We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. Specifically, we introduce a general method to parametrize and optimize the probability density function of a random number generator, which is the core of stochastic algorithms. We follow a bioinspired evolutionary mutation method to introduce changes in the involved matrices. Our optimization is based on two figures of merit: learning speed and learning accuracy. This method provides high fidelities for the searched eigenvectors and faster convergence on the way to quantum advantage with current noisy intermediate-scaled quantum computers.
There is no doubt artificial intelligence and machine learning have been significantly modifying the scientific and technological landscape, as well as society at large, over the last decade. Search engines, autonomous cars, language and speech translators, as well as image classifiers are only some possibilities this discipline enables and there is certainly more to come in the near future. Significant research efforts have been dedicated in the direction of quantum computing, a topic that has been receiving ever-growing attention from governmental institutions, multinational companies, and academics. A scalable quantum computer could offer unprecedented computational capabilities, enabling a revolution in information processing and transmission. Among the various approaches, quantum machine learning has emerged as an utterly successful method to better control and understand quantum systems, benefitting from machine learning algorithms, while relying on quantum processors to accelerate machine learning tasks. When coupled with biomimetics, an area of science that aims at imitating biological systems in order to design more efficient artificial devices, quantum biomimetics arises as a novel field that bridges the gap between natural selection in bio systems and engineering processes in quantum devices with one ultimate goal: The replication of biological behaviors in quantum controllable systems, what we call “bioinspired quantum technologies”. We believe that this Special Issue will shed light on unexplored areas up to now, acting as the precursor for further exciting research in the field of bioinspired quantum technologies.
Given an undirected, unweighted graph with n vertices and m edges, the maximum cut problem is to find a partition of the n vertices into disjoint subsetsV1andV2such that the number of edges between them is as large as possible. Classically, it is an NP-complete problem, which has potential applications ranging from circuit layout design, statistical physics, computer vision, machine learning and network science to clustering. In this paper, we propose a biomolecular and a quantum algorithm to solve the maximum cut problem for any graph G. The quantum algorithm is inspired by the biomolecular algorithm and has a quadratic speedup over its classical counterparts, where the temporal and spatial complexities are reduced to, respectively,O(2n/r)andO(m2). With respect to oracle-related quantum algorithms for NP-complete problems, we identify our algorithm as optimal. Furthermore, to justify the feasibility of the proposed algorithm, we successfully solve a typical maximum cut problem for a graph with three vertices and two edges by carrying out experiments on IBM’s quantum simulator.