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611 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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Energy per answer papers 8 papers
Platform builders, data-centre operators and energy analysts. What a correct answer costs in joules. Joules-per-Solution (JPCUB) definitions and measurements, Landauer and Margolus-Levitin limits, energy and power budgets, and energy benchmarking across quantum, HPC and AI hardware.
Benchmarking. Measurement protocols and comparative benchmarks Tagged papers: 13.
A Cost-Model Benchmark for Mixed-State Quantum Simulation Workloads: Entanglement-Cost Anchors and Reuse-Aware Re-Entry Metrics
Classical simulation of quantum circuits increasingly takes the form of repeated-run workloads—variational-parameter sweeps, noisy multishot studies, and error-correction cycles—rather than isolated executions, and the target states of such workloads are frequently mixed rather than pure. This paper…
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…
The Qudit Advantage: System-Level Joules-per-Solution Comparison of a Qudit Architecture Against 17 Conventional Qubit Quantum Computing Platforms
The JPCUB Competitive Landscape v2.0 benchmarked 17 qubit-based quantum computing platforms across a single system-level energy-efficiency metric: joules per solution (J/sol). All 17 platforms use two-level quantum systems (qubits). This paper extends the JPCUB framework to qudit architectures —…
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…
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
We define and formalize the joules-per-solution (J/S) metric as a universal, cross-domain measure of computational energy efficiency. The metric accounts for six energy components: computation, memory, I/O, cooling, power conversion, and amortized manufacturing. We provide a closed-form definition,…
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