Quantum AGI: Causality, Contextuality, Complexity

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Elija Perrier
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Quantum AGI: Causality, Contextuality, Complexity

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Overview

Our proposal advances the theoretical and experimental frontiers of quantum artificial general intelligence (QAGI). We will building on prior work from the previous Deep Funding round on quantum-native AGI (3 papers under review and open-source simulation in testing) in quantum ontology, simulation, and AGI-theoretic frameworks, we investigate how quantum causality, contextuality, thermodynamics, and complexity theory constrain or enhance AGI development. This stage formalises new theorems, tests hypotheses on real quantum devices, and develops a quantum control–based framework for analysing agentic cognition across distributed, error-corrected quantum systems.

RFP Guidelines

Explore theoretical quantum computing models

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $100,000 USD
  • Proposals 12
  • Awarded Projects 1
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SingularityNET
Apr. 14, 2025

This RFP seeks a technical and experimental assessment of quantum computing architectures in AGI applications. Proposals should explore the practicality and limitations of various quantum approaches — including trapped-ion, superconducting, photonic, and topological quantum computing — in handling probabilistic reasoning, parallel processing, and large-scale knowledge representation. The research could include quantum-classical hybrid simulations and feasibility studies for applying quantum advancements to AGI workloads. Bids are expected to range from $20,000 - $100,000.

Proposal Description

Project details

Quantum AGI: Causality, Complexity, Contextuality

Executive Summary

Building upon our DF-funded first phase of research into native Quantum Artificial General Intelligence (QAGI), we propose to extend our investigation into four critical domains that will provide both theoretical foundations and practical implementations for QAGI. Our team, comprising experts in quantum machine learning and AGI formerly of Stanford/UTS and ANU with publication records across leading journals such as Nature, IOP, JAGI will focus in this second phase on:

  1. Quantum causality and contextuality implications for QAGI and hardware implementations
  2. Quantum thermodynamic implications for AGI
  3. Quantum complexity and query complexity considerations
  4. Quantum error correction consequences for AGI

Our approach will combine theoretical advances with empirical validation through enhanced simulations and testing on real quantum devices through partnerships with quantum computing laboratories. We will use quantum control theory as an integrative framework to synthesize these distinct but interconnected research areas. 

Research Progress and Current Status

Since receiving initial funding, our team has made significant progress in establishing fundamental theoretical frameworks for quantum AGI:

  • Completed a comprehensive taxonomy of classical-quantum distinctions in AGI implementation, categorizing approaches based on algorithmic design (classical vs. quantum) and hardware implementation (classical vs. quantum)

  • Developed initial simulations testing different computational substrates for AGI implementation, focusing on how quantum properties affect core AGI subroutines

  • Produced three papers (currently under review at AGI-2025):

    1. "Quantum Foundations for QAGI" - Establishes the theoretical underpinnings for truly quantum-native AGI
    2. "Quantum AIXI: Extending Universal Intelligence to Quantum Domains" - Adapts the AIXI framework to quantum environments and capabilities
    3. "Semantic/Circuit Hamiltonian Comparison for Classical and Quantum Algorithmic Protocols" - Introduces novel mathematical methods to directly compare classical and quantum approaches

These accomplishments establish a solid foundation for the next phase of our research program, which will focus on quantum causality, complexity and contextuality implications for AGI.

Research Plan

1. Quantum Causality and Contextuality

Quantum systems exhibit indefinite causal structures (Oreshkov et al., 2012) and contextual behaviors (Kochen & Specker, 1975) that transcend classical logic constraints. These phenomena represent untapped computational resources for AGI, potentially enabling reasoning paradigms impossible within classical frameworks. Experimental demonstrations by Rubino et al. (2017) show quantum circuits can implement computations without fixed causal order, suggesting AGI architectures might exploit causal indefiniteness for enhanced problem-solving. Our work extends the quantum causal modeling framework of Leifer and Spekkens (2013) to develop AGI implementations that leverage these non-classical structures to formalise how quantum contextuality can enhance AGI systems.

Research Questions:

  • How do indefinite causal structures in quantum mechanics affect AGI reasoning and learning capabilities?
  • Can contextuality be leveraged as a resource for more powerful forms of machine cognition?
  • How do hardware implementations constrain or enable causal and contextual properties in QAGI?

Methodology:

  • Develop formal models of quantum causal inference compatible with AGI architectures
  • Create quantum circuit implementations of contextual knowledge representation
  • Simulate causal learning algorithms using both definite and indefinite causal structures
  • Identify hardware architectures most suitable for preserving contextual properties

Expected Outcomes:

  • Formal theorems connecting quantum contextuality to AGI capabilities
  • Prototype implementations of contextual reasoning for quantum-enabled AGI
  • Hardware specification requirements for contextuality-preserving quantum AGI

2. Quantum Thermodynamics for AGI

Quantum thermodynamics provides a fundamental framework for understanding the physical limits of information processing in AGI systems. Goold et al. (2016) establish that quantum effects significantly modify thermodynamic constraints on computation, potentially enabling more efficient paradigms. The resource theory approach developed by Brandão et al. (2015) offers analytical tools for quantifying thermodynamic resources required for quantum information processing. By applying these theories to quantum AGI architectures, we aim to identify fundamental energetic constraints on learning and inference.

Research Questions:

  • What are the fundamental thermodynamic limits on quantum AGI performance?
  • How do energetic constraints affect quantum AGI learning and inference?
  • Can thermodynamic computing principles enhance quantum AGI efficiency?

Methodology:

  • Develop thermodynamic models of quantum information processing relevant to AGI
  • Analyze energy requirements for quantum AGI operations across different hardware platforms
  • Investigate thermodynamic reversibility and its implications for AGI learning algorithms
  • Study entropy production in quantum AGI decision-making processes

Expected Outcomes:

  • Theoretical bounds on the energy efficiency of quantum AGI systems
  • Design principles for thermodynamically optimal quantum AGI architectures
  • Novel quantum learning algorithms that approach thermodynamic limits

3. Quantum Complexity

The advantages of quantum computing for AGI depend on understanding which cognitive functions benefit from quantum speedups and which remain computationally hard even with quantum resources Montanaro's (2016), Harrow et al. (2009).  To this end, we examine complexity constraints on QAGI.

Research Questions:

  • What AGI-relevant problems show provable quantum advantages in computational or query complexity?
  • How do quantum complexity classes inform practical QAGI implementation?
  • What are the implications of quantum query complexity for AGI knowledge acquisition?

Methodology:

  • Identify core AGI subroutines amenable to quantum speedup
  • Develop rigorous complexity analyses of hybrid classical-quantum AGI architectures
  • Create benchmarks comparing classical and quantum implementations of AGI functions
  • Analyze query complexity implications for knowledge acquisition in quantum environments

Expected Outcomes:

  • Catalog of AGI functions with provable quantum advantages
  • Complexity-theoretic framework for evaluating quantum AGI proposals
  • Resource estimation tools for practical quantum AGI implementation
  • Novel quantum algorithms for AGI-specific tasks with lower query complexity

4. Quantum Error Correction for AGI

Quantum error correction represents the most significant practical constraint on realizing quantum advantages for AGI. Gottesman's (1997) work establishes that quantum information requires sophisticated error correction techniques imposing substantial resource overheads. This component will address how error correction requirements interact with AGI principles, focusing on identifying which AGI functions maintain quantum advantages despite error correction overhead

Research Questions:

  • How do error correction requirements constrain practical quantum AGI implementation?
  • Can AGI architectures be embedded within quantum error correction codes?
  • What are the tradeoffs between error resilience and computational capacity in quantum AGI?

Methodology:

  • Analyze compatibility of AGI algorithmic structures with leading quantum error correction codes
  • Develop fault-tolerant implementations of core AGI functions
  • Investigate error-corrected quantum memory architectures for AGI knowledge representation
  • Simulate noise effects on quantum AGI performance with and without error correction

Expected Outcomes:

  • Error correction protocols optimized for AGI workloads
  • Design principles for fault-tolerant quantum AGI
  • Quantitative analysis of resource overhead for error-corrected quantum AGI
  • Performance benchmarks under realistic noise conditions

As an integrative framework for our research program, we will use quantum control theory to connect these four research areas. Quantum control provides mathematical tools to understand how complex quantum systems can be guided toward desired computational states while managing noise, thermodynamic constraints, and error propagation.

Our approach will:

  1. Develop unified mathematical formalisms that connect causality, thermodynamics, complexity, and error correction
  2. Create control protocols that optimize across these domains for AGI-specific workloads
  3. Identify fundamental tradeoffs in quantum AGI system design
  4. Establish practical engineering principles for future quantum AGI implementation

Simulation and Experiments

To validate our theoretical advances, we will:

  1. Enhance existing simulations to incorporate our new theoretical findings
  2. Develop new simulation frameworks specifically designed to test quantum causality, thermodynamics, complexity, and error correction in AGI contexts
  3. Establish partnerships with quantum computing laboratories to test our most promising algorithms and protocols on actual quantum hardware
  4. Create benchmarking frameworks to objectively compare classical and quantum approaches to AGI subroutines

Expected Impact and Outcomes

This research will advance both the theoretical understanding and practical implementation of quantum AGI through:

  1. Foundational theorems establishing the relationship between quantum phenomena and AGI capabilities
  2. Practical algorithms demonstrating quantum advantages for specific AGI functions
  3. Hardware specifications identifying requirements for implementing effective quantum AGI
  4. Experimental validation of our theoretical predictions on actual quantum devices
  5. Integration pathways for combining quantum components with classical AGI architectures

 

Open Source Licensing

GNU GPL - GNU General Public License

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  • Total Milestones

    4

  • Total Budget

    $100,000 USD

  • Last Updated

    20 May 2025

Milestone 1 - Quantum Causality & Contextuality of QAGI

Description

We will formalize and experimentally verify how agent-environment feedback loops behave when the interaction channel exhibits quantum causal non-separability (process-matrix formalism) and Kochen-Specker contextuality. Building on our previous work we'll extend our density-operator reinforcement model to a higher-order map without predefined causal arrows develop a Bell-Kochen-Specker-type inequality tailored to sequential QAGI actions and implement a two-round quantum game on IBM-Q hardware where the QAGI chooses between entangled measurement settings. Our theoretical work indicates that non-classical causal structure may endow QAGI policies with greater decision-theoretic power than any causally-ordered classical or quantum Markov chain though our extension of KS to typical AGI procedures suggests limits to its effectiveness. This milestone addresses fundamental questions about how indefinite causal structures affect AGI reasoning capabilities and how contextuality can be leveraged as a resource for machine cognition.

Deliverables

Deliverables 1. Preprint with complete mathematical proofs of process-matrix extension for QAGI-MDPs and contextual value assignment theorems 2. Open-source simulation code implementing indefinite-causality channels for AGI 3. Codebase of experimental implementation enabling reproducibility 4. Formal theoretical results related to causal inequalities relevant to QAGI implementation 5. Comparative analysis of quantum vs. classical causal performance on adaptive bandit tasks 6. (Prospective) Simulated circuit designs for hardware implementation of contextual knowledge representation

Budget

$25,000 USD

Success Criterion

Success Criteria 1. Demonstrate violation of our custom causal inequality on simulated quantum platform 2. Show statistically significant policy-efficiency uplift versus the best classical-causal baseline on adaptive bandit tasks 3. Prove formal theorems connecting quantum contextuality to specific AGI capabilities 4. Identify concrete hardware design requirements for preserving contextual properties in QAGI systems 5. Successfully implement at least one knowledge representation scheme that exploits quantum contextuality for improved reasoning

Milestone 2 - Quantum Thermodynamics of Agency

Description

We will quantify the minimal work cost and entropy production necessary for cognitive updates within QAGI extending our previous Landauer-style bounds to fully coherent time-dependent control unitaries. By modeling the agent's belief-updating channel as a geodesic on the manifold of unitaries with entropy-weighted quantum Fisher metric we'll derive closed-form work functionals and prove dissipation lower bounds for any CPTP update. Our analysis will identify regimes where quantum coherence provides a thermodynamic advantage despite error-correction overhead. This builds on our existing simulations of quantum hardware substrates and incorporates the latest developments in quantum Bayesianism. This milestone addresses fundamental questions about thermodynamic limits on quantum AGI performance and how energetic constraints affect learning and inference (builds on related work we have on thermodynamics).

Deliverables

Deliverables 1. Peer-reviewed article with analytic derivations of work costs for QAGI cognitive operations 2. Comprehensive thermodynamic data tables for various quantum AGI architectures 3. Reproducible code notebooks calculating optimal control pulses under realistic noise 4. Formal mathematical proof of dissipation lower bounds for cognitive updates 5. Simulation results comparing thermodynamic efficiency across hardware platforms 6. Design principles for thermodynamically optimal QAGI architectures

Budget

$25,000 USD

Success Criterion

Success Criteria 1. Demonstrate quantifiable thermodynamic advantage for at least one core AGI function using quantum coherence 2. Prove tight bounds on the minimal entropy production required for specific cognitive updates 3. Successfully model thermodynamic costs in simulated quantum hardware with realistic noise profiles 4. Identify at least one novel quantum learning algorithm that approaches the thermodynamic limits 5. Show improvement relative to the best-known classical reversible logic sequence for equivalent Bayesian evidence gain

Milestone 3 - Complexity implications for QAGI

Description

We will establish tight upper and lower bounds on the quantum time and query complexity (Qq) of core cognitive subroutines essential to AGI including credit assignment hierarchical planning and causal graph inference. By formulating each subroutine as an oracle problem we'll derive Grover-type lower bounds and design matching amplitude-amplification algorithms. We'll adapt verification protocols to certify that a QAGI policy encoded in a PEPS tensor network has correctly compressed training data and map abstract algorithms to surface-code logical qubits to quantify real-world resource requirements. This milestone addresses which AGI-relevant problems show provable quantum advantages in computational or query complexity and how these advantages translate to practical implementations when accounting for error-correction overhead.

Deliverables

Deliverables 1. Formal complexity-theory manuscript suitable for mathematics/CS journal publication 2. Open-access code implementing algorithms with proven complexity advantages 3. Simulated quantum compiler plug-in emitting lattice-surgery schedules for benchmark circuits 4. Resource estimation tools calculating physical qubit requirements and runtime for QAGI operations 5. Catalog of AGI functions with rigorous quantum advantage proofs 6. Complexity-theoretic framework for evaluating quantum AGI proposals

Budget

$25,000 USD

Success Criterion

Success Criteria 1. Prove asymptotic quantum advantage for at least three core AGI subroutines with full accounting for error correction overhead 2. Achieve an asymptotic complexity bound for causal-graph inference with total physical-qubit budget less than a scalar multiple of classical GPU baseline 3. Successfully implement at least one novel quantum algorithm specifically designed for an AGI-relevant task 4. Demonstrate lower query complexity for knowledge acquisition in simulated quantum environments 5. Create effective benchmarks comparing classical and quantum implementations of AGI functions

Milestone 4 - Quantum Error Correction for QAGI

Description

We will determine the fundamental performance envelope of AGI-level cognition when logical operations are mediated by fault-tolerant primitives. By integrating our transformer-style QAGI core into a rotated-surface-code stack we'll analyze how QEC constraints reshape agent architecture memory hierarchy and exploration strategies. We'll prove convergence conditions for policy-gradient ascent under fixed logical-cycle latency and design a hybrid stabilizer storage scheme that lowers logical memory cost while preserving code distance. Using hardware-in-the-loop testbeds with realistic correlated error spectra we'll run end-to-end agent-environment loops to evaluate real-world feasibility. This milestone addresses how error correction requirements constrain practical quantum AGI implementation and identifies which AGI functions maintain quantum advantages despite error correction overhead.

Deliverables

Deliverables 1. Academic paper on on architecture guidelines for fault-tolerant QAGI 2. Dataset of synthetic but physically-faithful error traces (subject to compute availability) 3. Dockerized simulation environment ready for adoption by peer laboratories (extending previous work on QDataSet) 4. Design specifications for hybrid memory-consolidation protocols 5. Formal proof of convergence conditions for stochastic control under QEC latency 6. Performance analysis of QAGI under realistic noise conditions with and without error correction

Budget

$25,000 USD

Success Criterion

Success Criteria 1. Maintain average episodic reward within close proximity to the ideal-logical-gate model while operating at physical error rates 2. Design error correction protocols specifically optimized for AGI workloads 3. Demonstrate successful fault-tolerant implementation of at least two core AGI functions 4. Quantify resource overhead tradeoffs for error-corrected quantum AGI 5. Establish clear design principles for fault-tolerant quantum AGI that can guide future hardware development

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