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封面
市场调查报告书
商品编码
1696258

全球金融服务市场中的量子运算 - 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个工作天内

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简介目录

2024 年全球金融服务市场的量子运算规模达到 3 亿美元，预计到 2032 年将达到 63 亿美元，在 2025-2032 年预测期内的复合年增长率为 46.5%。

量子运算时代即将到来，金融服务业应该做好相应的准备。硬体技术的资本投资和专利申请的增加表明，预计未来几年对量子相关能力的支出将迅速增加。量子电脑可以以传统系统无法想像的速度进行计算。这种能力使得在毫秒级的高频交易环境中能够快速做出决策，为早期采用者带来竞争优势。

一些金融机构已经在研究量子计算的可能性。高盛与亚马逊网路服务 (AWS) 合作研究量子解决方案如何改善衍生性商品定价和投资组合最佳化。这些项目旨在提高效率和盈利能力。此外，汇丰银行正在与 IBM 合作，研究使用量子演算法提高营运效率，专注于风险管理、诈欺检测和法规遵从性。此次合作体现了金融机构与科技巨头之间的日益融合。

动态的

超导量子位元的进展

金融服务采用量子运算硬体的主要驱动力之一是超导量子位元技术的快速进步，该技术可实现更快、更有效率的量子运算。 IBM、Google和 Rigetti Computing 等企业所采用的超导量子位元正变得更加稳定，具有更好的纠错机制和更长的相干持续时间，使其更适合复杂的金融建模。

例如，IBM 的 Eagle 处理器（127 量子位元）和 Osprey（433 量子位元）在运算能力方面已显示出显着的提升，使金融公司能够更有效地执行量子模拟，以进行风险评估、投资组合优化和诈欺检测。随着这些改进的不断进行，金融机构将逐步采用量子设备来获得高频交易、资产定价和加密安全的竞争优势。

成本高且商业可行性有限

金融服务采用量子运算技术的最大障碍之一是开发、维护和部署的成本高昂。建造和操作量子电脑需要极低的温度（接近绝对零度）、专门的超导材料和大量的能源，这使得它们成本高昂且难以扩大规模。

例如，IBM 的 Quantum System One 和 D-Wave 的 Advantage 量子电脑需要极其专业化的低温系统和基础设施，这限制了它们的普遍使用。希望使用量子运算的金融机构必须在硬体、专业技能和量子演算法方面进行大量投资，这对中型企业来说可能是一个巨大的障碍。在该技术变得更具经济可行性和成本效益之前，其在金融服务领域的使用将仅限于大型企业和研究公司。

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

  • 影响因素
    • 驱动程式
      • 超导量子位元的进展
    • 限制
      • 成本高且商业可行性有限
    • 机会
    • 影响分析

第五章：产业分析

  • 波特五力分析
  • 供应链分析
  • 价值链分析
  • 定价分析
  • 监理与合规性分析
  • 人工智慧与自动化影响分析
  • 研发与创新分析
  • 永续性与绿色技术分析
  • 网路安全分析
  • 下一代技术分析
  • 技术路线图
  • DMI 意见

第 6 章：奉献

  • 硬体
  • 软体
  • 服务

第 7 章：按部署类型

  • 本地
  • 基于云端

第 8 章：按技术

  • 量子点
  • 捕获离子
  • 量子退火

第九章：按应用

  • 公司银行
  • 风险与网路安全
  • 零售银行
  • 付款
  • 资产及财富管理
  • 投资银行
  • 其他的

第 10 章：按地区

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 亚太其他地区
  • 中东和非洲

第 11 章：竞争格局

  • 竞争格局
  • 市场定位/份额分析
  • 併购分析

第 12 章：公司简介

  • IBM Corporation
    • 公司概况
    • 产品组合和描述
    • 财务概览
    • 关键进展
  • 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：附录

简介目录
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