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Run your analysis now →https://eureka.patsnap.com/resources/blog/rd-blog/quantum-support-vector-machine-classification-algorithm-patent-landscape-patent-landscape/ · Patsnap · data cut-off 2026-08-31 · downloaded from the live page
Patent Landscape · Quantum TechnologiesQuantum Support Vector Machine Classification Patents
A patent landscape analysis of quantum support vector machine classification algorithms: 19 published records, filing trends through a 2025 peak, IPC technology composition, assignee concentration, and where claim space
19
Published Records
47%
Top-5 Share of All Records
CN
Leading Jurisdiction
21
Active Filers Ranked
Top-5 share = the 5 largest assignees ÷ all 19 records in scope (CR5), not the ranked leaders only.
Published byPatsnap Research··6 min readSourced from Patsnap Eureka
Landscape Overview
An early-stage claim space, not yet a crowded one
Quantum support vector machine classification sits at the intersection of two much larger patent categories — quantum computing and machine-learning classifiers — but as a distinct claimed technique it remains small. Only 19 published records match filings that combine a quantum SVM or quantum support vector machine with classification and algorithm claims, spanning 2015 through the August 2026 data cut-off. The leading assignee holds just 3 of those 19 records, and most classification activity is framed under G06N, the AI-model computing subclass, rather than as a distinct quantum-hardware category.
Filing activity rose steadily to a peak of 10 records in 2025 before an apparent drop in 2026 — almost certainly a publication-lag effect rather than a genuine slowdown, since the 18-month gap between filing and publication means 2025 and 2026 filings are still arriving. Receiving offices skew toward China and India, with smaller counts at the EPO, UK, US and Australia, suggesting filing strategy here is still regional rather than globally coordinated.
Filing trend and technology composition, 2015–2026
Source: Patsnap Eureka. Assignee ranking and totals. Derived from a Patsnap search on Quantum Support Vector Machine Classification Algorithm Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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Filing Data
Filing trends and technology composition
Nineteen published records anchor this landscape, filed against the search string tracking quantum support vector machine classification algorithms since 2015. The trend and classification breakdown below show where filing activity concentrates and how thinly it is spread across application domains.
Filings climbed toward a 2025 peak, then the most recent year understates itself
Filings rose from 3 in 2022 to a peak of 10 in 2025, before dropping to 2 in 2026 — a fall that reflects publication lag rather than a real slowdown, since 2026 records are still arriving. With fewer than four complete years of data once that lag is accounted for, no filing growth rate can be stated reliably.
AI-model classing dominates; application-domain classes stay thin
G06N (computing based on AI models) covers 78.9% of the 19 records and G06F (electric digital data processing) covers 47.4%, confirming most filings frame QSVM work as an AI-model variant with a classical-computing anchor. Healthcare informatics (G16H) sits at 15.8%, while diagnosis/surgery, image processing, image/video recognition and speech/audio each hold just a single record — 5.3% apiece — marking those as thin, single-filing domains rather than contested ground.
Shares are the percentage of the 19 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Source: Patsnap Eureka. Filing trend and technology composition. Derived from a Patsnap search on Quantum Support Vector Machine Classification Algorithm Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish.
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Try EurekaRepresentative Filing
A representative claim from the dataset
Representative Record
Quantum-enhanced misinformation detection and prevention
AMERICAN EXPRESS TRAVEL RELATED SERVICES CO., INC.
System, method, and computer program product embodiments detect misinformation based on received inferencing data that includes textual or media content. A data vector is derived from one or more samples of the inferencing data. Quantum computing hardware is commanded to select a plurality of features of the data vector using a quantum approximate optimization algorithm (QAOA). Quantum computing hardware is commanded to classify the inferencing data as one of misinformation or genuine information using a quantum support vector machine (QSVM) provided with the selected plurality of features of the data vector.Filed by American Express Travel Related Services Co., Inc.; published as EP4769245A1, dated 2026-07-01.
Source: Patsnap Eureka. Citation counts and representative records. Derived from a Patsnap search on Quantum Support Vector Machine Classification Algorithm Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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What the filing pattern tells you
Four readings of the same 19-record dataset, each pointing at a different decision a competitive-intelligence or R&D team needs to make.
Concentration
47.4%
top 5 share of 19 records
A leader exists, but does not dominate
The top-ranked assignee holds 3 of 19 records, and the top 5 combined reach 47.4% of all records in scope. That leaves more than half the field split across a long tail of single- and double-filing entrants, so freedom-to-operate work needs to check individual claims rather than assume one blocking incumbent.
Based on the 19-record assignee ranking
Technology framing
78.9%
of records classed under G06N
QSVM is filed as an AI-model variant, not a hardware claim
Nearly four in five records sit in G06N, computing based on AI models, with G06F digital-processing claims layered in on 47.4% of records. Filers are anchoring quantum classifier claims in familiar AI-model and classical-processing language rather than staking out pure quantum-hardware territory.
IPC subclass shares, out of 19 records; a record can carry multiple classes
Recency caveat
10 in 2025
peak filing year
The 2026 dip is a publication-lag artefact
Filings ran from 3 in 2022 to a peak of 10 in 2025 before falling to 2 in 2026. Publication typically lags filing by roughly 18 months, so 2026's low count understates real filing activity rather than reflecting a genuine cooling of interest.
Filing trend, 2022–2026 (2026 partial)
Application gaps
1 record each
in G10L, A61B, G06T, G06V
Domain-specific branches remain largely unclaimed
Speech and audio, medical diagnosis and surgery, image generation, and image/video recognition each hold a single filing out of 19. No repeat filer has emerged in any of these branches, leaving room for a domain-specific claim built around a distinct feature-encoding step.
IPC subclass counts, 19 records in scope
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Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to quantum support vector machine classification algorithm patent landscape, with the prior art for and against each one.
Source: Patsnap Eureka. Derived observations on the same dataset. Derived from a Patsnap search on Quantum Support Vector Machine Classification Algorithm Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
Next Steps
Where to take this analysis next
The filing pattern here — a modest leader, a long tail, and several unclaimed application branches — points to specific next moves depending on whether you are scoping freedom-to-operate or looking for a filing opportunity.
Check claim scope before filing near the core pipeline
The feature-selection-plus-QSVM-classifier pattern is the most repeated structure in this dataset. Before drafting near it, pull the individual claims of the top-ranked assignees rather than relying on the aggregate concentration figures.
Search related filings in EurekaTarget the thin application branches
Speech and audio, medical diagnosis, image generation and image/video recognition each carry only one filing out of 19. A domain-specific feature-encoding claim in one of these branches faces little existing competition today.
Explore white space in EurekaRevisit the trend once 2025–2026 filings finish publishing
The 2026 filing count is understated by publication lag. Re-running this analysis in twelve months will give a truer read on whether the 2025 peak was a one-off or the start of a sustained rise.
Track filing trends in EurekaSource: Patsnap Eureka. Forward-looking reading of the same dataset. Derived from a Patsnap search on Quantum Support Vector Machine Classification Algorithm Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
FAQ
Frequently asked questions
The dataset tracked here contains 19 published records matching quantum support vector machine or quantum SVM classification algorithm claims, filed between 2015 and the August 2026 data cut-off. This is a narrow, emerging field rather than a saturated one — the ranked assignee list covers only 21 companies and individuals in total. Because publication lags filing by roughly 18 months, the true count of filed-but-not-yet-published applications, especially from 2025 and 2026, is higher than what is currently visible.
Filing activity is concentrated but not dominated by a single player: the leading assignee holds 3 of the 19 records, and the top 5 assignees combined account for 9 records, or 47.4% of all records in scope. The top 10 combined reach 73.7% of all records, meaning the remaining ranked assignees each hold only one or two filings. This pattern — a modest leader plus a long tail of single-filing entrants — is typical of a technology still in its early claim-staking phase rather than one with an entrenched incumbent.
Most filings sit in G06N, the AI-model computing classification, covering 78.9% of the 19 records, often paired with G06F general digital data processing claims in 47.4% of records. Beyond that core, application-specific branches are thin: healthcare informatics (G16H) appears in 15.8% of records, while diagnosis and surgery, image generation, image/video recognition, and speech/audio processing each appear in only one record apiece. A single record can carry multiple IPC classes, so these percentages do not sum to 100%.
Filings rose from 3 records in 2022 to a peak of 10 in 2025, the highest year on record so far. The apparent drop to 2 records in 2026 is very likely an artefact of publication lag rather than a genuine decline, since many 2025 and 2026 filings will not be published yet. There are not enough complete years of data once that lag is accounted for to state a reliable growth rate, so treat any year-over-year comparison involving 2025 or 2026 with caution.
The most open ground sits in application-specific branches outside the core AI-model classification: speech and audio analysis, medical diagnosis and surgery, image generation, and image/video recognition each hold just one record out of the 19 in scope. No applicant has filed a second patent in any of these branches yet, so there is no defensive thicket blocking a domain-specific QSVM claim there. The more contested space is the core feature-selection-plus-QSVM-classifier pipeline itself, which repeat filers are already claiming across multiple application domains.
Answers are grounded in the same dataset. Derived from a Patsnap search on Quantum Support Vector Machine Classification Algorithm Patent Landscape covering 2015–2026, data cut-off 2026-08-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company's registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.