The Global Centre for Risk and Innovation (GCRI)
Quantum computers use qubits that can be in superpositions and become entangled, which in principle lets some problems be solved far faster than on classical computers. Current machines are noisy and small, and useful fault-tolerant computing requires error correction that is only beginning to be demonstrated. This page sets out how quantum computers work and fail, how progress is measured honestly, and what evidence serves the institutions deciding how to engage.
GCRI · In development · service level R0
Quantum computing is computation that uses qubits, superposition and entanglement to solve some problems faster than classical computers. Today's machines are noisy and small, fault tolerance needs error correction with large overhead, and advantage is specific to particular problems and must be compared with the best classical methods.
For quantum computing, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with true colour from NASA GIBS, framed on Japan and Korea; every other NASA layer, live feed and forecast waits in the layer list.
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For quantum computing, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with true colour from NASA GIBS, framed on Japan and Korea; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
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The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Quantum computing · Testbed lab · Device QD-28
| Q0 | 121 | 49 | 0.03 | Drifting |
| Q1 | 118 | 24 | 0.03 | Drifting |
| Q2 | 94 | 41 | 0.03 | Drifting |
| Q3 | 73 | 93 | 0.05 | Drifting |
| Q4 | 50 | 48 | 0.02 | Drifting |
Hardware, algorithms and actual maturity. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Qubits lose their quantum state through interaction with their environment. Gate errors and decoherence limit how long a computation can run before results become noise.
Fault-tolerant computing encodes each logical qubit in many physical qubits and corrects errors continuously. It only helps once physical error rates fall below a threshold.
Quantum algorithms speed up particular problems, such as simulating quantum systems or factoring. For many tasks classical computers remain as good or better.
Demonstrations of quantum advantage are often followed by improved classical methods. Honest comparison requires the best classical approach, not a convenient one.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Logical qubit | A qubit encoded across many physical qubits using error correction. |
| Gate fidelity | How closely a quantum operation matches its ideal, often expressed as a percentage. |
| Coherence time | How long a qubit keeps its quantum state before noise destroys it. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Physical qubit count | qubits | per announcement | Published device results | REPORTED |
| Two-qubit gate fidelity | percent | per device | Benchmark results | REPORTED |
| Logical qubit error rate | errors per logical operation | per experiment | Peer-reviewed experiments | OBSERVED |
| Coherence time | microseconds | per device | Device characterisation | REPORTED |
| Quantum volume or similar holistic benchmark | benchmark score | per device | Published benchmarks | REPORTED |
| Demonstrations surviving classical challenge | count | per review | Literature review | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| arXiv quant-ph and peer-reviewed journals | Primary experimental and theoretical results | Open access preprints; journal licence per article |
| Metriq benchmark platform (Unitary Foundation) | Community-submitted quantum benchmark results | Open, licence to check |
| Vendor technical documentation and roadmaps | Device specifications and stated targets | Public documents, terms per vendor |
| National academies and agency assessments of quantum computing | Independent assessments of progress and prospects | Public, terms per publisher |
| QED-C reports | Industry consortium analyses of use cases and benchmarks | Public reports, terms to check |
Quantum Technologies prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Research funders and science ministries | How much to invest in quantum computing research and access | Progress summaries; independent assessments |
| Organizations exploring quantum use | Whether a problem is a plausible quantum use case | Use case assessments with classical baselines |
| Security agencies | How progress affects cryptographic migration timelines | Error correction and scale evidence |
The central question is what has been demonstrated, not what has been promised.
Quantum computers gain their power from interference: algorithms are designed so that wrong answers cancel and right answers reinforce. That power is fragile. Qubits lose their quantum state through interaction with the environment, and every operation introduces some error. Quantum error correction encodes one reliable logical qubit in many physical qubits, but only works if physical error rates are below a threshold, and it multiplies the hardware required.
Known algorithms with large proven speed-ups are few. Factoring and discrete logarithms, which underpin public-key cryptography, are the best-known examples; simulation of quantum systems in chemistry and physics is another. For many optimization and machine-learning problems, a practical advantage has not been established, and improved classical algorithms have repeatedly narrowed claimed gaps.
| Method or data | What it shows | Limits |
|---|---|---|
| Benchmarking of gate fidelity and coherence | Quality of physical qubits | Benchmarks can be chosen to flatter a device |
| Error-correction experiments | Whether logical qubits outperform physical ones | Early demonstrations are small and short-lived |
| Algorithm complexity analysis | Theoretical speed-up and resource estimates | Resource estimates depend on hardware assumptions |
| Classical simulation and verification | Whether results could be reproduced classically | Verification becomes harder as devices grow |
| Peer-reviewed literature versus announcements | What has been independently assessed | Announcements often precede, or replace, peer review |
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.