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市場調查報告書
商品編碼
1696258

全球金融服務市場中的量子運算 - 2025 年至 2032 年

Global Quantum Computing in Financial Services Market - 2025-2032
出版日期: | 出版商: DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球金融服務市場的量子運算規模達到 3 億美元，預計到 2032 年將達到 63 億美元，在 2025-2032 年預測期內的複合年成長率為 46.5%。

量子運算時代即將到來，金融服務業應該做好相應的準備。硬體技術的資本投資和專利申請的增加表明，預計未來幾年對量子相關能力的支出將迅速增加。量子電腦可以以傳統系統無法想像的速度進行計算。這種能力使得在毫秒級的高頻交易環境中能夠快速做出決策，為早期採用者帶來競爭優勢。

一些金融機構已經在研究量子計算的可能性。高盛與亞馬遜網路服務 (AWS) 合作研究量子解決方案如何改善衍生性商品定價和投資組合最佳化。這些項目旨在提高效率和盈利能力。此外，匯豐銀行正在與 IBM 合作，研究使用量子演算法提高營運效率，專注於風險管理、詐欺檢測和法規遵從性。此次合作體現了金融機構與科技巨頭之間的日益融合。

動態的

超導量子位元的進展

金融服務採用量子運算硬體的主要驅動力之一是超導量子位元技術的快速進步，該技術可實現更快、更有效率的量子運算。 IBM、Google和 Rigetti Computing 等企業所採用的超導量子位元正變得更加穩定，具有更好的糾錯機制和更長的相干持續時間，使其更適合複雜的金融建模。

例如，IBM 的 Eagle 處理器（127 量子位元）和 Osprey（433 量子位元）在運算能力方面已顯示出顯著的提升，使金融公司能夠更有效地執行量子模擬，以進行風險評估、投資組合最佳化和詐欺檢測。隨著這些改進的不斷進行，金融機構將逐步採用量子設備來獲得高頻交易、資產定價和加密安全的競爭優勢。

成本高且商業可行性有限

金融服務採用量子運算技術的最大障礙之一是開發、維護和部署的成本高昂。建造和操作量子電腦需要極低的溫度（接近絕對零度）、專門的超導材料和大量的能源，這使得它們成本高昂且難以擴大規模。

例如，IBM 的 Quantum System One 和 D-Wave 的 Advantage 量子電腦需要極其專業化的低溫系統和基礎設施，這限制了它們的普遍使用。希望使用量子運算的金融機構必須在硬體、專業技能和量子演算法方面進行大量投資，這對中型企業來說可能是一個巨大的障礙。在該技術變得更具經濟可行性和成本效益之前，其在金融服務領域的使用將僅限於大型企業和研究公司。

目錄

第 1 章：方法與範圍

第 2 章：定義與概述

第 3 章：執行摘要

第 4 章：動態

  • 影響因素
    • 驅動程式
      • 超導量子位元的進展
    • 限制
      • 成本高且商業可行性有限
    • 機會
    • 影響分析

第5章：產業分析

  • 波特五力分析
  • 供應鏈分析
  • 價值鏈分析
  • 定價分析
  • 監理與合規性分析
  • 人工智慧與自動化影響分析
  • 研發與創新分析
  • 永續性與綠色技術分析
  • 網路安全分析
  • 下一代技術分析
  • 技術路線圖
  • DMI 意見

第 6 章：奉獻

  • 硬體
  • 軟體
  • 服務

第 7 章：按部署類型

  • 本地
  • 基於雲端

第 8 章：按技術

  • 量子點
  • 捕獲離子
  • 量子退火

第9章：按應用

  • 公司銀行
  • 風險與網路安全
  • 零售銀行
  • 付款
  • 資產及財富管理
  • 投資銀行
  • 其他

第 10 章：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 歐洲其他地區
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 亞太其他地區
  • 中東和非洲

第 11 章：競爭格局

  • 競爭格局
  • 市場定位/佔有率分析
  • 併購分析

第 12 章：公司簡介

  • IBM Corporation
    • 公司概況
    • 產品組合和描述
    • 財務概覽
    • 關鍵進展
  • 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：附錄

簡介目錄
Product Code: ICT9417

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