Company Name (if applicable)

Project details

Achieving practical quantum acceleration of AGI demands a deep, multidisciplinary assessment of current and near‑term quantum hardware. Our team—Omowuyi Olajide (IC design, neuromorphic, and quantum engineer), Gabriel Axel Montes (neuroscience‑AI specialist and early SingularityNET team member), award-winning quantum scientist Lucija Grba, multiple Nature-published Gert Cauwenberghs (ultra-low power integrated circuits and Quantum-inspired hardware expert), and Theo Valich (HPC & modular data‑center visionary and advisor) will collaborate to:

1. Literature Review & Comparative Analysis

• Survey trapped‑ion (e.g., IonQ H-series), superconducting (e.g., IBM Eagle, Google Sycamore), photonic (e.g., Xanadu Borealis), and nascent topological qubit platforms (e.g., Microsoft’s Majorana efforts).
  
  

• Catalog key metrics—coherence times (T₁, T₂), native gate sets, two‑qubit fidelities, connectivity graphs—into a queryable knowledge graph aligned with SingularityNET’s Knowledge Layer.
  
  

2. Quantum‑Classical Hybrid Simulation

• Build end‑to‑end pipelines coupling quantum subroutines (QAOA for combinatorial reasoning, VQE for pattern classification, Quantum Walks for graph traversal) with Hyperon’s probabilistic logic (PLN), evolutionary learning (MOSES), and attention networks (ECAN).
  
  

• Benchmark on representative AGI micro‑tasks: knowledge‑graph inference, large‑scale pattern matching, and real‑time decision loops.
  
  

3. Practical Feasibility & Roadmapping

• Execute small‑scale experiments on cloud‑accessible quantum hardware to measure real‑world gate performance and noise resilience.
  
  

• Assess scalability (qubit counts, error‑correction overhead), energy‑efficiency trade‑offs, and cost models versus classical HPC alternatives.
  
  

• Produce a “traffic‑light” feasibility matrix highlighting immediate opportunities, mid‑term prospects, and speculative avenues.
  
  

4. Integration & Recommendations

• Align quantum hardware capabilities with Hyperon’s modular requirements, recommending specific platform‑component pairings (e.g., trapped‑ion for high‑fidelity inference kernels, photonics for low‑latency knowledge searches).
  
  

• Propose compiler and toolchain adaptations for seamless Hyperon–quantum co‑processing.
  
  

• Deliver strategic guidance on next steps for quantum hardware adoption in AGI research.

The outcome will be an authoritative, actionable roadmap—grounded in peer‑reviewed research, hands‑on simulation data, and experimental benchmarks—guiding SingularityNET and OpenCog Hyperon toward quantum‑accelerated AGI.

To underscore how our structured workflow translates into tangible impact for SingularityNET and the wider open-source AGI community, we embed every analytic step in a shared Knowledge Layer. Each literature-derived metric, cloud experiment, and hybrid benchmark is ingested as a verifiable triple, giving Hyperon developers a living map of quantum strengths and limitations that updates as new data arrive. Because the same pipeline is reused for simulation and on-hardware execution, insights propagate seamlessly from raw qubit physics to high-level AGI reasoning modules—eliminating the “last-mile” gap that often separates academic quantum studies from production AI systems.

Beyond the final feasibility report, all prototype code, integration scripts, and annotated datasets will be released under open licence with accompanying documentation. This ensures that Hyperon contributors, external research groups, and future projects can reuse our artefacts without starting from scratch, accelerating community innovation while maintaining scientific rigour.

Table: Mapping Candidate Hardware Paradigms

*Capabilities map directly to the coherence, fidelity, error-correction overhead, and throughput metrics specified in the proposal.

About the Team

Omowuyi Olajide, PhD(c)

IC Design and Neuromorphic computing engineer
LinkedIn | Scholar

Omowuyi Olajide is a distinguished integrated circuit designer and neuromorphic engineer whose decade‑long career bridges VLSI hardware, mixed‑signal circuits, quantum computing, and brain‑inspired computing to realize ultra‑efficient AI accelerators. As a faculty affiliate in the Dept. of Bioengineering and the Institute for Neural Computation at UC San Diego, he co‑authored with Prof. Cauwenberghs the 2025 Nature Communications paper "ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers", and the IEEE BioCAS 2023 paper “Reconfigurable Event‑Driven Spiking Neuromorphic Computing near High‑Bandwidth Memory” (DOI 10.1109/BIOCAS58349.2023.10388692), demonstrating seamless integration of spiking SNN cores with HBM for sub‑microsecond, low‑power inference. His pioneering work on non‑binary low‑density parity‑check decoders—published in the Computer Engineering & Applications Journal—advanced error‑correction throughput in VLSI by over 50 %, showcasing his mastery of mixed‑signal design and coding theory. Olajide’s expertise extends to quantum‑inspired compute models, race logic prototyping on FPGA fabrics, and end‑to‑end analog in‑memory computing simulations using emerging memristor technologies, all complemented by his adeptness in Python‑ and MATLAB‑based benchmarking frameworks. His unique blend of hands‑on hardware development, theoretical rigor, and cross‑disciplinary fluency in AI algorithms positions him to spearhead our proof‑of‑concept implementations and ensure that our AGI hardware paradigms not only meet but exceed the stringent performance, energy‑efficiency, and scalability targets of this proposal.

Prof. Gert Cauwenberghs – Advisor

Prof. of Bioengineering, UCSD. Previously Prof. of Electrical and Computer Engineering, Johns Hopkins University.
Co-director, UCSD Institute of Neural Computation.
Director, UCSD Integrated Systems Neuroengineering Lab.

Specialties: Micropower mixed-signal VLSI circuits and systems, bioinstrumentation, neuron-silicon interfaces, brain-computer interfaces, large-scale neural computation.

LinkedIn | Scholar

Prof. Gert Cauwenberghs is a world‐renowned pioneer in neuromorphic engineering and adaptive circuit design, whose 35‑year career at the intersection of neuroscience and microelectronics has yielded foundational advances in energy‑efficient silicon implementations of synaptic plasticity. As the founding director of the UCSD Institute for Neural Computation, he has led the development of massively parallel, mixed‑signal microcircuits that emulate the structure and function of biological neural networks—most recently demonstrating on‑chip synaptic arrays for template‑based visual pattern recognition operating at less than one femtojoule per synaptic event, surpassing the nominal energy efficiency of the human brain. His seminal work on embedding dynamic learning rules directly into CMOS devices has informed the design of race logic architectures, analog in‑memory computing cells, and asynchronous spiking neural processors, making him uniquely qualified to guide our exploration of next‑generation AGI hardware paradigms. With over 200 peer‑reviewed publications in top venues including Nature Electronics, IEEE Transactions on Neural Networks and Learning Systems, and ISCA, and a track record of successful technology transfer to industry, Prof. Cauwenberghs brings unparalleled expertise in co‑designing algorithms and hardware, evaluating fabrication constraints, and delivering manufacturable, high‑performance neuromorphic systems that align precisely with the objectives of this proposal.

Lucija Grba, PhD(c)
Quantum scientist | MSc Quantum Science & Technology (Distinction) | BSc Mathematics
LinkedIn

Formerly a Quantum Algorithm Engineer at Quandela, Lucija helped build Perceval, the firm’s flagship toolkit for programming photonic quantum processors. Her work centred on designing and tuning algorithms for Quadratic Unconstrained Binary Optimisation (QUBO) with real-world use cases in finance and logistics, produced in close collaboration with Quandela’s theory and R&D groups. Awarded the Microsoft Ireland Scholarship while completing her MSc at Trinity College Dublin, Lucija has also written outreach pieces such as “Budgeting with Qubits: Make the Most of Your Photons,” showcasing her knack for making advanced photonic concepts accessible to non-specialists. In mid-2025 she will begin a funded PhD in Quantum Computing at the University of New South Wales, backed by a competitive Sydney Quantum Academy Partnership Scholarship. Her research—conducted jointly with silicon-spin pioneer Diraq and Australia’s CSIRO—will explore algorithm–hardware co-design for large-scale, fault-tolerant quantum systems.

Across industry and academia, Lucija leverages a blend of mathematical rigour, software engineering, and clear communication to push quantum technologies toward practical impact.

Gabriel Axel Montes, PhD
CEO & Founder, Neural Axis | R&D/Product, TrueAGI
LinkedIn | Scholar

Neuroscientist and AI entrepreneur; early SingularityNET team, 0-to-1 builder for neurotech, VR, robotics and blockchain startups. Co-author of The Consciousness Explosion (AI & consciousness). 10,000+ hours contemplative practice inform creative cognitive science approaches. Track record of project execution, and liaison with Hyperon.

Theo Valich – Advisor
CEO & Founder, Ecoblox (sustainable modular data centers)
LinkedIn

HPC and data-center veteran; architected several national supercomputers and carbon-aware routing protocols. Deep expertise in GPU/CPU road-maps, market trends and TCO modeling. Advises on deployment feasibility and industry alignment.

Open Source Licensing

The proposed license model is as follows:

• For SingularityNET Foundation: MIT License with Commons Clause:

• The work will be provided to SingularityNET Foundation under the terms of the MIT License, with the following modification:
  - The entity may not sell the work (or a modified or derivative aspect/version of it), or offer it for sale, unless the entity has a separate commercial license from the copyright holder.

• For all other non-SingularityNET Foundation entities: Proprietary

This is not a strict criterion of the proposal, but is the proposed license model for the time being.

Links and references

Olajide, Cauwenbaughs et al. "ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers". (Nature Communications, 2025)

Cauwenbaughs. "A compute-in-memory chip based on resistive random-access memory."
(Nature, 2022) 

Cauwenberghs. "The neurobench framework for benchmarking neuromorphic computing algorithms and systems". 
(Nature Communications, 2025)

Cauwenberghs. "Neuromorphic Computing at Scale".
(Nature, 2025)

Google Scholar:

Gert Cauwenberghs

Omowuyi Olajide

Gabriel Axel Montes