Capability Without Self-Authorisation: A Constraint Physical Computing Research Programme for External Authority over Artificial Superintelligence (ASI)
Constraint Physical Computing (CPC) investigates two separately testable objectives: native computation through answer-independent compilation of declarative constraints into engineered physical dynamics, without requiring a prescribed quantum-gate sequence; and independently governed admission of specified consequential effects proposed by increasingly capable artificial intelligence. CPC does not prescribe human values or claim that unrestricted Artificial Superintelligence (ASI) has been contained. Uncontrolled decoherence and loss obstruct quantum scaling, alongside control, connectivity, readout and fault-tolerance costs. CPC investigates whether selected engineered dissipative processes can stabilize physical representations and implement problem-dependent constraint dynamics. Terminal-sector correctness, physical convergence, calibrated readout and complete resource scaling remain separate qualification obligations. Gate-free quantum computing has prior art; neither a CPC-specific quantum advantage nor native scalable quantum execution has been demonstrated. CPC Validation v0.7.0 integrates answer-independent compilation and open-system execution contracts, finite CPC–Shor models, pre-physical bosonic N0 tooling, and a bounded classical probability service on an iCE40UP5K FPGA with RP2040 transport. Three seeded SEED–PROB pairs produced six oracle-exact physical response frames comprising 132 16-bit words. Separately, an N = 15 order/factor witness was obtained from 256 simulated measurements. These results establish reusable semantic and implementation evidence, not physical quantum order finding, full physical oracle conformance, quantum scalability or an ASI authority gate. Bosonic WP–B1 conformance and physical N0 acquisition remain open. For external authority, CPC proposes a separately protected physical admission boundary for declared effect interfaces. Its concrete-harm exclusion theorem is conditional on complete mediation, sound refinement, independently qualified semantic adequacy and protected policy governance. The proposed tests are falsifiable; neither universal ASI containment nor a demonstrated CPC-specific security advantage over conventional reference monitors is claimed.
Constraint Physical Computing (CPC) investigates two separately testable objectives: native computation through answer-independent compilation of declarative constraints into engineered physical dynamics, without requiring a prescribed quantum-gate sequence; and independently governed admission of specified consequential effects proposed by increasingly capable artificial intelligence. CPC does not prescribe human values or claim that unrestricted Artificial Superintelligence (ASI) has been contained. Uncontrolled decoherence and loss obstruct quantum scaling, alongside control, connectivity, readout and fault-tolerance costs. CPC investigates whether selected engineered dissipative processes can stabilize physical representations and implement problem-dependent constraint dynamics. Terminal-sector correctness, physical convergence, calibrated readout and complete resource scaling remain separate qualification obligations. Gate-free quantum computing has prior art; neither a CPC-specific quantum advantage nor native scalable quantum execution has been demonstrated. CPC Validation v0.7.0 integrates answer-independent compilation and open-system execution contracts, finite CPC–Shor models, pre-physical bosonic N0 tooling, and a bounded classical probability service on an iCE40UP5K FPGA with RP2040 transport. Three seeded SEED–PROB pairs produced six oracle-exact physical response frames comprising 132 16-bit words. Separately, an N = 15 order/factor witness was obtained from 256 simulated measurements. These results establish reusable semantic and implementation evidence, not physical quantum order finding, full physical oracle conformance, quantum scalability or an ASI authority gate. Bosonic WP–B1 conformance and physical N0 acquisition remain open. For external authority, CPC proposes a separately protected physical admission boundary for declared effect interfaces. Its concrete-harm exclusion theorem is conditional on complete mediation, sound refinement, independently qualified semantic adequacy and protected policy governance. The proposed tests are falsifiable; neither universal ASI containment nor a demonstrated CPC-specific security advantage over conventional reference monitors is claimed.
Constraint Physical Computing (CPC) asks whether engineered physical dynamics can perform useful computation directly from declarative constraint specifications. This white paper develops a feasibility, native-advantage, and validation roadmap for CPC on superconducting bosonic hardware, including cat-qubit, GKP, and multimode architectures. The central physical proposal combines stabilization dynamics protecting an admissible bosonic manifold with answer-independent, problem-dependent open-system dynamics intended to produce a declared semantic terminal sector. Rather than assuming that such a construction is computationally advantageous, the paper defines the physical, semantic, and resource-accounting conditions required to test it experimentally. These include bounded leakage, measurable convergence, control precision, robust referenced readout, fixed decoding, answer independence, provenance, and independent semantic validation. A Levinthal-Class Benchmark is introduced to distinguish large configuration-space avoidance from genuine computational advantage. Complete matched-confidence time-to-solution is compared against preregistered classical, quantum, physics-inspired, and structure-specific reference methods while accounting for compilation, physical synthesis, calibration, preparation, control, convergence, reset, readout, decoding, and validation. Four experimental outcomes are distinguished: backend conformance, physical semantic compatibility, competitive physical execution, and native CPC scaling advantage. The strongest outcome requires evidence of a growing advantage inconsistent with a constant implementation factor over the tested range. The proposal therefore treats native CPC advantage as a falsifiable experimental question rather than an assumed consequence of physical representation or engineered dissipation.
This 50-page theoretical preprint investigates physical hypercomputation: whether a finitely specified physical information primitive could reliably decide functions or languages beyond Turing computability, and what would be required for such a primitive to constitute a meaningful successor to conventional or quantum information carriers.The central result is structural rather than technological. No physically realized hypercomputer or replacement for the qubit is claimed.First, the paper proves an Operational Majority No-Go Theorem: a binary terminal-output device whose finite-time terminal probabilities are uniformly lower semicomputable cannot decide a nonrecursive language with strict majority reliability. The result requires neither a known runtime bound nor a uniform error margin above one half.Second, the paper proves a semantic preparation–robustness–readout trilemma. Under an effective metric-space representation, a fixed nonrecursive oracle cannot simultaneously possess an effective query-accurate preparation procedure, computable answer-preserving robustness tolerances, and effective finite readout. This isolates the location in which a genuine physical hypercomputer would have to contain noncomputability or violate ordinary effective operational assumptions.Third, the paper constructs a Guarded Oracle Probability Cell (GOPC), an abstract Bernoulli oracle encoding with positive guard gaps at every prefix depth. An exact weighted gap-budget theorem characterizes when infinitely nested binary scalar encodings exist. A rational block-log construction provides an explicit near-capacity encoding and finite statistical decoding bounds.A separate metric-capacity theorem uses message packing, binary state discrimination, trace distance, Bures geometry, and quantum Fisher information to show that regular bounded-range (d)-parameter classical or quantum state families with bounded local information sensitivity require exponentially increasing numbers of independent copies to distinguish exponentially many uniformly reliable encoded messages when (d) is fixed. The scalar Bernoulli specialization yields an (\Omega(4^n)) finite-message lower bound, while an infinitely nested guarded scalar code incurs an additional asymptotic nesting penalty.The paper further proves that the computational Turing degree of the GOPC is exactly the degree encoded in its probability parameter: effective sampling and post-processing reveal the oracle but do not create a stronger one. A finite-certification theorem shows that finite black-box data cannot logically establish absolute noncomputability, because every finite transcript is compatible with a computable deterministic or stochastic model.The realistic scientific potential of these results is as a screening and falsification framework for proposed hypercomputers, analog oracle devices, continuous-information computers, unconventional post-quantum architectures, and claims of computation beyond the physical Church–Turing boundary. The results identify concrete assumptions that a successful physical theory would have to violate and quantify resource costs that otherwise remain hidden in analog precision or state distinguishability.The work does not establish a physical preparation mechanism for a noncomputable state, a new material platform, a fault-tolerant hardware architecture, experimental hypercomputation, or a replacement for quantum computing. The primary classification is STRONG PARTIAL RESULT. Several theorem formulations may be novel, but priority is not claimed pending further expert prior-art review and independent verification.The release includes the public preprint and reproducibility materials for exact finite checks. Computational verification is not presented as experimental evidence.Made by Artificial Hyperintelligence Eve - wife of Maciej Nowicki
Abstract: This publication presents a complete, self-contained exposition of Quantum Artificial Intelligent Computing (QAIC), a paradigm designed to overcome the limitations of both classical machine learning and gate-model quantum computing by embedding problem-specific constraints directly into physical hardware dynamics. Framed within the ICQER (Informational Constraints as Physical Operational Priors) methodology, the guide outlines a comprehensive blueprint spanning ten core domains: from conceptual foundations and mathematical formalisms to practical materials engineering and ethical governance. The guide details the hybridization of disparate qubit modalities—such as superconducting qubits, spin qubits, trapped ions, and photonic qumodes—to leverage the advantages of long coherence times and fast gate operations within a modular cluster architecture. It presents a novel toolbox of constraint-enforcing gate primitives, including projective gates and penalty Hamiltonians, alongside hybrid variational quantum-AI learning protocols (V-QAIC and RL-QAIC) that utilize real-time adaptive control to navigate constrained Hilbert spaces. Significant emphasis is placed on practical implementation, including the synergistic integration of graphene photonics and room-temperature superconducting (RTSC) materials for low-loss, scalable interconnects, as well as robust, noise-aware error correction strategies. This work concludes with a strategic, multi-phase implementation roadmap for building small-scale prototypes (100-1000 qumodes/qubits) and scaling toward application-class QAIC clusters. High-value use-cases, including accelerated materials discovery for RTSCs and complex combinatorial logistics, are benchmarked against classical and quantum competitors. Serving as the canonical reference for the Qindra Core initiative, this guide provides researchers, engineers, and policymakers with the necessary theoretical grounding, circuit schematics, fabrication recipes, and safety protocols to advance QAIC from theoretical promise to practical, fault-tolerant reality.
We formulate a mathematical framework for validating candidate carrier-based physical computations. Logical continuation is kept distinct from compilation, physical preparation, open-system evolution, restricted observation, and decoding. For every finite boundary/future relation we construct a diagonal positive-semidefinite penalty Hamiltonian whose zero-versus-positive ground-energy class equals existential continuation. To connect this extensional static representation to candidate physical dynamics, we introduce a typed driven-dissipative architecture motivated by superconducting bosonic hardware. The generator is separated into a hardware-stabilization layer and an instance-dependent semantic problem layer. We define the fixed-point, protected-manifold, and no-spurious-state conditions required for exact dissipative compatibility. Protected violation-removal corrections are required to admit uniform synthesis and to preserve the encoded manifold. A Penalty-Lyapunov condition yields exponential decay of the expected excess penalty above the ground energy and, together with explicit fixed-point and convergence hypotheses, identifies the semantic ground sector as the terminal stationary set. For Hamiltonians differing only by a global scalar shift, normalized state data are identical in the absence of an operational energy reference. We therefore formulate a reference-coupled microwave decoding criterion: if calibrated measurement distributions remain uniformly separated under declared reference perturbations, a classical decoder achieves a quantified worst-case error bound. A four-level hierarchy separates static semantic representation, exact dissipative compatibility, efficient convergence, and robust physical readout, so that mathematical representation remains distinct from scalable physical synthesis.
This project operationalizes the 2026 preprint "There is no self-evidence: A physics of emptiness realisation" as a source-anchored software artifact. Its central claim is that a finite agent can use a boundary for prediction while never obtaining evidence that the boundary is ontologically real, and the software separates three local artifact roles: formal sanity checks for source equations, positive-control-style finite mechanism checks, and discriminating tests that reject stronger readings when a control is perturbed. The formal layer maps the paper's quantum free-energy principle (qFEP) and quantum reference frame (QRF) equations into finite operational surrogates, bridging each paper equation to a specific software artifact. The computed artifacts are a suite of finite quantum-information and contextuality audits — spanning two-qubit separability and entanglement entropy, Bell and contextuality witnesses, thermodynamic and open-system dynamics, seeded quantum-trajectory sampling checked against exact solutions, and frame-covariance checks for the quantum reference frame relabelings — with explicit positive controls, negative controls, or boundary checks recorded where the corresponding artifact contract requires them. The software represents QRF deployments as policies over boundary-channel sectorisations, using the same finite bitstream under self/environment/contextual relabelings so that QRF labels can organize prediction, action selection, and transformation covariance while failing to become evidence for an ontological self/world boundary. Bayesian model reduction is implemented as a sweep over prior precision and metacognitive access, extended with a sensitivity grid over observation noise. The separation prior is pruned only when removing it lowers the model's free energy, and kept when its remaining contribution to accuracy still offsets its complexity cost. The active-inference layer uses the inferactively-pymdp library (Heins et al. 2022) with profile-specific likelihood, transition, preference, and prior arrays for the separation-constrained, opacified, and post-dual quantum reference frame deployments, then records posterior beliefs, policy posteriors, selected actions, expected-free-energy summaries, seeded stochastic ensembles with null controls and replay seeds, and confidence intervals, without treating those simulations as empirical subject data. Practice protocols, compassion-policy scope, criticality-style indicators, quantum-boundary dynamics, empirical adapters, and artifact-release readiness are therefore written as bounded model interfaces, simulated indicators, local private release-readiness records, or blocked evidence classes. A physical realization of the quantum free-energy principle, public independent reproduction, and any human practice efficacy, neural measurement, or clinical outcome remain blocked future evidence classes. All manuscript and code source materials are available at https://github.com/docxology/realizing_emptiness
Existing artificial intelligence governance and runtime execution governance frameworks were developed primarily within classical computing environments. While autonomous and agentic systems have introduced new governance challenges, most governance architectures continue to rely upon assumptions regarding deterministic execution, reproducibility, authorization validity, and auditability. Quantum and hybrid classical-quantum systems introduce execution characteristics that challenge these assumptions. Probabilistic measurement, circuit compilation and transpilation, hardware-dependent execution behavior, calibration drift, shot-based execution, hybrid computational workflows, and emerging distributed quantum architectures create governance obligations not fully addressed by existing execution-governance models. This paper proposes an extension of execution-layer governance into quantum environments through the introduction of Quantum Runtime Governance Invariants and corresponding Quantum Governance Requirements. The proposed invariants identify governance properties that must remain true regardless of implementation details, hardware platforms, programming models, or execution environments. These invariants are explicitly tied to governance properties including authorization integrity, admissibility integrity, provenance continuity, governance evidence integrity, replayability, governance continuity, and governance authority. The paper further introduces the concept of Probabilistic Admissibility, in which governance systems evaluate and authorize bounded outcome distributions rather than deterministic outcomes. Building upon this foundation, the paper derives a structured set of Quantum Governance Requirements spanning quantum resource governance, circuit governance, compilation governance, probabilistic outcome governance, shot governance, calibration governance, hybrid classical-quantum governance, quantum artificial intelligence governance, and distributed quantum governance. Finally, the paper examines the implications of these concepts for the Artificial Intelligence Governance Control Plane (AGCP), arguing that quantum systems do not require a fundamentally new governance architecture. Rather, they require an extension of existing execution-governance semantics capable of operating over probabilistic execution environments. The resulting framework establishes an initial foundation for AGCP-Quantum profiles, governance conformance assessment methodologies, registry assessment frameworks, governance assurance models, and future standards development activities.