Energy limits of computation
The energy per operation of current hardware is compared with physical limits and with trends, to judge how much further efficiency can improve.
GCRI · Data and Compute
Physics of computing
Physics underpins computing hardware and the limits of efficiency: how transistors switch, how heat flows, and where new paradigms such as quantum and neuromorphic computing may change what is possible. In this field it grounds claims about hardware performance and energy.
01Contents
Physics of computing in five parts.
02Application
What physics and quantum science does here.
Physics studies matter, energy and their interactions, from fundamental particles to large-scale systems. It provides the governing equations used across hazard modelling, sensing and engineering. In data and compute and energy, physical principles constrain hazard models, sensor design and engineering limits. They help distinguish physically plausible scenarios from implausible ones. The canonical description is on the STEM Commons.
Thermal management physics
Heat transfer in dense racks and chips is modelled to understand cooling limits as power density rises.
Assessing new computing paradigms
Claims for quantum, analogue and neuromorphic computing are compared on the problems where each could outperform classical computing.
03Monitor
Quantum experiment.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Quantum experiment
Data and compute · Physics of computing · Run R-6942
Calibration runGCRI · Data and Compute
Qubit calibration
| Q0 | 108 | 130 | 0.003 | In spec |
| Q1 | 120 | 109 | 0.004 | Drifting |
| Q2 | 124 | 132 | 0.006 | Drifting |
| Q3 | 142 | 76 | 0.008 | Drifting |
Method card
- Method
- Physics and quantum science
- Device
- example testbed
- Shots
- 18,906
- Label
- MODELLED
Randomised benchmarking
04Data
What the method consumes.
Datasets and records used by physics and quantum science on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Device and chip specifications | Energy per operation | DECLARED |
| Thermal measurements | Heat transfer | OBSERVED |
| Peer-reviewed benchmarks | Paradigm comparison | REPORTED |
05Methods and evidence
How physics of computing is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Datasets and provenance
The data classes behind physics of computing and what is recorded about each before it is used.
| Data class | Provenance recorded | Resolution stated | Licence checked |
|---|---|---|---|
| Satellite imagery, optical and radar | sensor, processing level, acquisition date | pixel size and revisit | per scene |
| Reanalysis and climate model output | model, experiment, member, version | grid and time step | per release |
| In-situ sensors and gauges | station, instrument, calibration record | point and sampling rate | per network |
| Population and asset exposure | method, vintage, input sources | cell size and unit | per release |
| Boundaries and reference geography | producer, edition, date | scale | per edition |
- Lineage
- W3C PROV record from source to figure
- Principles
- FAIR: findable, accessible, interoperable, reusable
- Gaps
- shown as missing, never filled silently
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
Theories are tested by reproducible experiments and precise measurement. Numerical codes are verified against analytical solutions and benchmark problems.
| Check | How it is done |
|---|---|
| Peer-reviewed evidence | Performance claims rely on reproducible, peer-reviewed results |
| Like-for-like comparison | Paradigms are compared on the same problems and metrics |
| Physical plausibility | Claims are checked against known physical limits |
Idealised models may omit friction, heterogeneity or coupling present in real systems. Scale transfer from laboratory to field introduces error.
07Process
How the process runs.
Process
Physics of computing: Post-quantum cryptography migration
NIST published FIPS 203, 204 and 205 on 13 August 2024; the migration steps are common practice, and the IR 8547 timeline is a draft.
01 Discover
- 01Cryptographic inventoryDatasetOrganisations must find where vulnerable algorithms are used and plan to replace or update them. The inventory lists algorithms and keys.
- 02Prioritise by data lifetime and exposureStepIR 8547 names the harvest now, decrypt later threat. Systems holding long-lived sensitive data come first.
02 Plan
- 03Select standardised algorithmsStepFIPS 203, 204 and 205 specify ML-KEM, ML-DSA and SLH-DSA. Falcon and HQC were selected for ongoing standardisation.
- 04Vendor roadmapsDataset · Data stewardHardware security modules and libraries need vendor support for the selected algorithms. Roadmaps come from each vendor.
03 Deploy
- 05Hybrid deploymentModelIR 8547 says migration may start with hybrids of quantum-resistant and quantum-vulnerable algorithms, secure if one component is secure.
- 06TestExerciseInteroperability, performance and fallback are tested before cut-over.
- 07Cut over and retireDecision · Data stewardIn draft IR 8547, 112-bit public-key algorithms are deprecated after 2030 and quantum-vulnerable ones disallowed after 2035.
04 Sustain
- 08Crypto agilityStepCrypto agility is the ability to replace and adapt cryptographic algorithms while preserving security and ongoing operations.
Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names.
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Quantum Technologies stewards physics and quantum science for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of physics and quantum science in this field?
Idealised models may omit friction, heterogeneity or coupling present in real systems. Scale transfer from laboratory to field introduces error.
How are results checked?
Theories are tested by reproducible experiments and precise measurement. Numerical codes are verified against analytical solutions and benchmark problems.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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