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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” 14 papers
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…
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…
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…
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…
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…
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.…
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…
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…
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…
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;…
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…
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…
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…
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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