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GCRI · 量子技術

量子と材料

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.

01キャンバス

作業の進め方。

システム ・この主題の周辺説明用

Quantum testbed

Quantum and materials · Testbed lab · Device QD-77

Calibration cycleGCRI・量子技術

量子ビット

Q01421160.02In spec
Q183480.01In spec
Q269990.01Drifting
Q3142210.05Drifting
Q4162830.03Drifting

Device

Topology
heavy-hex, illustrative
2Q fidelity
0.989
アクセス
by committee
ラベル
観測
Quantum testbed: the screen’s structure, terms and states for Quantum and materials.例示の名称と説明用の値です。設計であり、記録でも稼働中のシステムでもありません。

図Quantum testbed: example names and illustrative values, not a record or a live system.

02質問

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.

メカニズム

Defects cause decoherence

Impurities, surface defects and two-level システム in materials interact with qubits and shorten coherence. Better materials and fabrication extend it.

メカニズム

Fabrication sets yield

Producing many identical qubits needs precise fabrication. Variability between devices, even from one wafer, limits how far systems can scale.

メカニズム

Some inputs are scarce

Helium-3 for dilution refrigerators, isotopically enriched silicon and specialised lasers have limited suppliers, creating supply risks.

メカニズム

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.

03手法とエビデンス

How quantum and materials is modelled, measured and tested.

モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。

システム・モデル

The couplings between the systems in quantum and materials: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−+量子サイバーコミュニティ経済R 自己強化B バランス
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
FAIR 原則・GO FAIR と FORCE11W3C PROV・World Wide Web Consortium不確実性の一貫した扱いに関するガイダンス・IPCCCEOS 解析即応データ・地球観測衛星委員会
04分野を横断して

Quantum devices depend on materials with their own supply risks.

05指標

何を、どの程度うまく測定しているか。

各指標には、その単位、更新頻度、背後にある出典の種類、値が持つことになるエビデンスのラベルが付いています。値は表示しません:プログラムが自らの権限のもとで値を提供します。

指標単位周期出典の種類ラベル
Qubit coherence time by material platformmicrosecondsper publicationPeer-reviewed results観測
Device yield in fabricationpercent of functional qubitsper fabrication runFoundry and lab reports報告値
Helium-3 supplylitres per year年次Government and industry reports推定
Quantum chemistry demonstrations with classical comparison件数レビューごとLiterature review推定
Specialised component supplierscount by component評価ごとSupply chain studies推定
Materials databases used for qubit research件数評価ごとResearch surveys報告値
06出典

エビデンスの出所。

名前の明示された公開データセットと機関の記録。それぞれにライセンス区分があります。合意に基づく出典は、その条件のもとでのみ使用し、再公開することはありません。

出典提供するものライセンス区分
Peer-reviewed materials and device literatureCoherence and fabrication resultsLicence per article
Materials Project and other open materials databasesComputed materials propertiesオープン、CC BY 4.0
Government reports on helium-3 and critical inputsSupply and demand of scarce inputsPublic, terms per government
Quantum supply chain studiesComponent suppliers and dependenciesLicence per publication (to check)
Benchmark studies of quantum chemistry algorithmsComparisons with classical methodsLicence per publication
07決定

誰が決め、エビデンスが何に役立つか。

Quantum Technologies prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

誰が決定するか質問このページが提供するエビデンス
研究資金提供者Which materials and fabrication research to supportBottleneck reviews
Industry and national ラボHow to secure specialised inputsSupply risk assessments
Chemistry and materials researchersWhere quantum simulation could help their workUse case assessments

このトピックが生み出す成果物

  • 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
08中核となる問い

専門家が問うこと。

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?
09メカニズム

メカニズムと要因。

The behaviour of molecules and materials depends on how electrons interact. For many システム, 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.

10手法とデータ

どのように研究され、手法がどこで止まるか。

手法またはデータ表示内容限界
Classical computational chemistryAccurate results for many システムCost grows quickly for strongly correlated electrons
Quantum algorithm resource estimationQubits and operations needed for target problemsHighly sensitive to algorithm and hardware assumptions
Small-scale quantum simulation experimentsProof of principle on simple moleculesNot yet beyond classical reach for useful problems
Experimental materials characterisationGround truth for validationExperiments are costly and not always available
11手法

どのように研究され、手法が言えないことは何か。

12Where this page’s references

Where this page’s references lead.

このページの読解の基準となる機関と文書。状態と版は変わります。各リンクは発行機関へ移動します。

13質問

質問と回答。

Why do materials matter for qubits?
Because defects and impurities in materials disturb qubits and shorten coherence, so better materials and fabrication directly improve device performance.
What is quantum simulation?
Using a controllable quantum system or quantum computer to model another quantum system, such as a molecule or material.
Are there supply risks for quantum technology?
Some inputs, such as helium-3 and certain lasers and isotopes, have few suppliers, which supply chain studies identify as risks.
Has a quantum computer already designed a new material or drug?
Not in a way that classical methods could not match. Current demonstrations are proofs of principle on small systems.
Why is chemistry a natural application?
Because molecules are quantum systems. Simulating quantum systems with quantum hardware avoids some of the exponential costs classical computers face.
Should organisations wait for quantum simulation?
That is their decision. Evidence suggests classical methods will remain the workhorse for most problems for some time, while research into quantum methods continues.
報告書貢献する

このページからエビデンスを提供する。

報告書 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRI量子技術指数関数的テクノロジーフォーサイト・アウトルック
量子技術

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