GCRI · Quantum Technologies
Quantum and materials
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.
How the work runs.
Quantum testbed
Quantum and materials · Testbed lab · Device QD-77
Qubits
| 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 |
Device
- Topology
- heavy-hex, illustrative
- 2Q fidelity
- 0.989
- Access
- by committee
- Label
- OBSERVED
Fig.Quantum testbed: example names and illustrative values, not a record or a live system.
Quantum and materials: how the risk develops.
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.
Defects cause decoherence
Impurities, surface defects and two-level systems in materials interact with qubits and shorten coherence. Better materials and fabrication extend it.
Fabrication sets yield
Producing many identical qubits needs precise fabrication. Variability between devices, even from one wafer, limits how far systems can scale.
Some inputs are scarce
Helium-3 for dilution refrigerators, isotopically enriched silicon and specialised lasers have limited suppliers, creating supply risks.
Quantum simulation of matter
Simulating molecules and materials is a leading candidate use of quantum computers, though classical methods remain strong for most problems today.
How quantum and materials 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.
System model
The couplings between the systems in quantum and materials: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Quantum devices depend on materials with their own supply risks.
Materials studies are designed for the Strategic Systems series. Export controls coordinated through the Wassenaar Arrangement and national lists apply to some quantum items.
- Supply data for specialised materials.
- Export control analyses.
What is measured, and how well.
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 |
Where the evidence comes from.
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 |
Who decides, and what the evidence serves.
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 |
Outputs this topic produces
- Materials bottleneck reviews for qubit platforms
- Supply risk assessments for specialised inputs
- Assessments of quantum simulation use cases with classical baselines
- Evidence notes for research funders
What experts ask.
The question is whether quantum simulation will beat improving classical methods on problems that matter.
- Which chemical and materials problems are genuinely hard for classical methods?
- How large and how error-free must a quantum computer be to simulate them usefully?
- Can near-term noisy devices provide any useful result, or is error correction required?
- How will results be verified when classical methods cannot reproduce them?
- Which industrial and scientific questions would change if simulation improved?
Mechanisms and drivers.
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.
How it is studied, and where the methods stop.
| 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 characterisation | Ground truth for validation | Experiments are costly and not always available |
How it is studied, and what the methods cannot say.
- 01Materials science and chemistryMaterials for longer coherence · Fabrication variability · Quantum chemistry use casesApplied here
- 02Physics and quantum scienceCharacterising qubit noise · Metrology of quantum clocks · Verifying quantum effectsApplied here
- 03Quantum computing, sensing and communicationComparable benchmarking · Technology readiness assessment · Security implications trackingApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Why do materials matter for qubits?
What is quantum simulation?
Are there supply risks for quantum technology?
Has a quantum computer already designed a new material or drug?
Why is chemistry a natural application?
Should organisations wait for quantum simulation?
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