양자 기계학습(QML) 소프트웨어 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the quantum machine learning (QML) software market size was valued at USD 0.48 billion in 2025 and estimated to grow from USD 0.62 billion in 2026 to reach USD 2.49 billion by 2031, at a CAGR of 32.06% during the forecast period 2026-2031.

This report is Segmented by Solution (Software Platforms, and Services), Deployment (Cloud-Based, Hybrid, and On-Premises), Organization Size (Large Enterprises, and Small and Medium Enterprises), Application (Optimization, and More), End-User Industry (BFSI, Education and Research Institutions, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Quantum Machine Learning (QML) Software Market Trends and Insights

Enterprise Demand for Hybrid Quantum-Classical Workflows

The Quantum Machine Learning (QML) Software Market is benefiting from stronger enterprise demand for hybrid workflows, as software orchestration determines whether a quantum task can be used in a real production environment. Most near-term machine learning and optimization use cases still need repeated classical processing before and after the quantum step, which makes workflow design more important than raw qubit counts at this stage. Quantinuum's commercial launch of Helios in November 2025 clearly demonstrated this, as the system was introduced alongside Guppy, a Python-based programming language, and the Nexus cloud platform for hybrid computing, with early customer use cases in drug discovery, materials research, and financial analytics. This pattern is creating a larger role for middleware vendors that can translate existing data structures, model logic, and process flows into quantum-compatible formats without forcing a full redesign of enterprise systems. As a result, the Quantum Machine Learning (QML) Software Market is moving toward products that connect quantum experimentation with standard artificial intelligence, analytics, and research operations, rather than treating quantum computing as a stand-alone environment. Vendors that make this connection easier are likely to stay better positioned as buyers look for repeatable value rather than isolated demonstrations.

Rising Need for Quantum-Safe Optimization in High-Complexity Use Cases

The Quantum Machine Learning (QML) Software Market is also supported by demand from sectors where large-scale optimization problems are becoming harder to solve within practical business time frames using only classical methods. Logistics planning, financial portfolio design, derivative pricing, and pharmaceutical screening all require decision structures that become more difficult as the number of variables, constraints, and possible outcomes increases. D-Wave stated in its first-quarter 2026 results that its Stride hybrid solver now supports surrogate machine learning integration, indicating that customers are already moving toward more adaptive industrial optimization workflows. A second factor is that mathematically structured optimization pathways are easier to review and explain than black-box prediction models, which matters in banking, healthcare, and other regulated settings. This is helping the Quantum Machine Learning (QML) Software Market because quantum-assisted optimization is being evaluated not only as a performance tool, but also as a way to improve traceability and decision accountability. That broader value proposition is making software adoption more relevant in sectors that must balance computational performance with oversight requirements.

Limited Commercial Scale of Fault-Tolerant Quantum Hardware

The Quantum Machine Learning (QML) Software Market still faces a major restraint because commercially meaningful fault-tolerant quantum hardware is not yet available at the scale needed for broad enterprise use. That keeps software vendors tied to noisy intermediate-scale systems, where products must be designed around error rates, limited coherence windows, and narrow performance conditions rather than around the full theoretical benefits of error-corrected computing. IBM stated that it expects quantum advantage by the end of 2026 and fault tolerance by 2029, while Microsoft has pointed to 2029 as a path toward scalable systems through its Majorana 2 work. This delay shifts competition in the Quantum Machine Learning (QML) Software Market, as vendors that manage noise, compilation, and hardware-aware execution gain greater relevance than those that focus solely on algorithmic theory. It also erodes buyer confidence because many organizations still want proof that software can generate value before the supporting hardware reaches a more mature, stable phase. Until that gap narrows, the software layer will continue growing, but it will do so with more testing, benchmarking, and caution than a fully mature hardware environment would allow.

Other drivers and restraints analyzed in the detailed report include:

1. Rapid Expansion of Cloud Access to Quantum Development Environments
2. Regulatory and Public Funding Support for Quantum Software Ecosystems
3. High Integration Cost With Legacy Enterprise Data and MLOps Stacks

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software platforms commanded 72.41% of the Quantum Machine Learning (QML) Software Market share in 2025, which reflected enterprise preference for integrated development toolkits, simulation environments, and algorithm design software over narrower point products. Buyers favored these platforms because they could manage code development, workflow testing, benchmarking, and backend access in a single environment, reducing friction during the early shift from proof-of-concept work to limited production use. IBM strengthened this platform pattern in July 2026 when it released Qiskit v2.5 with a multi-representation compiler framework and dedicated fault-tolerant compilation pipelines, which made it easier for developers to work across near-term and future architectures in the same codebase. Simulation software also gained strategic value as enterprise teams increasingly sought side-by-side comparisons of quantum and classical performance before expanding budgets. Algorithm design environments added another layer of demand by enabling users to explore problem formulation without requiring deep expertise in quantum physics or low-level circuit design.

Services is the fastest-growing solution segment, with the Quantum Machine Learning (QML) Software Market size for services projected to expand at a 35.82% CAGR between 2026 and 2031. This growth shows that many buyers still lack the internal talent to handle model design, workflow integration, benchmarking, and in-house deployment support. Advisory, implementation, and managed deployment work is becoming more valuable as organizations move from initial experimentation to projects tied to business outcomes or scientific targets. Service demand is especially strong in pharmaceuticals and financial services, where engagements are often linked to molecular simulation, portfolio construction, fraud detection, or optimization quality rather than open-ended testing. A related shift is the move toward outcome-based contracts, as buyers increasingly want providers to stand behind measurable improvements in optimization or modeling gains rather than billing only by time spent. That change gives an edge to firms with greater domain depth, because the Quantum Machine Learning (QML) Software Market rewards service providers that can combine technical delivery with industry-specific application knowledge.

Cloud-based deployment held a 68.24% share in 2025, indicating that the Quantum Machine Learning (QML) Software Market still relied mainly on remote access models that allow users to reach quantum backends via large cloud environments. This model stayed attractive because it removed the need for capital spending on specialized systems and gave enterprises an easier way to compare tools, providers, and hardware types before making larger commitments. Amazon Braket's August 2025 program, which supported this position by reducing execution time on compatible workloads, improved the handling of repeated circuit runs, often required in training and optimization workflows. Cloud-based delivery also gave vendors a more practical way to distribute updates, benchmarking features, and managed access to multiple processors. That combination kept cloud delivery ahead because immediate availability and lower entry barriers mattered more to most buyers than direct local control.

Hybrid deployment is projected to grow at a 34.19% CAGR through 2031, making it the fastest-growing deployment mode in the Quantum Machine Learning (QML) Software Market. This shift reflects a more deliberate architecture in which enterprises route different workflow steps across classical central processing units, graphics processing units, and quantum processors based on latency tolerance, data sensitivity, and cost efficiency. PASQAL's March 2026 CUDA-Q integration demonstrated that quantum processing can fit within standard high-performance computing scheduling patterns rather than sitting outside existing compute operations. Hybrid design is gaining relevance because most practical use cases still rely on repeated classical optimization, parameter updates, and data preparation around the quantum portion of the task. On-premises deployment remains smaller, but it still has a role in government and regulated financial settings where security rules, sovereignty concerns, or network restrictions limit cloud usage. Over time, the Quantum Machine Learning (QML) Software Market is likely to treat hybrid architecture as a standard operating model rather than as a temporary transition stage.

Complete Report Scope:

• By Solution
  • Software Platforms
    • Development Tools And SDKs
    • Simulation And Benchmarking Software
    • Algorithm Design And Optimization Software
  • Services
• By Deployment
  • Cloud-Based
  • Hybrid
  • On-Premises
• By Organization Size
  • Large Enterprises
  • Small and Medium Enterprises
• By Application
  • Optimization
  • Drug Discovery and Life Sciences
  • Materials Science and Quantum Chemistry
  • Financial Analytics and Risk Modeling
  • Cryptography and Cybersecurity
  • Other Applications
• By End-User Industry
  • IT and Telecommunication
  • BFSI
  • Healthcare and Life Sciences
  • Industrial Manufacturing
  • Education and Research Institutions
  • Government and administration
  • Energy and Utilities
  • Other End-User Industries
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Russia
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • South Korea
    • Southeast Asia
    • Rest of Asia-Pacific
  • Middle East and Africa
    • Middle East
      • Saudi Arabia
      • United Arab Emirates
      • Turkey
      • Rest of Middle East
    • Africa
      • South Africa
      • Nigeria
      • Egypt
      • Rest of Africa

Geography Analysis

North America held 38.62% of the Quantum Machine Learning (QML) Software Market share in 2025, making it the largest regional center for software adoption, platform development, and enterprise procurement. The region benefited from a dense mix of hyperscaler cloud infrastructure, specialist vendors, venture activity, and enterprise users that were already prepared to test advanced computational tools. Public funding also reinforced that lead in 2026 through the U.S. Department of Commerce incentive package and the White House executive order on quantum commercialization and performance assessment. These actions matter because they support hardware, software, benchmarking, and procurement structures simultaneously. Canada added meaningful activity through companies such as Xanadu Quantum Technologies and 1QBit, while Mexico remained at an earlier stage of enterprise adoption. Taken together, these factors gave North America the most complete commercial environment in the Quantum Machine Learning (QML) Software Market during 2025 and 2026.

Europe held the second-largest regional position in the Quantum Machine Learning (QML) Software Market, supported by Germany, the United Kingdom, and France through structured ecosystem development and public collaboration programs. The region's strength came less from a single dominant company and more from coordinated work on standards, interoperability, and research-to-commercialization pathways. The EU Quantum Flagship framework and the EuroHPC Joint Undertaking's QEC4QEA initiative are helping create shared infrastructure and a clearer software pathway for quantum-enhanced applications across borders. Germany's FullStaQD initiative added another layer by developing a reference software architecture for quantum computing stacks, which supports component interoperability across the domestic ecosystem. Spain also remained relevant through Multiverse Computing, one of the region's more commercially active optimization-focused software vendors. This structure gave Europe a stable regional role in the Quantum Machine Learning (QML) Software Market, even without the same level of hyperscaler concentration seen in North America.

Asia-Pacific is projected to grow at a 35.28% CAGR through 2031, which makes it the fastest-growing regional block in the Quantum Machine Learning (QML) Software Market. Growth in the region reflects national commercialization programs, broader cloud access, and rising interest in life sciences and research applications. Japan stood out in May 2026 with the collaboration among RIKEN, the Cleveland Clinic, and IBM on the 12,635-atom protein simulation milestone, which represented one of the clearest applications in the region. South America accounted for a modest revenue share in 2025, with Brazil remaining the region's most active base for early research and enterprise pilots, while the Middle East and Africa remained at an emerging stage driven more by talent development and cloud access than by direct hardware investment. This means regional expansion outside the largest markets is still being shaped by ecosystem building rather than by mature commercial deployment.

1. Xanadu Quantum Technologies Inc.
2. Zapata Computing Holdings Inc.
3. Classiq Technologies Ltd.
4. QC Ware Corp.
5. 1QBit Canada Ltd.
6. QunaSys Inc.
7. Quantinuum Ltd.
8. Riverlane Ltd.
9. Terra Quantum AG
10. Multiverse Computing S.L.
11. PASQAL SAS
12. SEEQC, Inc.
13. Algorithmiq Oy
14. ProteinQure Inc.
15. Atos SE
16. D-Wave Quantum Inc.
17. Rigetti Computing, Inc.
18. IBM Corporation
19. Microsoft Corporation
20. Amazon Web Services, Inc.
21. Google LLC
22. IonQ Inc.
23. Intel Corporation
24. Fujitsu Limited
25. QuEra Computing Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Enterprise Demand for Hybrid Quantum Classical Workflows
  • 4.2.2 Rising Need for Quantum Safe Optimization In High Complexity Use Cases
  • 4.2.3 Rapid Expansion of Cloud Access to Quantum Development Environments
  • 4.2.4 Regulatory and Public Funding Support For Quantum Software Ecosystems
  • 4.2.5 Under-Reported Driver, Algorithm Portability Across Hardware Backends
  • 4.2.6 Under-Reported Driver, Quantum Readiness Benchmarking In Procurement
• 4.3 Market Restraints
  • 4.3.1 Limited Commercial Scale of Fault Tolerant Quantum Hardware
  • 4.3.2 High Integration Cost With Legacy Enterprise Data and MLOps Stacks
  • 4.3.3 Talent Shortage in Quantum Algorithms And Quantum DevOps
  • 4.3.4 Under-Reported Restraint, Validation Gaps for Quantum Advantage Claims
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Technology Outlook
• 4.7 Regulatory Landscape
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Bargaining Power of Buyers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Solution
  • 5.1.1 Software Platforms
    • 5.1.1.1 Development Tools And SDKs
    • 5.1.1.2 Simulation And Benchmarking Software
    • 5.1.1.3 Algorithm Design And Optimization Software
  • 5.1.2 Services
• 5.2 By Deployment
  • 5.2.1 Cloud-Based
  • 5.2.2 Hybrid
  • 5.2.3 On-Premises
• 5.3 By Organization Size
  • 5.3.1 Large Enterprises
  • 5.3.2 Small and Medium Enterprises
• 5.4 By Application
  • 5.4.1 Optimization
  • 5.4.2 Drug Discovery and Life Sciences
  • 5.4.3 Materials Science and Quantum Chemistry
  • 5.4.4 Financial Analytics and Risk Modeling
  • 5.4.5 Cryptography and Cybersecurity
  • 5.4.6 Other Applications
• 5.5 By End-User Industry
  • 5.5.1 IT and Telecommunication
  • 5.5.2 BFSI
  • 5.5.3 Healthcare and Life Sciences
  • 5.5.4 Industrial Manufacturing
  • 5.5.5 Education and Research Institutions
  • 5.5.6 Government and administration
  • 5.5.7 Energy and Utilities
  • 5.5.8 Other End-User Industries
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 South America
    • 5.6.2.1 Brazil
    • 5.6.2.2 Argentina
    • 5.6.2.3 Rest of South America
  • 5.6.3 Europe
    • 5.6.3.1 Germany
    • 5.6.3.2 United Kingdom
    • 5.6.3.3 France
    • 5.6.3.4 Russia
    • 5.6.3.5 Spain
    • 5.6.3.6 Rest of Europe
  • 5.6.4 Asia-Pacific
    • 5.6.4.1 China
    • 5.6.4.2 Japan
    • 5.6.4.3 India
    • 5.6.4.4 South Korea
    • 5.6.4.5 Southeast Asia
    • 5.6.4.6 Rest of Asia-Pacific
  • 5.6.5 Middle East and Africa
    • 5.6.5.1 Middle East
      • 5.6.5.1.1 Saudi Arabia
      • 5.6.5.1.2 United Arab Emirates
      • 5.6.5.1.3 Turkey
      • 5.6.5.1.4 Rest of Middle East
    • 5.6.5.2 Africa
      • 5.6.5.2.1 South Africa
      • 5.6.5.2.2 Nigeria
      • 5.6.5.2.3 Egypt
      • 5.6.5.2.4 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Xanadu Quantum Technologies Inc.
  • 6.4.2 Zapata Computing Holdings Inc.
  • 6.4.3 Classiq Technologies Ltd.
  • 6.4.4 QC Ware Corp.
  • 6.4.5 1QBit Canada Ltd.
  • 6.4.6 QunaSys Inc.
  • 6.4.7 Quantinuum Ltd.
  • 6.4.8 Riverlane Ltd.
  • 6.4.9 Terra Quantum AG
  • 6.4.10 Multiverse Computing S.L.
  • 6.4.11 PASQAL SAS
  • 6.4.12 SEEQC, Inc.
  • 6.4.13 Algorithmiq Oy
  • 6.4.14 ProteinQure Inc.
  • 6.4.15 Atos SE
  • 6.4.16 D-Wave Quantum Inc.
  • 6.4.17 Rigetti Computing, Inc.
  • 6.4.18 IBM Corporation
  • 6.4.19 Microsoft Corporation
  • 6.4.20 Amazon Web Services, Inc.
  • 6.4.21 Google LLC
  • 6.4.22 IonQ Inc.
  • 6.4.23 Intel Corporation
  • 6.4.24 Fujitsu Limited
  • 6.4.25 QuEra Computing Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment