Carlos Pedro Gonçalves

Carlos Pedro Gonçalves

The development of automated gate specification for quantum communications and quantum networked computation opens up the way for malware designed at corrupting the automation software, changing the automated quantum communications protocols and algorithms. We study two types of attacks on automated quantum communications protocols and simulate these attacks on the superdense coding protocol, using remote access to IBM's Quantum Computers available through IBM Q Experience to simulate these attacks on what would be a low noise quantum communications network. The first type of attack leads to a hacker-controlled bijective transformation of the final measured strings, the second type of attack is a unitary scrambling attack that modifies the automated gate specification to effectively scramble the final measurement, disrupting quantum communications and taking advantage of quantum randomness upon measurement in a way that makes it difficult to distinguish from hardware malfunction or from a sudden rise in environmental noise. We show that, due to quantum entanglement and symmetries, the second type of attack works as a way to strategically disrupt quantum communications networks and quantum networked computation in a way that makes it difficult to ascertain which node was attacked. The main findings are discussed in the wider setting of quantum cybersecurity and quantum networked computation, where ways of hacking including the role of insider threats are discussed.

Carlos Pedro Gonçalves

An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a complex main problem expressed as a network of linked subproblems. In the case of neural networks, these systems function as a form of an "artificial brain", where each module is like a specialized brain region comprised of a neural network with a specific architecture.

Carlos Pedro Gonçalves

The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to solve computing problems. The results are extended to general architectures including recurrent networks that interact with an environment, coupling with it in the neural links' activation order, and self-organizing in a dynamical regime that intermixes patterns of dynamical stochasticity and persistent quasiperiodic dynamics, making emerge a form of noise resilient dynamical record.

Carlos Pedro Gonçalves

Econophysics has developed as a research field that applies the formalism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quantum Econophysics. In Finance, Quantum Econophysics' contributions have ranged from option pricing to market dynamics modeling, behavioral finance and applications of Game Theory, integrating the empirical finding, from human decision analysis, that shows that nonlinear update rules in probabilities, leading to non-additive decision weights, can be computationally approached from quantum computation, with resulting quantum interference terms explaining the non-additive probabilities. The current work draws on these results to introduce new tools from Quantum Artificial Intelligence, namely Quantum Artificial Neural Networks as a way to build and simulate financial market models with adaptive selection of trading rules, leading to turbulence and excess kurtosis in the returns distributions for a wide range of parameters.

Carlos Pedro Gonçalves

Quantum cybernetics and its connections to complex quantum systems science is addressed from the perspective of complex quantum computing systems. In this way, the notion of an autonomous quantum computing system is introduced in regards to quantum artificial intelligence, and applied to quantum artificial neural networks, considered as autonomous quantum computing systems, which leads to a quantum connectionist framework within quantum cybernetics for complex quantum computing systems. Several examples of quantum feedforward neural networks are addressed in regards to Boolean functions' computation, multilayer quantum computation dynamics, entanglement and quantum complementarity. The examples provide a framework for a reflection on the role of quantum artificial neural networks as a general framework for addressing complex quantum systems that perform network-based quantum computation, possible consequences are drawn regarding quantum technologies, as well as fundamental research in complex quantum systems science and quantum biology.

Carlos Pedro Gonçalves

The current work addresses a virtual environment with self-replicating agents whose decisions are based on a form of "somatic computation" (soma - body) in which basic emotional responses, taken in parallelism to actual living organisms, are introduced as a way to provide the agents with greater reflexive abilities. The work provides a contribution to the field of Artificial Intelligence (AI) and Artificial Life (ALife) in connection to a neurobiology-based cognitive framework for artificial systems and virtual environments' simulations. The performance of the agents capable of emotional responses is compared with that of self-replicating automata, and the implications of research on emotions and AI, in connection to both virtual agents as well as robots, is addressed regarding possible future directions and applications.