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Project details

Introduction

This research probes the integration of quantum computing architectures into AGI systems, focusing on OpenCog Hyperon framework’s metagraph-based reasoning capabilities. Our investigations span both currently deployable quantum computing technologies–trapped-ion, superconducting, and photonic quantum architectures–and theoretical models, notably topological quantum computing, which proposes using non-Abelian anyons to construct qubits with inherent error resistance. Each architecture will be analyzed for AGI utility based on its physical principles, qubit coherence properties, decoherence rates, and gate fidelities, establishing baselines for their applicability in probabilistic, recursive, and high-complexity AGI computations. 

The Team

Our team is composed of researchers from MIT, Wolfram Research, and Cornell University, primarily focused on graph theory, transformer-based machine learning, and quantum computing. Previous works include creating a NMR-field quantum computer, studying foundational consistency as it relates to the completeness and decidability of mathematical systems, and latent space generative machine learning.

Background

The analysis of trapped-ion systems will consider the linear arrangement of atomic ions manipulated in electromagnetic fields. Trapped ions offer excellent coherence times and entanglement fidelity due to ion-ion interactions via Coulomb forces, enabling quantum error correction protocols and stable multi-qubit operations necessary for AGI tasks involving prolonged data retention and complex interaction modeling. Superconducting systems, which employ Josephson junctions to create fast, low-decoherence qubits, will be studied for their capability to perform rapid gate operations–important for high-throughput AGI computations such as non-linear cognitive mapping and real-time probabilistic updates. Photonic quantum systems, which encode qubits as photons, are unique in their potential for high-speed data processing and near-error-free state transmission, making them relevant for AGI applications requiring instantaneous data transfer and complex graph traversal within the metagraph framework. Our analysis of these architectures will apply detailed metrics like coherence length, state collapse probability, cross-talk effects, and gate operation success rates to delineate each system's compatibility with computational models required for AGI.

Architectures

The analysis of physical quantum computing architectures will be a large portion of this project. In current literature, there are three prevailing architectures for quantum computation, mainly: trapped ion, superconducting, and photonic quantum computing [1-3]. On a high level, trapped ions offer long coherence times and high gate fidelity, using individual ions confined in electromagnetic fields as qubits [1]. On the other hand, superconducting quantum computing uses Josephson junctions to form qubits that operate at extremely low temperatures and are maintained by dilution refrigerators [2]. These systems achieve rapid gate operations, making them suitable for high-speed quantum processes. Platonic quantum computing encodes qubits in photons, allowing for qubit transmission over long distances with minimal error, a feature beneficial in distributed AGI frameworks [3].

Topological quantum computation is an additional point of contention, although theoretical. The architecture employs non-local anyonic statistics within a fault-tolerant framework; the topology is contained in a two-dimensional lattice (see: 2DEG) which theoretically reduces the fault-tolerance of the system. We will conduct simulations to assess topological qubits' fault-tolerance and decoherence suppression effects for continuous recursive computations and multi-level probabilistic reasoning intrinsic to AGI frameworks. Topological computing’s braid-group-based logic operations will be mapped to potential applications in recursive, probabilistic queries and self-organizing metagraphs, assessing their utility in cognitive operations necessitating persistent state retention under error-prone conditions [4-5].

OpenCog and AGI

In parallel, we will research quantum interactions with the OpenCog Hyperon framework’s metagraph structures, which encode hierarchical and associative knowledge. We will construct a mathematical model to analyze single and multi-qubit state superpositions, entanglement dynamics, and coherence decay within the metagraph’s relational graphs. Our research will use quantum gate formation, density matrix formalism, and stochastic quantum state diffusion modeling to ascertain how multi-qubit interactions influence AGI processes that demand high-dimensional non-linear inference, parallel state updates, and probabilistic self-modifying algorithms. Entanglement distributions across hierarchical metagraph nodes will be mapped to model how various degrees of entanglement and coherence impact knowledge representation and adaptive reasoning.

The collaborative work with OpenCog Hyperon’s MeTTa interpreter will develop algorithms optimized for quantum compatibility, enabling recursive and composable reasoning. The MeTTa interpreter, which is designed to handle flexible rule-based logic and self-referential updates, will be adapted to incorporate quantum-based probabilistic operators. This integration supports dynamic updates in knowledge representation updates, recursive query processing, and metagraph-based associative memory retrieval. Quantum algorithms such as Quantum Walks, Quantum Approximate Optimization Algorithms (QAOA), and the Variational Quantum Eigensolver (VQE) will be adapted to metagraph structures to optimize AGI tasks requiring rapid inference, non-deterministic polynomial-time query processing, and recursive reasoning [6-7]. Our study will further assess the feasibility of integrating these algorithms within MeTTa’s compositional framework, establishing whether quantum operations like superposition and entanglement can improve the interpreter’s symbolic processing and self-modification capacities in practical AGI applications. 

Impact

This research employs a methodological framework, incorporating direct experimental setups, quantum-classical hybrid simulations, and theoretical analyses to establish a rigorous comparison of quantum platforms within AGI contexts. Each quantum architecture will undergo extensive benchmarking in coherence time, gate fidelity, multi-qubit connectivity, and effective quantum volume. The research includes classical simulation of qubit interactions within metagraph data structures using entanglement entropy calculations, quantum noise models, and tensor network representations to simulate the impacts of quantum phenomena on complex, high-dimensional AGI reasoning processes. Hybrid quantum-classical approaches will allow for comprehensive modeling of entangled states in multi-node metagraphs and the effects of quantum inference on recursive AGI inference mechanisms.

The expected outcomes of this research include a technical delineation of quantum computing’s viable contributions to AGI, differentiating between applicable and speculative quantum-enhanced functions; if practical, we aim to implement a graph-based architecture to implement a quantum computer. Through our interactions with OpenCog Hyperon’s development team, we aim to produce algorithmic modifications and protocol recommendations for the MeTTa interpreter to incorporate quantum-driven reasoning improvements. This includes protocol modifications for quantum-assisted probability-based inference, engagement-supported recursive queries, and persistent quantum-state representation across large-scale metagraphs. This research establishes a foundation for integrating quantum-based computational advancements into AGI frameworks by comprehensively mapping quantum computation's theoretical and practical compatibility with AGI. 

Sources

[1] Schwerdt, David, Lee Peleg, Yotam Shapira, Nadav Priel, Yanay Florshaim, Avram Gross, Ayelet Zalic, et al. “Scalable Architecture for Trapped-Ion Quantum Computing Using RF Traps and Dynamic Optical Potentials.” Physical Review X 14, no. 4 (October 21, 2024): 041017. https://doi.org/10.1103/PhysRevX.14.041017.

[2] Arute, Frank, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, et al. “Quantum Supremacy Using a Programmable Superconducting Processor.” Nature 574, no. 7779 (October 24, 2019): 505–10. https://doi.org/10.1038/s41586-019-1666-5.

[3] Romero, Jacquiline, and Gerard Milburn. “Photonic Quantum Computing.” arXiv, 2024. https://doi.org/10.48550/ARXIV.2404.03367.

[4] Iqbal, Mohsin, Nathanan Tantivasadakarn, Ruben Verresen, Sara L. Campbell, Joan M. Dreiling, Caroline Figgatt, John P. Gaebler, et al. “Non-Abelian Topological Order and Anyons on a Trapped-Ion Processor.” Nature 626, no. 7999 (February 15, 2024): 505–11. https://doi.org/10.1038/s41586-023-06934-4.

[5] Struski, Łukasz, Tomasz Danel, Marek Śmieja, Jacek Tabor, and Bartosz Zieliński. “SONG: Self-Organizing Neural Graphs.” arXiv, July 28, 2021. https://doi.org/10.48550/arXiv.2107.13214.

[6] Blekos, Kostas, Dean Brand, Andrea Ceschini, Chiao-Hui Chou, Rui-Hao Li, Komal Pandya, and Alessandro Summer. “A Review on Quantum Approximate Optimization Algorithm and Its Variants.” Physics Reports 1068 (June 2024): 1–66. https://doi.org/10.1016/j.physrep.2024.03.002.

[7] Tilly, Jules, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, et al. “The Variational Quantum Eigensolver: A Review of Methods and Best Practices,” 2021. https://doi.org/10.48550/ARXIV.2111.05176.

Open Source Licensing

Public use.