金融サービス向け量子コンピューティングの世界市場（2025年～2032年）

世界の金融サービス向け量子コンピューティングの市場規模は、2024年に3億米ドルに達し、2032年までに63億米ドルに達すると予測され、予測期間の2025年～2032年にCAGRで46.5%の成長が見込まれます。

量子コンピューティングの時代が急速に到来しており、金融サービス産業はそれに合わせて準備する必要があります。ハードウェア技術への設備投資や特許出願が増加していることから、量子関連機能への支出は今後数年間で急速に増加すると予測されます。量子コンピューターは、古典的なシステムでは計り知れないスピードで計算を行うことができます。この能力は、ミリ秒単位の取引が行われる高頻度取引環境において迅速な意思決定を可能にし、いち早く導入した金融機関は競合優位性を得ることができます。

すでに複数の金融機関が量子コンピューティングの可能性を調査しています。Goldman SachsはAmazon Web Services（AWS）と提携し、量子ソリューションが金融派生商品の価格設定やポートフォリオの最適化をどのように改善するかを調査しています。これらのプロジェクトは、効率性と収益性の向上を目指しています。さらに、HSBCはIBMと協力し、リスク管理、不正検知、規制遵守に重点を置き、量子アルゴリズムを用いた業務効率の調査を行っています。この提携は、金融機関とハイテク企業の融合が進んでいることを示しています。

力学

超伝導量子ビットの進歩

金融サービス向け量子コンピューティングハードウェア採用の主な促進要因の1つは、より迅速で効率的な量子計算を可能にする超伝導量子ビット技術の急速な進歩です。IBM、Google、Rigetti Computingなどの企業が採用している超伝導量子ビットは、エラー訂正メカニズムが向上し、コヒーレンス時間が長くなるなど安定性が増しており、複雑な金融モデリングに適しています。

例えば、IBMのEagleプロセッサー（127量子ビット）やOsprey（433量子ビット）の計算能力は大幅に向上しており、金融機関はリスク評価、ポートフォリオの最適化、不正行為の検知に量子シミュレーションをより効果的に実行できるようになっています。このような改良が進むにつれ、金融機関は高頻度取引、資産の価格決定、暗号セキュリティにおいて競争力を得るため、量子関連機器を徐々に導入していくとみられます。

高いコストと限られた商業的実現可能性

量子コンピューター技術の金融サービスへの導入におけるもっとも大きな障壁の1つは、開発、メンテナンス、展開にかかる高いコストです。量子コンピューターの構築と運用には、極低温（絶対零度に近い）、特殊な超伝導材料、大規模なエネルギー資源が必要であり、コストが高く、規模の拡大が難しいです。

例えば、IBMのQuantum System OneやD-WaveのAdvantage量子コンピューターは、極めて特殊な極低温システムとインフラを必要とするため、一般的な利用は制限されています。量子コンピューティングを利用しようとする金融機関は、ハードウェア、専門スキル、量子対応アルゴリズムにかなりの投資を行わなければならず、中堅企業にとっては大きなハードルとなりえます。この技術が経済的に実現可能になり、費用対効果が高まるまでは、金融サービスでの利用は大手企業や研究企業に限られる見込みです。

当レポートでは、世界の金融サービス向け量子コンピューティング市場について調査し、市場力学、地域とセグメントの分析、競合情勢、企業プロファイルなどを提供しています。

目次

第1章 調査手法と範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

• 影響要因
  • 促進要因
    • 超伝導量子ビットの進歩
  • 抑制要因
    • 高いコストと限られた商業的実現可能性
  • 機会
  • 影響の分析

第5章 産業の分析

• ポーターのファイブフォース分析
• サプライチェーン分析
• バリューチェーン分析
• 価格分析
• 規制とコンプライアンスの分析
• AIと自動化の影響の分析
• 研究開発とイノベーションの分析
• 持続可能性とグリーンテクノロジーの分析
• サイバーセキュリティの分析
• 次世代技術の分析
• 技術ロードマップ
• DMIの見解

第6章 提供別

• ハードウェア
• ソフトウェア
• サービス

第7章 展開タイプ別

• オンプレミス
• クラウドベース

第8章 技術別

• 量子ドット
• イオントラップ
• 量子アニーリング

第9章 用途別

• コーポレートバンキング
• リスク・サイバーセキュリティ
• リテールバンキング
• 決済
• 資産管理・資産運用
• 投資銀行業務
• その他

第10章 地域別

• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • イタリア
  • スペイン
  • その他の欧州
• 南米
  • ブラジル
  • アルゼンチン
  • その他の南米
• アジア太平洋
  • 中国
  • インド
  • 日本
  • オーストラリア
  • その他のアジア太平洋
• 中東・アフリカ

第11章 競合情勢

• 競合シナリオ
• 市場ポジショニング/シェア分析
• 合併と買収の分析

第12章 企業プロファイル

• IBM Corporation
• Intel Corporation
• IonQ Inc.
• Silicon Quantum Computing
• Huawei Technologies Co. Ltd
• Alphabet Inc.
• Rigetti & Co, LLC
• Microsoft Corporation
• D-Wave Quantum Inc
• Zapata Computing Inc

第13章 付録

Global Quantum Computing in Financial Services Market reached US$ 0.3 billion in 2024 and is expected to reach US$ 6.3 billion by 2032, growing with a CAGR of 46.5% during the forecast period 2025-2032.

The age of quantum computing is fast arriving and the financial services industry should prepare accordingly. Increased capital investments and patent applications for hardware technology indicate that spending on quantum-related capabilities is anticipated to increase rapidly in the next years. Quantum computers can do calculations at speeds unfathomable for classical systems. This capacity allows for speedy decision-making in high-frequency trading environments where milliseconds count, giving early adopters a competitive advantage.

Several financial institutions are already investigating the possibilities of quantum computing. Goldman Sachs has teamed with Amazon Web Services (AWS) to examine how quantum solutions might improve derivative pricing and portfolio optimization. These projects aim to increase efficiency and profitability. Furthermore, HSBC is working with IBM to investigate operational efficiency using quantum algorithms, with an emphasis on risk management, fraud detection and regulatory compliance. This collaboration demonstrates the growing convergence between financial institutions and tech titans.

Dynamic

Advancements in Superconducting Qubits

One of the primary drivers of quantum computing hardware adoption in financial services is the rapid progress of superconducting qubit technology, which allows for quicker and more efficient quantum computations. Superconducting qubits, which are employed by businesses such as IBM, Google and Rigetti Computing, are becoming more stable, with better error correction mechanisms and longer coherence durations, making them more suitable for complicated financial modeling.

For example, IBM's Eagle processor (127 qubits) and Osprey (433 qubits) have shown considerable gains in computational capacity, allowing financial firms to execute quantum simulations for risk assessment, portfolio optimization and fraud detection more effectively. As these improvements continue, financial organizations will progressively embrace quantum gear to obtain a competitive edge in high-frequency trading, asset pricing and cryptographic security.

High Costs and Limited Commercial Viability

One of the most significant barriers to the adoption of quantum computing technology for financial services is the high cost of development, maintenance and deployment. Building and operating quantum computers necessitates extremely low temperatures (near absolute zero), specialized superconducting materials and large energy resources, making them costly and difficult to scale.

For example, IBM's Quantum System One and D-Wave's Advantage quantum computers require extremely specialized cryogenic systems and infrastructure, restricting their general use. Financial organizations wishing to use quantum computing must make considerable investments in hardware, specialist skills and quantum-ready algorithms, which can be a big hurdle for mid-sized businesses. Until the technology becomes more economically feasible and cost-effective, usage in financial services will be limited to major corporations and research companies.

Segment Analysis

The global quantum computing in financial services market is segmented based on offering, deployment type, technology, application and region.

Advancements in Hardware Enhancing Computational Power

Rapid developments in quantum hardware are a major driver of quantum computing usage in financial services. Leading businesses such as IBM, Google and Rigetti Computing are constantly upgrading quantum processors, increasing the number of qubits while decreasing error rates. The gains are critical for financial applications such as risk modeling, portfolio optimization and fraud detection, which require significant computer capacity to efficiently process big datasets.

For example, IBM's Eagle processor, which has 127 qubits, has shown considerable gains in quantum computation, making complicated financial simulations possible. Similarly, Google's Sycamore quantum processor has demonstrated the ability to accomplish calculations that would take classical supercomputers thousands of years. As quantum hardware advances with increased qubit stability and better error correction, financial institutions increasingly use quantum computing, fueling industry expansion.

Geographical Penetration

Growing Demand for Advanced Risk Management and Fraud Detection in North America

The increasing complexity of financial markets, combined with the growing threat of cyber fraud, is propelling the deployment of quantum computing in financial services across North America. Traditional computing methods struggle to detect real-time fraud and analyze complicated risks, particularly in high-frequency trading and financial modeling. Quantum algorithms, such as those created by IBM and D-Wave, allow financial firms to examine massive information at unprecedented rates, detecting fraudulent transactions and market risks more quickly.

For example, JPMorgan Chase has been aggressively researching quantum computing for portfolio optimization and risk management, using quantum capabilities to improve Monte Carlo simulations, which are critical for predicting financial market volatility. As financial organizations in the US and Canada seek faster and more accurate decision-making tools, demand for quantum computing in the financial sector is likely to expand, making it an important market driver.

Sustainability Analysis

The integration of quantum computing in financial services presents both sustainability opportunities and challenges. On the positive side, quantum computing has the potential to significantly reduce energy consumption compared to traditional supercomputers for complex financial modeling, risk assessment and fraud detection. Since quantum processors can handle computations exponentially faster, they require fewer computational resources to achieve the same or superior results, contributing to lower energy consumption over time.

The materials required for quantum processors, such as superconducting materials and rare-earth elements, present supply chain and environmental impact challenges. To address these concerns, companies like IBM, Google and D-Wave are focusing on energy-efficient quantum architectures and exploring alternatives such as room-temperature quantum computing. As financial institutions adopt quantum solutions, ensuring sustainable hardware development and responsible energy usage will be crucial to minimizing the environmental impact of this emerging technology.

Competitive Landscape

The major global players in the market include IBM Corporation, Intel Corporation, IonQ Inc., Silicon Quantum Computing, Huawei Technologies Co. Ltd, Alphabet Inc., Rigetti & Co, LLC, Microsoft Corporation, D-Wave Quantum Inc and Zapata Computing Inc.

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Target Audience 2024

• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies

Table of Contents

1. Methodology and Scope

• 1.1. Research Methodology
• 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

• 3.1. Snippet by Offering
• 3.2. Snippet by Deployment Type
• 3.3. Snippet by Technology
• 3.4. Snippet by Application
• 3.5. Snippet by Region

4. Dynamics

• 4.1. Impacting Factors
  • 4.1.1. Drivers
    • 4.1.1.1. Advancements in Superconducting Qubits
  • 4.1.2. Restraints
    • 4.1.2.1. High Costs and Limited Commercial Viability
  • 4.1.3. Opportunity
  • 4.1.4. Impact Analysis

5. Industry Analysis

• 5.1. Porter's Five Force Analysis
• 5.2. Supply Chain Analysis
• 5.3. Value Chain Analysis
• 5.4. Pricing Analysis
• 5.5. Regulatory and Compliance Analysis
• 5.6. AI & Automation Impact Analysis
• 5.7. R&D and Innovation Analysis
• 5.8. Sustainability & Green Technology Analysis
• 5.9. Cybersecurity Analysis
• 5.10. Next Generation Technology Analysis
• 5.11. Technology Roadmap
• 5.12. DMI Opinion

6. By Offering

• 6.1. Introduction
  • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 6.1.2. Market Attractiveness Index, By Offering
• 6.2. Hardware*
  • 6.2.1. Introduction
  • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 6.3. Software
• 6.4. Service

7. By Deployment Type

• 7.1. Introduction
  • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 7.1.2. Market Attractiveness Index, By Deployment Type
• 7.2. On-premises*
  • 7.2.1. Introduction
  • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 7.3. Cloud-based

8. By Technology

• 8.1. Introduction
  • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 8.1.2. Market Attractiveness Index, By Technology
• 8.2. Quantum Dots*
  • 8.2.1. Introduction
  • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 8.3. Trapped Ions
• 8.4. Quantum Annealing

9. By Application

• 9.1. Introduction
  • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 9.1.2. Market Attractiveness Index, By Application
• 9.2. Corporate Banking*
  • 9.2.1. Introduction
  • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
• 9.3. Risk & Cybersecurity
• 9.4. Retail Banking
• 9.5. Payments
• 9.6. Asset & Wealth Management
• 9.7. Investment Banking
• 9.8. Others

10. By Region

• 10.1. Introduction
  • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
  • 10.1.2. Market Attractiveness Index, By Region
• 10.2. North America
  • 10.2.1. Introduction
  • 10.2.2. Key Region-Specific Dynamics
  • 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 10.2.7.1. U.S.
    • 10.2.7.2. Canada
    • 10.2.7.3. Mexico
• 10.3. Europe
  • 10.3.1. Introduction
  • 10.3.2. Key Region-Specific Dynamics
  • 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 10.3.7.1. Germany
    • 10.3.7.2. UK
    • 10.3.7.3. France
    • 10.3.7.4. Italy
    • 10.3.7.5. Spain
    • 10.3.7.6. Rest of Europe
• 10.4. South America
  • 10.4.1. Introduction
  • 10.4.2. Key Region-Specific Dynamics
  • 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 10.4.7.1. Brazil
    • 10.4.7.2. Argentina
    • 10.4.7.3. Rest of South America
• 10.5. Asia-Pacific
  • 10.5.1. Introduction
  • 10.5.2. Key Region-Specific Dynamics
  • 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
  • 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
    • 10.5.7.1. China
    • 10.5.7.2. India
    • 10.5.7.3. Japan
    • 10.5.7.4. Australia
    • 10.5.7.5. Rest of Asia-Pacific
• 10.6. Middle East and Africa
  • 10.6.1. Introduction
  • 10.6.2. Key Region-Specific Dynamics
  • 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
  • 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
  • 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
  • 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

11. Competitive Landscape

• 11.1. Competitive Scenario
• 11.2. Market Positioning/Share Analysis
• 11.3. Mergers and Acquisitions Analysis

12. Company Profiles

• 12.1. IBM Corporation*
  • 12.1.1. Company Overview
  • 12.1.2. Product Portfolio and Description
  • 12.1.3. Financial Overview
  • 12.1.4. Key Developments
• 12.2. Intel Corporation
• 12.3. IonQ Inc.
• 12.4. Silicon Quantum Computing
• 12.5. Huawei Technologies Co. Ltd
• 12.6. Alphabet Inc.
• 12.7. Rigetti & Co, LLC
• 12.8. Microsoft Corporation
• 12.9. D-Wave Quantum Inc
• 12.10. Zapata Computing Inc

LIST NOT EXHAUSTIVE

13. Appendix

• 13.1. About Us and Services
• 13.2. Contact Us