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arXiv:2004.01331 · Electronic Proceedings in Theoretical Computer Science

Growing Random Graphs with Quantum Rules

Hamza Jnane, Giuseppe Di Molfetta, Filippo M. Miatto

Published Apr 1, 2020• 0 citations
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Abstract

Random graphs are a central element of the study of complex dynamical networks such as the internet, the brain, or socioeconomic phenomena. New methods to generate random graphs can spawn new applications and give insights into more established techniques. We propose two variations of a model to grow random graphs and trees, based on continuous-time quantum walks on the graphs. After a random characteristic time, the position of the walker(s) is measured and new nodes are attached to the nodes where the walkers collapsed. Such dynamical systems are reminiscent of the class of spontaneous collapse theories in quantum mechanics. We investigate several rates of this spontaneous collapse for an individual quantum walker and for two non-interacting walkers. We conjecture (and report some numerical evidence) that the models are scale-free.

DOI
10.4204/eptcs.315.4

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It is well known that many real world networks have a power-law degree distribution (the scale-free property). However, there are no rigorous results for continuous-time quantum walks on such realistic graphs. In this paper, we analyse the space–time behaviour of continuous-time quantum walks and random walks on the threshold network model, which is a reasonable candidate model having the scale-free property. We show that the quantum walker exhibits localisation at the starting point, although the random walker tends to spread uniformly.
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Quantum random walks have received much interest due to their nonintuitive dynamics, which may hold the key to a new generation of quantum algorithms. What remains a major challenge is a physical realization that is experimentally viable and not limited to special connectivity criteria. We present a scheme for walking on arbitrarily complex graphs, which can be realized using a variety of quantum systems such as a Bose-Einstein condensate trapped inside an optical lattice. This scheme is particularly elegant since the walker is not required to physically step between the nodes; only flipping coins is sufficient.
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The generation of true randomness remains a significant challenge in computer science, particularly when relying solely on classical algorithms. Many current approaches employ pseudo-random number generators (PRNGs), which, despite their computational efficiency, are inherently predictable and lack genuine randomness. This paper proposes a novel method for algorithmic randomness generation leveraging the principles of quantum walks. We demonstrate that the inherent uncertainty and superposition properties of quantum systems provide a robust mechanism for generating truly random numbers. Specifically, we outline a framework for designing quantum walks on graphs, where the evolution of the walker's state is fundamentally probabilistic and unpredictable. The core of our approach resides in exploiting the quantum walk's non-deterministic nature to produce sequences of numbers that exhibit statistical properties consistent with true randomness. The theoretical framework is presented, including a detailed description of the mathematical formalism underlying the quantum walk and the probability distribution of the walker's positions. We highlight the potential advantages of this approach, particularly in scenarios demanding high-quality randomness for cryptographic applications or simulations. The key concept is to translate a classical problem into a quantum one, where the inherent probabilistic nature of quantum mechanics offers a superior solution to the problem of generating genuine randomness.
Quantum walks provide a natural framework to approach graph problems with quantum computers, exhibiting speedups over their classical counterparts for tasks such as the search for marked nodes or the prediction of missing links. Continuous-time quantum walk algorithms assume that we can simulate the dynamics of quantum systems where the Hamiltonian is given by the adjacency matrix of the graph. It is known that such can be simulated efficiently if the underlying graph is row-sparse and efficiently row-computable. While this is sufficient for many applications, it limits the applicability for this class of algorithms to study real world complex networks, which, among other properties, are characterized by the existence of a few densely connected nodes, called hubs. In other words, complex networks are typically not row-sparse, even though the average connectivity over all nodes can be very small. In this work, we extend the state-of-the-art results on quantum simulation to graphs that contain a small number of hubs, but that are otherwise sparse. Hopefully, our results may lead to new applications of quantum computing to network science.