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728 papers from the QNFO program, filed on nine shelves by the reader each one is written for: energy per answer, error correction, physics, numbers and p-adics, logic and formalism, AI and knowledge, autonomous systems, assessments, and cross-cutting essays. Every page renders its mathematics and can be questioned in place. How a living paper works.

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Results for “joules” 22 papers

  1. Physics

    Computation as Hamiltonian Isomorphism: A Mode-Based Taxonomy of Physical Computing Substrates

    Special-purpose accelerators, neuromorphic chips, and optical processors are today evaluated by qubit or transistor counts, yet the machines that deliver the largest effective speedups are those whose physical dynamics directly embodies the target problem. This paper addresses a specific gap: no…

  2. Energy per answerrev 27 Oct 2026v2.0.0

    Ultrametric p-Adic Arithmetic as Energy-Proportional Precision Computing: An Energy Model for Digit-Terminable Multiply-Accumulate

    Fixed-width Archimedean floating-point arithmetic pays a constant energy cost per multiply-accumulate (MAC) regardless of the precision actually required by the computation. The gap this paper addresses is the absence of any explicit joules-per-operation model connecting non-Archimedean digit…

  3. Numbers & p-adics

    Optimal Radix q under Resource Constraints: A Reconciled Derivation Framework for Ultrametric Hierarchies

    Hierarchical (ultrametric) representations organize N items in a q-ary tree of depth n = ⌈ln N / ln q⌉, and the choice of branching factor q determines how storage, interconnection, and traversal resources scale. Prior ultrametric modeling has treated the radix as a fixed structural parameter…

  4. Energy per answerrev 29 Oct 2026v3.0.0

    The Margolus-Levitin Bound as an Energy-Time Limit for Quantum Computation at the Landauer Scale: Derivation, Saturation Analysis, and a Joules-per-Compute Normalization for Cryogenic Controllers

    Quantum speed limits bound how fast a quantum state can evolve, and the Margolus-Levitin (ML) bound sets the canonical floor: a system with mean energy $\langle H\rangle$ above its ground state needs at least $\tau \ge \pi\hbar/(2\langle H\rangle)$ to reach an orthogonal state. Hardware teams…

  5. Energy per answerrev 3 Sep 2026v2.0.1

    Landauer-Limit Energy Floor and JPCUB Normalization for Cryogenic Quantum Control Electronics

    Cryogenic quantum control electronics dissipate energy many orders of magnitude above the thermodynamic minimum implied by Landauer's principle, and the gap matters for the joules-per-compute economics of fault-tolerant machines. This paper computes the Landauer floor for a rotated surface-code…

  6. Energy per answerrev 3 Sep 2026v2.0.1

    A Lower Bound on Energy Cost per Logical-Qubit Operation in Surface-Code Quantum Error Correction

    Surface-code quantum error correction translates one logical qubit into a two-dimensional array of physical qubits, and the energy cost of that translation is rarely quantified at the level of a logical operation. This paper specifies a minimal, convention-explicit energy model for a rotated…

  7. Energy per answerrev 3 Sep 2026v2.0.0

    Quantum Computation Latency-Energy Tradeoff and the JPCUB Metric

    Fault-tolerant quantum computation trades latency against energy through the code distance: a larger distance d costs more syndrome rounds and more physical gates per logical operation, lengthening every operation and raising its energy. This paper computes that tradeoff for the rotated surface code…

  8. Energy per answerrev 3 Sep 2026v2.0.0

    Revising the Joules-per-Compute (JPCUB) Benchmark: Normalizing Energy Efficiency by Logical-Qubit Operations

    Energy benchmarks for quantum processors are usually quoted per physical gate or per physical qubit, figures that hide the dominant cost of a fault-tolerant machine: the error-correction overhead that multiplies physical work by a distance-dependent factor before any logical operation is delivered.…

  9. Numbers & p-adicsv2.5.1

    The Ultrametric Program: One Structural Object Across Seven Research Domains, and Its Falsifiable Tests

    Seven research domains — ultrametric physics, the laws of form, infomatics, paradigm engineering, consilience research, a cloud-native platform, and interactive demos — are claimed to be seven vocabularies for one structural object: a nested, hierarchical partition logic. The object is defined by…

  10. Numbers & p-adicsv1.4.0

    The Trapped-Ion Ultrametric Testbed: A Falsifiability Register for Testing p-Adic Structure in Quantum Dynamics

    Twenty-one published records from a single research program, spanning December 2025 to August 2026, are here organized into one testable claim: trapped-ion quantum simulators are the first near-term platform on which ultrametric (p-adic) structure in quantum dynamics can be accepted or rejected by…

  11. Energy per answerv1.1.2

    Joules-per-Solution for Stochastic and Agentic Inference: Benchmarking Frontier and Agentic LLMs Against the Human Brain

    The joules-per-solution (J/S) metric, introduced in JPCUB P0 as a universal, physics-grounded measure of computational efficiency, assumed deterministic solvers: one run yields one solution. Large language models (LLMs) violate that assumption twice over: they are stochastic samplers whose outputs…

  12. Energy per answerv2.4.1

    QWAV Strategy v2.4.1: The Energy-Standard Playbook — Consortium Governance, Verified Precedents, and the JPCUB Road to a Quantum Computing Energy Benchmark

    Quantum computation is known to possess theoretical energy-consumption advantages over classical computation — Meier and Yamasaki proved an exponential energy advantage on Simon's problem (arXiv:2305.11212), and Fellous-Asiani et al. demonstrated full-stack resource-efficiency optimization regimes…

  13. Energy per answerv2.3.0

    JPCUB Competitive Landscape v2.0: System-Level Joules-per-Solution Estimates for 17 Quantum Computing Platforms from Published Specifications

    The JPCUB P0 protocol (https://papers.qnfo.org/papers/joules-per-solution-metric/) defines the joules-per-solution metric — total system energy per correct answer — as a universal, physics-grounded benchmark for computational platforms. The qwav.tech competitive landscape displays six platforms with…

  14. Energy per answerv1.2.0

    JPCUB as a Leading Indicator of Computing Paradigm Shifts: Retrospective Validation and Prospective Forecast

    The computing industry lacks a single, cross-domain metric that can compare energy efficiency across fundamentally different paradigms — CPU, GPU, neuromorphic processor, in-memory accelerator, or spintronic logic. Traditional metrics (FLOPS, MIPS, transistor count) describe what already exists;…

  15. Energy per answerv0.7.0

    The Qudit Advantage: System-Level Joules-per-Solution Comparison of a Qudit Architecture Against 17 Conventional Qubit Quantum Computing Platforms

    Prior work benchmarked 17 qubit-based quantum computing platforms across the joules-per-solution (JPCUB) energy-efficiency metric. All 17 platforms use two-level quantum systems (qubits). This paper extends the JPCUB framework to qudit architectures — d-level quantum systems — and computes a…

  16. Energy per answer

    The QWAV Decade: Enterprise p-Adic Computing 2025-2035

    Computing efficiency — measured by Joules Per Computational Unit (JPCUB †) — will be the dominant competitive axis in enterprise computing by 2035. This document forecasts the JPCUB ecosystem's evolution across three eras. Era 1: Benchmark (2025-2028) — JPCUB is validated against public hardware…

  17. Energy per answer

    The Joules-per-Solution Metric: Definition, Measurement Protocol, and Anti-Gaming Provisions for Honest Computational Benchmarking

    The global computational infrastructure spans seven distinct paradigms — from classical CPUs to quantum processors, from AI accelerators to neuromorphic chips — yet no universal metric exists for comparing their computational value. Every paradigm benchmarks itself on its own terms: quantum volume…

  18. AI & knowledge

    Auditing the BQNN: Does a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware Demonstrate a Route to Near-Term Quantum Advantage?

    We audit the BQNN paper by Lakhdar-Hamina et al. (2025, PRL / arXiv:2507.21222v2) which implements a tunable quantum neural network on three quantum computing platforms and claims that the architecture "may offer a route to near-term quantum advantage." Through a complete Phase 1-4 research pipeline…

  19. Physicsv2.0.0

    Project Rosetta: The Approximation Entropy & The Fractal Limits of Digital Physics — v2.0

    Digital computation has been elevated from an engineering convenience to an ontological assumption -- the belief that physical reality is, at bottom, computable in binary. We challenge this assumption. Through a systematic metamathematical analysis connecting symplectic geometry, transcendental…

  20. AI & knowledgerev 20 Oct 2026v1.1.0

    Institutional Reform: Fixing the Epistemic Incentive Structure of Post-Classical Computing

    The first four papers in this series established that quantum computing's failure is epistemic (Phase I), surveyed alternatives (Phase II), examined physical limits (Phase III), and proposed an investment portfolio (Phase IV). This paper addresses the institutional question: why did $35 billion of…

  21. AI & knowledge

    Manifesto for Honest Computation

    Five papers have traced an arc from diagnosis to prescription. The qubit is a scaffold, not an invariant. Alternative paradigms exist. Physical limits are real. A diversified portfolio is rational. Institutions must be reformed. This final paper --- the Manifesto --- synthesizes these findings into…

  22. Energy per answerrev 14 Sep 2026v2.0.0

    The Physics of Computation: Fundamental Limits and the Honest Boundaries of Post-Classical Computing

    Phase III of the Qubit Delusion series. Examines fundamental physical limits on computation: Landauer bound, Margolus-Levitin theorem, Bremermann limit, Bekenstein bound. Shows that quantum error-correction overhead (1e2-1e3 physical operations per logical operation) multiplies thermodynamic cost…

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