Hey there! 👋
Cláudio Gomes here.

I'm a dual-degree PhD student with one foot at the Carnegie Mellon University and the other foot at the University of Porto. Pretty exciting, I know! 😄

My research focuses on sustainable forms of computing, such as quantum computing. Massive amounts of information are handled and processed by small, big and super computers all over the world. Any alternative form of computing that offers the same processing power for a lot less energy would have a tremendous benefit for society! My goal is to augment classical computing with sustainable alternatives. 🌳

Welcome to my webpage and feel free to contact me! 🤠

Find me in those places! 👇

Check out my research! 💡

An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization

2022-06-14

Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains. [DOI](http://doi.org/10.1007/s42979-022-01215-9)

Portfolio Optimization in Financial Markets using Quantum Computing: An Experimental Study

2021-07-15

Quantum computing is bound to change the world as we know it. By exploring the properties of quantum theory for computational purposes, it is expected to substantially reduce the amount of problems that are nowadays considered computationally intractable. This means that quantum computers have the power of providing solutions for some of the problems of practical interest for which a classical computer cannot, at least in a timely manner. This is even more revolutionary and remarkable given the fact these problems range from multidisciplinary domains such as Chemistry, Medicine, and, most relevant in the context of this dissertation, Finance. In this work, we will focus on leveraging quantum computing to addressing a relevant and timely problem within the financial domain. We will target a combinatorial optimization problem, the portfolio optimization problem, which consists of selecting the best portfolio (combination of assets) among all possible portfolios, according to some objective function, whether to maximize return or minimize risk. Due to the high number of parameters, such as the expected return per asset and market conditions, this problem attains an exponential complexity and is an NP-hard problem, intractable in the context of classical computing. We designed and conducted an empirical study on the effect of parameters on solutions to the portfolio optimization problem given by a quantum computer. In particular, we use a quantum computer from D-Wave Systems, Inc. and vary the parameters related to not only the quantum computer, but also to the portfolio optimization problem itself. We believe that our findings are useful not only for those using adiabatic quantum computers in the context of portfolio optimization problem, and also in other application domains. Our findings suggest that the parameters do have an effect on the results, whether they are related to the portfolio optimization problem or to the quantum computer. Moreover, we found that some of the parameters have a great impact, such as the chain strength, which defines the strength associated to the couplings between qubits that represent a variable, and that other parameters have no statistically significant effect, such as the anneal schedule or embedding used. [URI](http://hdl.handle.net/10316/96124)

And also some random stuff...

Hey! I'm Cláudio Gomes.

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Here are my contacts:

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