publications

2026

1. Proc.

   Hardware Efficient Framework for QAOA

   Thiago Assis, Laila Melo, Cristiano Arbex, and 7 more authors

   In Anais do VIII WECIQ\textbarWCQ: Workshop-Escola de Computação e Informação Quântica, 2026

   Research supported by Inter Science – Inter&co

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   The Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. However, its performance on Noisy Intermediate-Scale Quantum (NISQ) devices is hindered by limited qubit connectivity, which increases circuit depth. We introduce a hardware-aware QAOA framework, using a Mixed Integer Semidefinite Programming (MISDP) formulation to find an approximate cost Hamiltonian compatible with hardware topology. To solve the MISDP, we propose heuristics based on spectral properties of the hardware graph. We apply this framework to the index tracking problem, and our numerical results demonstrate that this approach yields high-quality solutions.

   @inproceedings{assis2026hardware,
     title = {Hardware Efficient Framework for {QAOA}},
     author = {Assis, Thiago and Melo, Laila and Arbex, Cristiano and Assump{\c{c}}\~{a}o, Henrique and Baptista, Pedro Vin{\'i}cius Ferreira and Chaves, Rodrigo and Ferreira, Diego and Henrique, Luan and Oliveira, Mathias and Coutinho, Gabriel},
     booktitle = {Anais do VIII WECIQ{\textbar}WCQ: Workshop-Escola de Computa{\c{c}}\~{a}o e Informa{\c{c}}\~{a}o Qu{\^{a}}ntica},
     year = {2026},
     publisher = {Even3},
     address = {Florian{\'o}polis, SC, Brasil},
     url = {https://www.even3.com.br/anais/weciqwcq2025-556747/1263187-hardware-efficient-framework-for-qaoa/},
     note = {Research supported by Inter Science -- Inter{\&}co},
   }

2. Proc.

   Quantum Foundations for a Large Class of Graphs

   Thiago Assis and Gabriel Coutinho

   In Anais do VIII WECIQ\textbarWCQ: Workshop-Escola de Computação e Informação Quântica, 2026

   Research supported by Inter Science – Inter&co

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   In the graph-theoretic approach to contextuality, a key problem is to give a foundational justification for quantum theory from some simple physical principles. This was already solved for perfect graphs. We investigate whether assuming this to hold for minimally imperfect graphs implies it for all imperfect graphs. We confirm this hypothesis for a large family of graphs, G, which contains all minimally imperfect graphs and is closed under complement, disjoint union, and addition of twins. Our proof relies on a new characterization of the theta body of a graph under the addition of twins.

   @inproceedings{assis2026quantum,
     title = {Quantum Foundations for a Large Class of Graphs},
     author = {Assis, Thiago and Coutinho, Gabriel},
     booktitle = {Anais do VIII WECIQ{\textbar}WCQ: Workshop-Escola de Computa{\c{c}}\~{a}o e Informa{\c{c}}\~{a}o Qu{\^{a}}ntica},
     year = {2026},
     publisher = {Even3},
     address = {Florian{\'o}polis, SC, Brasil},
     url = {https://www.even3.com.br/anais/weciqwcq2025-556747/1263086-quantum-foundations-for-a-large-class-of-graphs/},
     note = {Research supported by Inter Science -- Inter{\&}co},
   }

3. Proc.

   Generative QAOA and its Application to Portfolio Optimization

   Luan Costa and Gabriel Coutinho

   In Anais do VIII WECIQ\textbarWCQ: Workshop-Escola de Computação e Informação Quântica, 2026

   Research supported by Inter Science – Inter&co

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   We present the Generative Quantum Approximate Optimization Algorithm (GQAOA), a method for applying classical generative models to optimize QAOA circuit parameters. Our work is inspired by the Generative Quantum Eigensolver (GQE), which trains a classical generative model to produce a sequence of quantum gates to generate quantum states that minimize the energy of a Hamiltonian. Our method adapts this framework to optimize the QAOA circuit parameters, given an Ising Hamiltonian as input that represents a real-world portfolio optimization problem.

   @inproceedings{henrique2026generative,
     title = {Generative {QAOA} and its Application to Portfolio Optimization},
     author = {Costa, Luan and Coutinho, Gabriel},
     booktitle = {Anais do VIII WECIQ{\textbar}WCQ: Workshop-Escola de Computa{\c{c}}\~{a}o e Informa{\c{c}}\~{a}o Qu{\^{a}}ntica},
     year = {2026},
     publisher = {Even3},
     address = {Florian{\'o}polis, SC, Brasil},
     url = {https://www.even3.com.br/anais/weciqwcq2025-556747/1262964-generative-qaoa-and-its-application-to-portfolio-optimization/},
     note = {Research supported by Inter Science -- Inter{\&}co},
   }

2025

1. arXiv

   CodeEvolve: an open source evolutionary coding agent for algorithmic discovery and optimization

   Henrique Assumpção, Diego Ferreira, Leandro Campos, and 1 more author

   2025

   Abs arXiv Bib Code

   We introduce CodeEvolve, an open-source framework that combines large language models (LLMs) with evolutionary search to synthesize high-performing algorithmic solutions. CodeEvolve couples an islands-based genetic algorithm with modular LLM orchestration, using execution feedback and task-specific metrics to guide selection and variation. Exploration and exploitation are balanced through context-aware recombination, adaptive meta-prompting, and targeted refinement of promising solutions. We evaluate CodeEvolve on benchmarks used to assess Google DeepMind’s AlphaEvolve, and include direct comparisons with popular open-source frameworks for algorithmic discovery and heuristic design. Our results show that CodeEvolve achieves state-of-the-art (SOTA) performance on several tasks, with open-weight models often matching or exceeding closed-source baselines at a fraction of the compute cost.

   @misc{assumpcao2025codeevolve,
     title = {CodeEvolve: an open source evolutionary coding agent for algorithmic discovery and optimization},
     author = {Assump{\c{c}}\~{a}o, Henrique and Ferreira, Diego and Campos, Leandro and Murai, Fabricio},
     year = {2025},
     archiveprefix = {arXiv},
     primaryclass = {cs.AI},
   }

2024

1. ePrint

   Applying Post-Quantum Cryptography Algorithms to a DLT-Based CBDC Infrastructure: Comparative and Feasibility Analysis

   Daniel Haro Moraes, João Paulo Aragão Pereira, Bruno Estolano Grossi, and 7 more authors

   Cryptology ePrint Archive, 2024

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   This article presents an innovative project for a Central Bank Digital Currency (CBDC) infrastructure. Focusing on security and reliability, the proposed architecture employs post-quantum cryptography (PQC) algorithms for long-term security, can be integrated with a Trusted Execution Environment (TEE) to safeguard confidentiality, and uses Distributed Ledger Technology (DLT) to promote transparency and tamper resistance. We experimentally evaluate CRYSTALS-Dilithium, Falcon, and SPHINCS+ integrated into Hyperledger Besu, both inside and outside an Intel SGX TEE. CRYSTALS-Dilithium-2 combined with classical secp256k1 signatures achieves the shortest execution times, reaching 1.68 ms for block signing without TEE and 0.5 ms for signature verification.

   @article{moraes2024pqc,
     title = {Applying Post-Quantum Cryptography Algorithms to a {DLT}-Based {CBDC} Infrastructure: Comparative and Feasibility Analysis},
     author = {de Haro Moraes, Daniel and Arag{\~a}o Pereira, Jo{\~a}o Paulo and Grossi, Bruno Estolano and Mirapalheta, Gustavo and Smetana, George Marcel Monteiro Arcuri and Rodrigues, Wesley and Guimar{\~a}es Jr., Courtnay Nery and Domingues, Bruno and Saito, F{\'a}bio and Simplicio, Marcos},
     year = {2024},
     journal = {Cryptology ePrint Archive},
     url = {https://eprint.iacr.org/2024/1206},
   }

2022

1. IEEE BigData

   DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs

   Henrique S. Assumpção, Fabrício Souza, Leandro Lacerda Campos, and 3 more authors

   In 2022 IEEE International Conference on Big Data (Big Data), 2022

   Abs arXiv Bib

   Money laundering has become one of the most relevant criminal activities in modern societies, as it causes massive financial losses for governments, banks and other institutions. Detecting such activities is among the top priorities when it comes to financial analysis, but current approaches are often costly and labor intensive partly due to the sheer amount of data to be analyzed. Hence, there is a growing need for automatic anti-money laundering systems to assist experts. In this work, we propose DELATOR, a novel framework for detecting money laundering activities based on graph neural networks that learn from large-scale temporal graphs. DELATOR provides an effective and efficient method for learning from heavily imbalanced graph data, by adapting concepts from the GraphSMOTE framework and incorporating elements of multi-task learning to obtain rich node embeddings for node classification. DELATOR outperforms all considered baselines, including an off-the-shelf solution from Amazon AWS by 23% with respect to AUC-ROC. We also conducted real experiments that led to the discovery of 7 new suspicious cases among the 50 analyzed ones, which have been reported to the authorities.

   @inproceedings{assumpcao2022delator,
     title = {{DELATOR}: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs},
     author = {Assump{\c{c}}\~{a}o, Henrique S. and Souza, Fabr{\'i}cio and Campos, Leandro Lacerda and Pires, Vin{\'i}cius T. de Castro and de Almeida, Paulo M. Laurentys and Murai, Fabricio},
     booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
     year = {2022},
     publisher = {IEEE},
     url = {https://ieeexplore.ieee.org/document/10021010},
   }