Tensor Networks for Complex Quantum Systems. Nature Reviews Physics. 1/9, 2019. We cite this extensive, well referenced paper by the Spanish physicist with postings such as Barcelona Supercomputing Center and CSO Multiverse Computing (see RO’s site) for how it treats this quantum domain in several nonlinear ways. The author goes on to develop affinities with Chomsky linguistics, machine learning, chemistry, neural net topologies and more. In regard, the entry exemplifies progress toward our current micro quantum and macro classical integral unification.

Tensor network states and methods have advanced in recent years. Originally developed in condensed matter physics and based on renormalization group ideas, tensor networks are being revived thanks to quantum information theory and understandings of entanglement in quantum many-body systems. Tensor network states play a key role in other disciplines such as quantum gravity and artificial intelligence. In this context, we provide an overview of basic concepts and key developments such as structures and algorithms, global and gauge symmetries, fermions, topological order, classification of phases, entanglement Hamiltonians, AdS/CFT, conformal field theory, quantum chemistry, disordered systems, and many-body localization. (Abstract excerpt)

Overbye, Dennis. Quantum Trickery. New York Times. December 27, 2005. From Einstein and Bohr to today’s theorists, the quantum realm seems to resist comprehension. The article touches many bases to convey an uneasy sense of something being missed, that fundamental conjectures still need revision. Are we finding irreducible randomness, or is reality in some way informational in essence.

Paparo, Giuseppe, et al. Quantum Google in a Complex Network. arXiv:1303.3891. Mathematicians Paparo, with Mark Muller and Miguel Martin-Delgado, Universidad Complutense, Madrid, and Francesc Comellas, Universitat Politecnica de Catalunya, Barcelona, make a quantum leap from this deep domain to the algorithmic worldwide web to propose that the same dynamic computational systems can be found in effect in both cases. In any event, the latest inklings of a grand unitary scale of nature and society, universe to human, as long intimated and sought, as must be there and true.

We investigate the behavior of the recently proposed quantum Google algorithm, or quantum PageRank, in large complex networks. Applying the quantum algorithm to a part of the real World Wide Web, we find that the algorithm is able to univocally reveal the underlying scale-free topology of the network and to clearly identify and order the most relevant nodes (hubs) of the graph according to their importance in the network structure. Moreover, our results show that the quantum PageRank algorithm generically leads to changes in the hierarchy of nodes. In addition, as compared to its classical counterpart, the quantum algorithm is capable to clearly highlight the structure of secondary hubs of the network, and to partially resolve the degeneracy in importance of the low lying part of the list of rankings, which represents a typical shortcoming of the classical PageRank algorithm. (Abstract)

It is of great interest to explore and classify the large amount of information that is stored in huge complex networks like the World Wide Web (WWW). A central problem of bringing order to classical information stored in networks such as the WWW amounts to rank nodes containing such information according to their relevance. A highly successful and nowadays widespread tool for this purpose has been the PageRank algorithm, which lies at the core of Google's ranking engine. In the foreseeable future where large-scale quantum networks have become a reality, classifying the quantum information stored therein will become a priority. It is in this sense that the recently introduced quantum PageRank algorithm is an important achievement as it constitutes a quantization of the classical PageRank protocol. This new quantum algorithm has shown, applied to small networks, a striking behavior with respect to its classical counterpart, such as producing a different hierarchy of nodes together, paired with a better performance. In this paper we investigate the properties of the quantum algorithm for networks which model large real-world complex systems. (1)

Paparo, Paparo, Giuseppe, et al. Quantum Speedup for Active Learning Agents. Physical Review X. 4/031002, 2014. A team of European systems physicists applies the Projective Simulation method of co-author Hans Briegel (search) to quantum phenomena which is similarly seen as capable of modifying responses and behaviors by reference to past experience. We note in another venue how it is vital to be able to accord novel events with familiar memory to effectively learn and succeed.

One of the defining characteristics of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in any real-life situation is the size and complexity of the corresponding task environment. Even for a moderate task environment, it may simply take too long to rationally respond to a given situation. Here we show that quantum physics can help and provide a significant speed-up for active learning as a genuine problem of artificial intelligence. We introduce a large class of quantum learning agents for which we show a quadratic boost in their active learning efficiency over their classical analogues. This result will be particularly relevant for applications involving complex task environments. (Abstract)

In conclusion, it seems to us that the embodied approach to artificial intelligence acquires a further fundamental perspective by combining it with concepts from the field of quantum physics. The implications of embodiment are, in the first place, described by the laws of physics, which tell us not only about the constraints but also the ultimate possibilities of physical agents. In this paper, we have shown an example of how the laws of quantum physics can be fruitfully employed in the design of future intelligent agents that will outperform their classical relatives in complex task environments. (5)

Pseiner, Johannes, et al.. Quantum interference between distant creation processes.. arXiv:2304.03683. We record this entry by University of Vienna and MPI Science of Light physicists including Mario Krenn as an example in these 2020s of how research endeavors are able to freely range about and apply this foundational quantascape.

The search for macroscopic quantum phenomena is a fundamental pursuit in quantum mechanics. In this work, we introduce a novel approach to generate macroscopic quantum systems by demonstrating that the creation process of a quantum system can span a macroscopic distance. This new approach not only provides an exciting opportunity for foundational experiments in quantum physics.

Rispoli, Matthew, et al. Quantum Critical Behavior at the Many-Body-Localization Transition. arXiv:1812.06959. While equilibrium quantum systems are said to be well quantified, non-equilibrium phenomena have not yet been. Here seven Harvard University physicists describe how these active phases can be explained by better measurements of their entanglement properties. We cite to record how the arcane quantum realm is being parsed by the same critically poised systems theory as everywhere else. And from the Abstract: Our results unify the system's microscopic structure with its macroscopic quantum critical behavior, and they provide an essential step towards understanding criticality and universality in non-equilibrium systems.

Rotter, Ingrid and J. P. Bird. A Review of Progress in the Physics of Open Quantum Systems. Reports on Progress in Physics. 78/114001, 2015. MPI Physics of Complex Systems and SUNY Buffalo scientists survey the 21st century, worldwide revolutionary understanding of this most fundamental micro-realm. As yet mostly unnoticed, an arcane, off-putting 20th century version has been set aside for the presence of complex networks similar to every other classical macro-stage.

Sachdev, Subir and Bernhard Keimer. Quantum Criticality. Physics Today. February, 2011. Harvard University and Max Planck Institute physicists are able to deeply glimpse into material realm whose phases of large numbers of particles interact at low enough temperatures that quantum effects produce the title phenomena. An expanded technical version can be found at arxiv:1102.4628.

Sanchez-Burillo, Eduardo, et al. Quantum Navigation and Ranking in Complex Networks. Nature Scientific Reviews. 2/605, 2012. Universidad de Zaragoza, and Universitat Rovira i Virgili, Spain, systems physicists cleverly notice that Google’s PageRank algorithms, in their webwork dynamics, can be similarly found and availed even in quantum realms. An extended reference list offers an entry to this considerable project. Can one now say that every disparate, stratified natural domain seems in fact to be distinguished by such ultimately genetic-like qualities?

Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems. (Abstract)

Scholes, Gregory, et al. Using Coherence to Enhance Function in Chemical and Biophysical Systems. Nature. 543/647, 2018. As quantum and complexity studies grow and converge in scope and veracity, they are erasing a classical divide so as to reveal a seamless unity (as David Bohm would say) to cross-advise each other. Here some 19 researchers from Harvard to UC Berkeley and onto Canada and Germany draw serious parallels which appear to infuse a natural universe to us vitality.

Coherence phenomena arise from interference, or the addition, of wave-like amplitudes with fixed phase differences. Although coherence has been shown to yield transformative ways for improving function, advances have been confined to pristine matter and coherence was considered fragile. However, recent evidence of coherence in chemical and biological systems suggests that the phenomena are robust and can survive in the face of disorder and noise. Here we survey the state of recent discoveries, present viewpoints that suggest that coherence can be used in complex chemical systems, and discuss the role of coherence as a design element in realizing function. (Abstract)

Defining and Detecting Coherence Coherence can be classical or quantum mechanical and comes from well - defined phase and amplitude relations where correlations are preserved over separations in space or time. While an intuitive picture for classical coherence is a recurring pattern, quantum mechanical coherence is exemplified by superposition states. The distinction between classical and quantum coherence is not always obvious, but is indicated by special correlations — a notable example is photonbunching and antibunching. Quantum superposition states thereby have properties that are not realized in classical superpositions. (647-849)

Schuld, Maria, et al. Viewpoint: Neural Networks take on Open Quantum Systems. Physics Review Letters. 122/25, 2019. University of KwaZulu-Natal, RSA physicists MS, Ilya Sinayskify and Francesco Peruccione comment on articles in this issue such as Neural Network Approach to Dissipative Quantum Many-Body Physics and Quantum Monte Carlo Method with a Neural Network Ansatz for Open Quantum Systems which report ways that this brain-based problem-solving method can similarly apply to nature’s deepest realm. By way of its physical affinity, quantum phenomena can actually possess classical dynamic complexities. See also Machine Learning and the Physical Sciences by Giuseppe Carleo, et al. at arXiv:1903.10563 and The Quest for a Quantum Neural Network by the authors in Quantum Information Processing (13/11, 2014).

Simulating a quantum system that exchanges energy with the outside world is difficult, but the necessary computations might be easier with the help of neural networks. These general problem solvers reach their solutions by being adapted or “trained” to capture correlations in real-world data. Physicists are asking if the tools might also be useful in areas ranging from high-energy physics to quantum computing. Four research groups now report on using neural networks to tackle computationally challenging problems such as simulating the behavior of an open many-body quantum system. (Abstract)

Schutt, Kristof, et al. Quantum-Chemical Insights from Deep Tensor Neural Networks. Nature Communications. 8/13890, 2017. Technical University of Berlin and MPI Fritz Haber Institute, Berlin informatics theorists provide another entry to how much quantum phenomena is now commonly treated as a complex dynamic system, akin to all other phases and scales. See also Neural Message Passing for Quantum Chemistry by Justin Gilmer at arXiv:1704.01212.

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to predictions in compositional and configurational chemical spaces. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. (Abstract)

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