Our Team

Our team is composed of highly qualified scientists with advanced degrees in physics, working on computer science, engineering, mathematics, finance and related fields. Our members bring a strong record of academic excellence, international research experience, and interdisciplinary collaboration. Our expertise spans data science and analytics, quantum computing and quantum technologies, complex systems modeling, financial markets, distributed decision-making and blockchain technology.

Company Name (if applicable)

Project details

1 INTRODUCTION

Recent advances in quantum computing have revitalized interest in its potential to accelerate the development of Artificial General Intelligence (AGI). Four quantum architectures are in the spotlight for paramount near-future applications: trapped-ion, superconducting, photonic, and topological quantum computing. This investigation examines how these four architectures will either meet or fail to meet the computational demands presented by AGI systems, particularly within the advancing OpenCog Hyperon framework.

Artificial General Intelligence is a field of AI research that works on developing a software framework that can expand and assign itself to handle general tasks, with the ability to self-teach, exhibit human-like cleverness, and manifest autonomous self-control with a proper degree of self-understanding. Although AGI with human abilities remains a theoretical concept and research goal, the looming quantum computing revolution can offer fundamentally unforeseeable forms of logic, memory, and learning at scales, forms, and speeds unreachable by classical systems, potentially enabling breakthroughs that allow the realization of transformative AGI architectures.

Each quantum computing technology offers specialized challenges in parallel with singular advantages that may contribute uniquely to quantum information processing, which might redefine processing frontiers and impact the future of AGI:

1. Trapped-ion systems offer high-fidelity operations and long coherence times but lack scalability [1, 2].

2. Superconducting circuits provide fast gate speeds and are currently the most mature in terms of industrial development yet remain limited by relatively short coherence times and significant error rates [3, 4].

3. Photonic platforms leverage light-based qubits and promise high-speed, room-temperature operations and seamless integration with classical communication technologies, though they face challenges in deterministic two-qubit gates [5, 6].

4. Topological quantum computing aims to provide error protection by encoding quantum information in quasiparticles. These exotic states of matter are expected to be highly resistant to local noise and decoherence, potentially enabling scalable and robust quantum processors [7, 8].

However, the quantum computing’s actual applicability to AGI workloads, such as probabilistic reasoning, pattern recognition, and symbolic manipulation, as well as their comparative advantages over classical computing, remains inadequately explored.

2 OBJECTIVES

2.1 Main Objective

Assess the realistic potential of quantum computing technologies for advancing AGI within the OpenCog Hyperon framework and future research directions through a comprehensive literature review and computer simulation analysis.

2.2 Specific Objectives

Our specific objectives focus on advancing quantum computing research related to AGI-related tasks, emphasizing evaluating architectures, modeling key quantum phenomena, and assessing practical feasibility for real-world intelligent systems.

• Examine leading quantum computing paradigms: trapped-ion, superconducting, photonic, and topological architectures.

• Investigate core quantum properties: multi-qubit interactions, entanglement stability and error correction.

• Evaluate practical limitations: decoherence, scalability challenges, and quantum gate fidelities.

• Develop and test computer simulations to assess the applicability of quantum computing.

• Promote the “Open Quantum Intelligence” event to share results and engage scientific and technological communities.

2.3 Outcomes

Our research begins with a survey of foundational materials of Artificial General Intelligence (AGI) and the OpenCog Hyperon frameworks, including the probabilistic logic networks (PLN), the economic attention allocation (ECAN), and the evolutionary program learning (MOSES). By analyzing how these components function in standard implementations, we aim to identify opportunities amenable to quantum acceleration. The team will carry out a thorough assessment of quantum computing’s strengths and limitations in relation to AGI based on peer-reviewed sources and experimental data, establishing an essential literature and simulation data for evaluating which theoretical frameworks can be meaningfully addressed in quantum-native or hybrid quantum-classical formulations.

Benchmarks. We focus on three main aspects as potential benchmarks: probabilistic reasoning, parallel processing, and large-scale knowledge representation. Some flexibility is reserved to allow deeper investigation into any of these areas should particularly promising or unexpected results emerge during the project. A comparative evaluation of different quantum computing architectures based on key performance metrics including (A) Coherence length, or the spacetime over which a qubit maintains its quantum state without decoherence; (B) Entanglement robustness, or the resilience of entangled qubit pairs or systems in the presence of noise; and (C) Computational throughput, defined as the rate at which quantum gate operations can be executed and measured across a multi-qubit system.

Simulation Data. We will experiment with open-source software frameworks designed to build, simulate, and run quantum algorithms that relate to AGI-related reasoning, such as Qiskit, Cirq, PennyLane, and QuTiP. Our goal is to simulate and assess the feasibility of applying quantum-enhanced algorithms to probabilistic computing essential tasks AGI by exploring three key algorithms: Quantum Walks, for modeling probabilistic transitions across semantic or knowledge graphs; Quantum Approximate Optimization Algorithm (QAOA), for solving combinatorial problems relevant to cognitive planning and program evolution; and Variational Quantum Eigensolver (VQE), for identifying low-energy or stable configurations in reasoning or belief networks. The simulation data will allow us to benchmark performance across different quantum architectures and provide experimental feasibility on AGI-related tasks. Comparative classical-quantum analysis will be entangled with scalability and cost-beneficial evaluation to address and estimate the economic feasibility of quantum computing in AGI development, laying the foundations of a strategic road map for integrating quantum technologies into AGI evolution.

Core Elements. Our quantum computing exploration for the AGI development proposal presents three main core elements:

1. Professional research: an experienced scientific team with prior publications in quantum computing and information research, distributed decision-making, complex systems, and AGI-related fields.
2. A Reviewer team: tasked to follow and conduct independent critical reviews of research progress.
3. A high-impact AGI outreach component, the “Open Quantum Intelligence” symposium: we conclude our investigation with a public event to showcase research results, tools, and potential applications in AGI.

Research results, including methodologies, datasets, and code, will be rigorously documented, revised and delivered to the Deepfunding Team and in open-access repositories (GitHub and/or Arxiv).

2.4 Team Background & Experience

Our physics team brings together a group of researchers with extensive expertise in quantum computing, statistical physics, complex systems, data science and analytics, computer simulations and artificial intelligence. Our prior works include high-impact journals, peer-reviewed publications, conference presentations, several national and international awards and successful coordination of interdisciplinary research efforts. Combined with experience in corporate R&D environments, such as SingularityNET, Cardano and IOTA, the team’s capacity positions us uniquely to execute the proposed project with both scientific rigor and applied relevance.

1. Dr. André L. M. Vilela (PhD)
2. Dr. Gabriel Dias Carvalho (PhD)
3. Igor V. G. Oliveira (MSc)
4. Mateus F. B. Granha (MSc)
5. Caio B. L. Silva (BSc)

3 BUDGET DETAILS

The proposed budget supports the successful execution of all project phases, from theoretical analysis, quantum modeling, simulation, benchmarking, and public outreach. It includes allocations for dedicated research time, access to quantum computing platforms, software development, simulation resources, and outreach participation in relevant events for knowledge exchange. Overhead costs represent the essential institutional resources critical for sustaining the operational environment necessary for high-quality scientific research. Our financial plan emphasizes cost-efficiency while ensuring that the technical depth and collaborative scope of the project are fully subsidized.

3.1 Research Team (featuring the Red Team): $7,430 USD / month

The team comprises physics researchers with high levels of academic training. The total monthly cost of personnel and operations is $7,430 United States Dollars (USD), including $1,230 USD in overhead costs, institutional infrastructure, administrative services, and compliance.

3.2 Quantum Computing Access: $400 USD / month

Essential to run and benchmark quantum algorithms on real hardware via platforms, enabling the team to evaluate performance under realistic noise and decoherence conditions, critical to AGI feasibility.

3.3 Scientific Outreach: $250 USD / month

Our team will host one concluding public event: the “Open Quantum Intelligence” to showcase developed tools, research results, and potential applications in AGI.

The total investment in 6 months: $48,480.00 USD.

4 REFERENCES

[1] M. A. Nielsen and I. L. Chuang, Cambridge Univ. Press, 10th Anniv. Ed., 2010.

[2] K. R. Brown, J. Kim, C. Monroe, npj Quantum Inf., 2, 2016.

[3] G. Wendin, Rep. Prog. Phys., 80(10), 2017.

[4] J. M. Chow, J. M. Gambetta, M. Steffen, npj Quantum Inf., 3(2), 2017.

[5] J. L. O’Brien, A. Furusawa, J. Vučković, Nat. Photonics, 3, 2009.

[6] S. Slussarenko, G. J. Pryde, Appl. Phys. Rev., 6, 2019.

[7] A. Stern, N. H. Lindner, Science, 339, 2013.

[8] T. D. Stanescu, CRC Press, 2nd Ed., 2024.

Open Source Licensing

Links and references

FromScience.io :: Official Website

fromscience.io

FromScience.io :: Scientific Team

fromscience.io/researchteam

FromScience.io :: Research Publications

fromscience.io/publications/

ORCiD Profiles

1. Dr. André L. M. Vilela (PhD)
2. Dr. Gabriel Dias Carvalho (PhD)
3. Igor V. G. Oliveira (MSc)
4. Mateus F. B. Granha (MSc)
5. Caio B. L. Silva (BSc)

Detailed Project

https://fromscience.io/publications/FromScienceQuantumIntelligenceApplications.pdf