Adriano Maron

High-Performance Quantum Computing Simulation for the Quantum Geometric Machine Model

Due to the unavailability of quantum computers, simulation of quantum algorithms using classical computers is still the most affordable option for research of algorithms and models for quantum computing. As such simulation requires high computing power and memory resources, and computations are regular and data-intensive, GPUs become a suitable solution for accelerating the simulation of quantum algorithms. This work introduces an extension of the execution library for the VPE-qGM environment to support GPU acceleration. Hadamard gates with up to 20 quantum bits were simulated, and speedups of up to approximately 540× over a sequential simulation and of approximately 85× over a 8-core distributed simulation in the VirD-GM environment were achieved.

Socialite: A Flexible Framework for Social Internet of Things

The Social Internet of Things is a new paradigm that merges the Internet of Things with Social Networks. We propose a paradigm for how to model interactions between devices and humans, and among devices themselves to support sharable value-added applications throughout the device life cycle (e.g., Configuration, operation, preventive diagnosis), as well as to achieve common goals and recommendations. To realize this paradigm we propose a novel architecture and implementation to enable new Internet of Things applications based on emerging types of social relationships. We develop the semantic models for the Social Internet of Things including device types and their capabilities, users and their relationships, and rules leveraging such models. We demonstrate our concept by building a real system called Socialite that integrates various devices from different manufacturers with different functional interfaces and explicitly represents the relationships among active participants (devices as well as users) in our system.

Aggregation operations from quantum computing

Computer systems based on fuzzy logic are capable of generating a reliable output even when handling inaccurate input data by applying a rule based system. The main contribution of this paper is to show that quantum computing can be used to extend the class of fuzzy sets. The central idea associates the states of a quantum register to membership functions (mFs) of fuzzy subsets, and the rules for the processes of fuzzyfication are performed by unitary quantum transformations. Thus, this paper describes multi-dimensional quantum registers, associated to mFs on the unitary interval U, in order to introduce a novel interpretation of aggregations found in fuzzy set theory. In particular, t-norms and t-conorms based on quantum gates allows the modeling and interpretation of union, intersection, difference and implication among fuzzy sets, also including an expression for the class of fuzzy S-implications. Furthermore, an interpretation of the symmetric sum was achieved by considering the quantum register sum operator.

Quantum processes: A new interpretation for quantum transformations in the VPE-qGM environment

The simulation of quantum algorithms in classic computers is a task which requires high processing and storing capabilities, limiting the size of quantum systems supported by the simulators. However, optimizations for reduction of temporal and spatial complexities are promising, expanding the capabilities of some simulators. The main contribution of this work consists in designing optimizations resulting from the description of quantum transformations using Quantum Processes and Partial Quantum Processes conceived in the qGM theoretical model. These processes, when computed on the VPE-qGM execution environment, require low execution time and result in the improvement of the performance, allowing the simulation of more complex quantum algorithms. The performance evaluation of this proposal was performed by benchmarks used in similar works and included the sequential simulation of quantum algorithms up to 24 qubits. The results are promising when compared to the state-of-art, indicating the possibilities of advances in this research.

Optimizing Quantum Simulation for Heterogeneous Computing: a Hadamard Transformation Study

The D-GM execution environment improves distributed simulation of quantum algorithms in heterogeneous computing environments comprising both multi-core CPUs and GPUs. The main contribution of this work consists in the optimization of the environment VirD-GM, conceived in three steps: (i) the theoretical studies and implementation of the abstractions of the Mixed Partial Process defined in the qGM model, focusing on the reduction of the memory consumption regarding multidimensional QTs; (ii) the distributed/parallel implementation of such abstractions allowing its execution on clusters of GPUs; (iii) and optimizations that predict multiplications by zero-value of the quantum states/transformations, implying reduction in the number of computations. The results obtained in this work embrace the distribute/parallel simulation of Hadamard gates up to 21 qubits, showing scalability with the increase in the number of computing nodes.

Relation between polling and Likert-scale approaches to eliciting membership degrees clarified by quantum computing

In fuzzy logic, there are two main approaches to eliciting membership degrees: an approach based on polling experts, and a Likert-scale approach, in which we ask experts to indicate their degree of certainty on a scale - e.g., on a scale form 0 to 10. Both approaches are reasonable, but they often lead to different membership degrees. In this paper, we analyze the relation between these two approaches, and we show that this relation can be made much clearer if we use models from quantum computing.

Distributed Quantum Simulation on the VPE-qGM Environment

This work describes a proposal for a platform focused on modeling and simulation (sequential or distributed) of quantum algorithms. More specifically, the integration between the environments VPE-qGM and VirD-GM is considered, providing a solution to the high computational cost associated to the simulation of quantum algorithms on classical computers. In this context, the algorithms are visually programmed and the details of distribution, communication and synchronization, required for the execution in distributed systems, are transparently managed by the execution environment. The validation and performance analysis consist in simulating a quantum search algorithm, obtaining results which demonstrate the performance gain of the platform compared to the sequential simulation.

qGMC-Analyzer -- Quantum Simulation on Multicore Architectures

This paper presents an extension of the VPE-qGM execution environment in order to support parallel execution using OpenMP. As a relevant part of a broader project represented by the simulation framework D-GM which aims to achieve quantum simulation on hybrid architectures (clusters, multicore CPUs and GPUs), this work enables the control and execution of the simulation from multicore architectures. This proposal is shown by providing relevant management simulation on multicore CPUs, which will be later combined with GPU targeted implementations. From the synergy between these implementations, this work aims to establish a general platform for hybrid simulation, exploring different architectures usually found in clusters and expanding the capabilities of the simulation environment VPE-qGM.