QNFO Papers

Open research library

Papers you can read, cite and question

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.

Looking for an answer rather than a paper? Ask the whole corpus: answers cite the papers they come from.

Energy per answer papers 5 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.

Choosing a platform. Judging a vendor or platform before committing Tagged papers: 27.

  1. Energy per answer

    Thermodynamic Budgeting for Hybrid Quantum-Classical Computing: A Three-Layer Energy Model with Explicit Arithmetic and a 15-Month Validation Roadmap

    Hybrid quantum-classical (HQC) computing is the dominant near-term paradigm, yet its energy accounting is rarely made explicit: the classical control, readout, cryogenic, and orchestration hardware surrounding the quantum processing unit (QPU) can dominate the total power budget, eroding any…

  2. Energy per answerv0.7

    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 —…

  3. Energy per answerv2.0.1

    The BQNN Classical Baseline: Constructive Falsification of Near-Term Quantum Advantage at a Fifteen-Million-to-One Energy Disadvantage

    Classical BinaryConnect ensemble achieves 92.73% accuracy vs BQNN quantum inference (~82-85%) at 0.0365 J vs 550,000 J (ratio 1.5x10^7). CAL-BQNN-05 confirmed one year early.

  4. Energy per answer

    Quantum Abacus

    The scalability of active, gate-based quantum computers is fundamentally challenged by the “Thermodynamic Wall,” where the heat from control electronics becomes prohibitive. This study computationally investigates the “Quantum Abacus,” a passive quantum computing architecture that circumvents this…

  5. Energy per answer

    Escaping the Millikelvin Death Spiral

    Standard quantum architectures rely on dilution refrigeration to suppress thermal noise, tethering the industry to a non-scalable 20 mK thermal floor. The cooling power at millikelvin temperatures scales poorly, creating a “mainframe constraint” that limits widespread deployment. Here, a 4-Kelvin…

New papers by email

One short weekly digest: titles and links. No tracking; unsubscribe any time.