simuliinc

Project Owner

Entangled Intelligence: Quantum Computing in AGI

Expert Rating

n/a

Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Review of quantum computing technologies

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Overview

By exploring this convergence between quantum and AGI, we glimpse the universe striving to understand its own infinite mind. AGI represents one of computing's most profound challenges, requiring scalable, efficient methods for differentiation, optimization, and information retrieval. Quantum computing (QC) offers a revolutionary approach to these compute-intensive processes, but separating hype from potential demands rigorous evaluation. This work examines three major QC paradigms—gate-based computing, quantum annealing, and measurement-based quantum computing—assessing their ability to accelerate AGI using tools like ZX calculus for cross-paradigm translation.

RFP Guidelines

Review of quantum computing technologies

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $80,000 USD
Proposals 10
Awarded Projects n/a

SingularityNET

Oct. 4, 2024

This RFP seeks to critically evaluate the role of quantum computing in advancing Artificial General Intelligence (AGI). The goal is to distinguish between realistic capabilities and hype, providing clear insights into the practical benefits and limitations of quantum computing for AGI architectures, particularly within the OpenCog Hyperon framework. Part of this should involve interacting with the Hyperon team who've built the existing and in-development MeTTa interpreters.

Proposal Description

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Total Milestones
4
Total Budget
$80,000_USD
Last Updated
8 Dec 2024

Milestone 1 - Quantum Computing Paradigm Analysis

Description

Develop comprehensive research framework and conduct initial analysis of quantum computing paradigms relevant to AGI development focusing on gates annealers and hybrid systems. This phase establishes the theoretical foundation and analytical approach for the entire project.

Deliverables

Detailed research plan and methodology document Initial analysis of quantum computing paradigms Review of AI and AGI technical challenges Review of quantum computing paradigms Analysis of intersection between technical challenges and opportunities in AI and quantum Review of current AI quantum computing and near term capabilities Review of pseudo-quantum approach of Hyperdimensional Computing Projections on quantum AI computing roadmap

Budget

$20,000 USD

Success Criterion

Analysis covers at least 3 major quantum paradigms Minimum of 50 peer-reviewed sources identified Research methodology approved by Hyperon team Clear framework for distinguishing hype from reality established Submission to peer-reviewed journal

Milestone 2 - Technical Feasibility Assessment

Description

Deep technical evaluation of how quantum computing approaches can address specific AGI computational challenges with focus on practical implementation within the Hyperon framework through collaborative analysis with the development team.

Deliverables

Review of quantum technical capabilities and fundamentals Review of AGI/AI technical capabilities and fundamentals Analysis of existing AI quantum compute and feasible solutions Analysis of NISQC and FTQC paradigms in relation to ML/DL tasks Review of quantum-classical hybrid solutions in AI Projected quantum AI roadmap Detailed integration scenarios document Resource requirement analysis Scalability assessment report Updated technical findings documentation Refined use case specifications

Budget

$20,000 USD

Success Criterion

Complete analysis of hybrid quantum-classical approaches Resource requirements quantified for each scenario Integration pathways validated by technical team Clear differentiation between near-term vs future benefits

Milestone 3 - Integration Planning & Analysis

Description

Comprehensive analysis of integration pathways for quantum computing within AGI systems focusing on hybrid approaches and practical implementation strategies that address current technological limitations while preparing for future capabilities.

Deliverables

Review of current Hyperon bottlenecks Initial mapping of quantum approaches to AGI tasks Technical analysis report of MeTTa/Hyperon architecture Performance bottleneck analysis Quantum advantage calculations using ZX-calculus Initial use case specifications Preliminary integration assessment Design of Hyperon/MeTTa quantum integrations Roadmap of integrating quantum compute into SingularityNet

Budget

$20,000 USD

Success Criterion

Identification of at least 3 concrete use cases ZX-calculus analysis completed for key operations Technical review completed by Hyperon team Clear quantification of potential quantum advantages

Milestone 4 - Final Research Synthesis

Description

Final synthesis of all research findings technical analyses and integration planning into a comprehensive roadmap for quantum computing integration in AGI development with clear recommendations for immediate and future actions.

Deliverables

Final research report with actionable recommendations Integration roadmap for Hyperon framework Future research recommendations Presentation of findings to stakeholders Complete technical documentation package

Budget

$20,000 USD

Success Criterion

All claims supported by peer-reviewed sources Clear recommendations for at least 3 implementation paths Roadmap approved by Hyperon team Comprehensive hype vs. reality assessment completed

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The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.

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This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).

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This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.

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This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

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Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

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This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”

For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
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A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

simuliinc

Project Owner

Co-Principal Investigator with expertise in AGI systems and pseudo-quantum computing, specializing in Hyperon and hyperdimensional computing implementations.

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Del Lucent

PI bringing expertise in computational physics, quantum mechanics education, and distributed computing. Stanford PhD in biophysics with focus on complex systems and teaching quantum computing.

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