The Global Centre for Risk and Innovation (GCRI)
Quantum technologies depend on materials, from superconducting circuits and semiconductor spin qubits to photonic chips and isotopically pure crystals. In turn, quantum simulation may one day help design new materials and molecules. This page sets out how materials limit quantum devices, how quantum methods could aid materials science, and what evidence serves the researchers and funders working at this intersection.
Quantum and materials · Testbed lab · Device QD-77
| Q0 | 142 | 116 | 0.02 | In spec |
| Q1 | 83 | 48 | 0.01 | In spec |
| Q2 | 69 | 99 | 0.01 | Drifting |
| Q3 | 142 | 21 | 0.05 | Drifting |
| Q4 | 162 | 83 | 0.03 | Drifting |
Fig.Quantum testbed. Design wireframe with placeholder names and illustrative values, not a record or a live system.
Quantum materials, in this context, are the substrates, films and crystals that quantum devices are built from, whose defects limit coherence. Fabrication yield sets how far devices scale, some inputs such as helium-3 have few suppliers, and quantum simulation may in future help design new materials and molecules.
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For quantum and materials, 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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Chemistry and materials simulation. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Impurities, surface defects and two-level systems in materials interact with qubits and shorten coherence. Better materials and fabrication extend it.
Producing many identical qubits needs precise fabrication. Variability between devices, even from one wafer, limits how far systems can scale.
Helium-3 for dilution refrigerators, isotopically enriched silicon and specialised lasers have limited suppliers, creating supply risks.
Simulating molecules and materials is a leading candidate use of quantum computers, though classical methods remain strong for most problems today.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Two-level system defect | A material defect that can absorb energy from a qubit and shorten its coherence. |
| Dilution refrigerator | A cryogenic system that cools devices to millikelvin temperatures, using helium-3 and helium-4. |
| Fabrication yield | The share of manufactured devices or qubits that meet specification. |
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 |
|---|---|---|---|---|
| Qubit coherence time by material platform | microseconds | per publication | Peer-reviewed results | OBSERVED |
| Device yield in fabrication | percent of functional qubits | per fabrication run | Foundry and lab reports | REPORTED |
| Helium-3 supply | litres per year | annual | Government and industry reports | ESTIMATED |
| Quantum chemistry demonstrations with classical comparison | count | per review | Literature review | ESTIMATED |
| Specialised component suppliers | count by component | per assessment | Supply chain studies | ESTIMATED |
| Materials databases used for qubit research | count | per assessment | Research surveys | REPORTED |
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 |
|---|---|---|
| Peer-reviewed materials and device literature | Coherence and fabrication results | Licence per article |
| Materials Project and other open materials databases | Computed materials properties | Open, CC BY 4.0 |
| Government reports on helium-3 and critical inputs | Supply and demand of scarce inputs | Public, terms per government |
| Quantum supply chain studies | Component suppliers and dependencies | Licence per publication (to check) |
| Benchmark studies of quantum chemistry algorithms | Comparisons with classical methods | Licence per publication |
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 | Which materials and fabrication research to support | Bottleneck reviews |
| Industry and national labs | How to secure specialised inputs | Supply risk assessments |
| Chemistry and materials researchers | Where quantum simulation could help their work | Use case assessments |
The question is whether quantum simulation will beat improving classical methods on problems that matter.
The behaviour of molecules and materials depends on how electrons interact. For many systems, classical approximations such as density functional theory work well. For strongly correlated systems, such as some catalysts, transition-metal complexes and high-temperature superconductors, the computational cost of exact classical simulation grows very rapidly with system size. A quantum computer represents quantum states natively and could, in principle, simulate such systems efficiently.
Resource estimates for industrially relevant problems suggest that large, error-corrected machines would be required. Meanwhile classical methods, including tensor networks and machine-learning approaches, keep improving and raising the bar. Quantum materials research also runs the other way: new materials are needed to build better qubits.
| Method or data | What it shows | Limits |
|---|---|---|
| Classical computational chemistry | Accurate results for many systems | Cost grows quickly for strongly correlated electrons |
| Quantum algorithm resource estimation | Qubits and operations needed for target problems | Highly sensitive to algorithm and hardware assumptions |
| Small-scale quantum simulation experiments | Proof of principle on simple molecules | Not yet beyond classical reach for useful problems |
| Experimental materials characterization | Ground truth for validation | Experiments are costly and not always available |
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.