Our Team

Prasad Kumkar — Lead Researcher
• 6 years in Web3 R&D and protocol design
• IBM Qiskit Excellence Certificate
• Passionate about translating theory into practical experiments

Siva Kumar — Quantum Researcher
• Academic background in cryptography and blockchain
• Contributor to hybrid simulation frameworks
• Committed to rigorous empirical validation

Company Name (if applicable)

Project details

Artificial General Intelligence (AGI) frameworks such as OpenCog Hyperon rely on symbolic reasoning (Probabilistic Logic Networks – PLN), economic attention mechanisms (Economic Attention Networks – ECAN), and large-scale knowledge storage and retrieval (Distributed Atomspace – DAS). As these subsystems scale, they face combinatorial explosion: PLN’s inference paths grow super-exponentially; ECAN’s resource allocation is NP-hard; DAS’s graph searches encounter millions of nodes. Simultaneously, quantum computing hardware has reached a critical inflection point: state-of-the-art platforms (superconducting, trapped-ion, photonic, and early topological qubits) now support dozens to hundreds of qubits, with gate fidelities approaching error-correction thresholds. Yet, the field lacks a rigorous, empirical evaluation of how quantum paradigms can accelerate or transform AGI workloads.

Q-Sage - Quantum Architectures for Scalable AGI Evaluation—bridges this gap. We will systematically map core AGI subsystems (PLN, ECAN, DAS) to quantum algorithms (quantum walks, QAOA, VQE), implement hybrid quantum-classical prototypes, benchmark them on real hardware (IBM Q, IonQ, Rigetti, D-Wave, Xanadu), and deliver a reproducible, open-source SDK and a comprehensive feasibility matrix. By the end of the project, the AGI community will have actionable insights into which quantum approaches can meaningfully enhance reasoning, optimization, and memory tasks today, which require near-term advances, and which remain speculative until long-term breakthroughs (e.g., topological qubits) materialize.

We propose Q-Sage, a four-phase research program:

1. Architecture Survey: tabulate specs of superconducting, trapped-ion, photonic, and topological qubit platforms.

2. Algorithmic Mapping: reduce PLN, ECAN, DAS tasks to quantum routines (quantum walks, QAOA, VQE).

3. Hybrid Simulation & Benchmarking: implement and run circuits in Qiskit/Cirq/PennyLane, execute on IBM Q, IonQ, Rigetti, D-Wave with real noise models.

4. Feasibility & Roadmap: publish a multi-metric feasibility matrix, cost-benefit analysis, integration guidelines, and an open-source Python SDK.

Research Objectives

• O1: Formal Reduction of AGI Subsystems to Quantum Routines.

  • Translate PLN’s logical inference into quantum walk search problems and amplitude-amplification circuits.

  • Express ECAN’s attention allocation as QUBO (Quadratic Unconstrained Binary Optimization) instances for QAOA or as Ising models for quantum annealing.

  • Model DAS’s pattern matching and subgraph isomorphism queries as quantum associative memory or continuous-time quantum walk on large graphs.

• O2: Hybrid Simulation & Real-Device Benchmarking.

  • Develop a Python-based SDK (Q-Sage SDK) integrating Qiskit, Cirq, and PennyLane for unified circuit construction and parameter management.

  • Execute hybrid variational algorithms (VQE, QAOA) and circuit-based searches on simulators with realistic noise models, then on actual cloud quantum backends (IBM Q Experience, AWS Braket for IonQ/Rigetti/Xanadu, D-Wave Leap).

• O3: Comparative Performance Analysis & Feasibility Framework.

  • Quantify resource trade-offs: qubit counts, gate depth, coherence requirements, circuit error-rates, and required error-correction overhead for each mapping.

  • Produce heatmaps and multi-dimensional performance plots comparing classical vs. quantum runtimes, quality of solutions (e.g., inference accuracy, optimization approximation ratio), and cost-benefit metrics.

• O4: Community-Driven Open Artifacts & Integration Roadmap.

  • Release the Q-Sage SDK under MIT License, complete with Jupyter notebooks, CI/CD pipelines, and documentation.

  • Publish the Quantum AGI Feasibility Matrix summarizing viability by AGI module, quantum platform, and problem size.

  • Provide detailed guidelines for integrating quantum co-processors into future OpenCog deployments, including recommended hybrid architectures and error-mitigation strategies.

Technical Approach

1. Formal Task Mappings

1. PLN → Quantum Walks & Amplitude Amplification

   • Represent the Atomspace’s hypergraph (nodes = concepts, hyperedges = relations) as the vertex set of a quantum walk.

   • Implement discrete-time quantum walk operators ($U=S\cdot C$, where CC is a coin operator and SS a shift) to explore inference paths in superposition.

   • Use Grover-style amplitude amplification to boost the probability of measuring states corresponding to valid inference chains.

2. ECAN → QAOA & Annealing

   • Formulate attention allocation as a binary vector x∈{0,1}nx∈{0,1}n, where $xi=1$ if resource is assigned to process ii.

   • Encode constraints (budget, mutual exclusivity) and reward functions into a cost Hamiltonian HC(x)HC​(x).

   • Apply QAOA circuits with pp alternating layers of problem and mixer Hamiltonians (e−iγjHCe−iβjHMe−iγj​HC​e−iβj​HM​), optimizing {γj,βj}{γj​,βj​} via classical outer-loop.

   • Benchmark on IonQ (full connectivity) for small $n\le 20$ and on D-Wave annealer for larger $n\le 200$.

3. DAS → Quantum Associative Memory & Quantum Walk Search

   • Use variational quantum classifiers (ansatz circuits) to implement associative memory: encode each known subgraph pattern into a quantum state and measure overlap with query states.

   • Implement continuous-time quantum walks under Hamiltonian $H=A$ (adjacency matrix) to exploit natural diffusion dynamics on the Atomspace graph; measure first-pass hitting times for marked subgraphs.

2. Hybrid Simulation & Execution

• SDK Architecture:

  • Core modules for circuit generation (QASM translation), noise modeling (Kraus channels, readout error), and result aggregation.

  • Support for multiple backends via adapter pattern: QuantumBackend{IBMQ, IonQ, Rigetti, DWave, Xanadu}.

  • Parameter server for variational algorithms, caching of intermediate measurement statistics, and performance logging.

• Noise Modeling & Error Mitigation:

  • Calibrate device noise channels using provider-supplied tomography data.

  • Apply zero-noise extrapolation (ZNE) and probabilistic error cancellation (PEC) to improve measurement fidelity for critical runs.

  • Compare mitigated vs. unmitigated results to quantify real-world accuracy.

• Benchmark Suite:

  • Inference Benchmark: Randomly generated logic queries of varying depth (2–6 inference hops) on synthetic Atomspaces (|V|=64 to 256).

  • Optimization Benchmark: Standard Max-Cut and custom ECAN instances (n=10–30) for QAOA on gate-model devices; larger $n\le 200$ on D-Wave.

  • Memory Benchmark: Graph pattern search tasks using continuous-time walks on graphs with 100–1000 nodes, executed on photonic simulators and IBM superconducting devices.

Q-Sage delivers the first end-to-end, empirically grounded framework for mapping core AGI workloads (PLN inference, ECAN attention, DAS memory) onto quantum hardware. By providing open-source hybrid simulation tools, real-device benchmarks, and a feasibility matrix, we enable AGI researchers to make data-driven decisions about quantum co-processor integration.

Open Source Licensing

Links and references

• Proposal Pitch Deck - https://drive.google.com/file/d/15pc1xusS_-emjGTJW1GGrO0pzBJbIQSC/view?usp=sharing
• Research Paper on comparitive analysis of various VRF methods - https://drive.google.com/file/d/1z3e5hPWbs5BdeusJrQ1uwemt2HCGukQW/view
• Certificate of Quantum Excellence - https://drive.google.com/file/d/1KaF-IzJZYF68QW56UNrO1rDuvade8Z5u/view