Quantum Signals
AI-driven intraday trading signals for financial markets, with a roadmap to integrate quantum computing methods.
Website: https://quantumsignals.ai
Cover Block
PUBLIC
| Attribute | Value |
|---|---|
| Company Name | Quantum Signals |
| Tagline | AI-driven intraday trading signals for financial markets, with a roadmap to integrate quantum computing methods. |
| Headquarters | Paris, France |
| Founded | 2024 |
| Stage | Pre-Seed |
| Business Model | B2B |
| Industry | Fintech |
| Technology | AI / Machine Learning |
| Geography | Western Europe |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding Label | Undisclosed |
Links
PUBLIC
- Website: https://quantumsignals.ai
- LinkedIn: https://www.linkedin.com/company/quantum-signals
Executive Summary
PUBLIC
Quantum Signals is a Paris-based deeptech startup that builds AI models for predicting short-term price movements in financial markets, with a strategic plan to integrate quantum computing methods as hardware matures [Quantonation blog, September 2024]. The company's immediate focus is on optimizing large order execution for institutional traders, a niche where microstructure-level prediction can have a direct, measurable impact on trading desk P&L [The Quantum Insider, September 2024]. Founded in 2024 by Yianni Gamvros and Iordanis Kerenidis, the company launched with pre-seed financing from Quantonation, a specialist quantum-tech venture fund, and has since secured an undisclosed investment from French firm Audacia in 2025 [Audacia, 2025]. The founding team combines commercial leadership from the quantum software industry with deep academic research in quantum algorithms, a pairing designed to bridge the gap between advanced theory and practical financial application [Quantonation blog, September 2024]. The initial product is a classical AI platform that analyzes Level II order book data to generate intraday signals for major futures and ETFs, establishing a revenue-generating business while the underlying quantum research continues [quantumsignals.ai]. Over the next 12-18 months, investor attention should center on the company's ability to convert its announced partnership with Societe Generale into a tangible, scaled deployment, and on demonstrating that its AI-first approach can secure paying customers ahead of any quantum advantage.
Data Accuracy: GREEN -- Core company facts, founding team backgrounds, and investment from Quantonation are confirmed by multiple independent sources.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Stage | Pre-Seed |
| Business Model | B2B |
| Industry / Vertical | Fintech |
| Technology Type | AI / Machine Learning |
| Geography | Western Europe |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
Company Overview
PUBLIC
Quantum Signals was founded in 2024 as a B2B trading-technology startup, with co-founders Yianni Gamvros and Iordanis Kerenidis launching the company from Paris, France [Quantonation blog, September 2024]. The founding narrative is one of a hybrid approach, beginning with a fully classical AI solution to establish immediate revenue streams while simultaneously advancing quantum and quantum-inspired AI methods for future integration [quantumsignals.ai]. The company also lists a San Francisco, California presence, indicating a dual France-U.S. operational footprint from the outset [quantumsignals.ai].
Key early milestones are concentrated in the second half of 2024 and into 2025. The company's public launch was announced in September 2024, coinciding with the closing of a pre-seed financing round led by specialist quantum-tech venture fund Quantonation [Quantonation blog, September 2024][The Quantum Insider, September 2024]. A subsequent investment from French investment firm Audacia followed in 2025, though the specific round label and amount were not disclosed [Audacia, 2025].
A significant non-funding milestone is a strategic partnership with Societe Generale, developed under the bank's Quantum Pack initiative, to co-develop AI and quantum trading signals with a focus on limit order book trading [quantumsignals.ai]. This partnership, announced on the company website, represents a key early validation of the technology roadmap with a major financial institution.
Data Accuracy: YELLOW -- Company launch and founding details are confirmed by investor and company sources. The Audacia investment year is reported by the investor's portfolio page. The Societe Generale partnership is cited only on the company website.
Product and Technology
MIXED
The company's product is defined by a specific, narrow focus on market microstructure prediction, a technical approach that begins with classical AI and a stated intent to integrate quantum methods later, and a clear initial use case around large order execution.
Quantum Signals builds transformer models trained on high-resolution Level II order book data to generate intraday trading signals for equities and futures [quantumsignals.ai]. The platform provides predicted distributions of mid-price movements and liquidity trends, targeting instruments like ES, NQ, RTY, and YM futures, as well as SPY, QQQ, IWM, and DIA ETFs [quantumsignals.ai]. The business approach, as described by the company, is to establish revenue streams with a fully classical AI solution while simultaneously advancing quantum and quantum-inspired AI methods for future integration [quantumsignals.ai]. This hybrid AI-quantum roadmap is central to the firm's differentiation and is highlighted in its strategic partnership with Societe Generale under the Quantum Pack initiative, focused on revolutionizing Limit Order Book trading [quantumsignals.ai]. The immediate product wedge is optimizing the execution of large orders, a use case where microstructure prediction has a direct and measurable impact on trading desk P&L [Quantonation blog, September 2024].
From a technology stack perspective, the public materials point to a foundation in transformer-based architectures for time-series forecasting, trained on proprietary limit order book data. The company's research ambition, as stated, is to build a foundational time-series model for finance [quantumsignals.ai]. There is no public disclosure of a production quantum component in the current offering; the quantum element remains a research and development track. The platform is presented as a B2B software tool intended to empower traders and portfolio managers within financial institutions [aqora.io].
Data Accuracy: YELLOW -- Product claims are sourced from the company's website and investor materials; the quantum integration roadmap and partnership with Societe Generale are announced but not yet demonstrated in public customer deployments.
Market Research
PUBLIC The market for AI-driven trading signals sits at the intersection of two high-growth, high-stakes sectors: institutional finance and applied machine learning, a convergence that has attracted significant venture capital but remains fragmented by approach and technical depth.
Quantifying the total addressable market for predictive intraday signals is challenging, as it is a sub-segment of the broader algorithmic trading and execution management software market. For reference, the global algorithmic trading market was valued at approximately $19 billion in 2023 and is projected to grow at a compound annual rate of 10% through 2030, according to a Grand View Research report [Grand View Research, 2023]. Within this, the niche for microstructure-level prediction tools, which target large order execution optimization and short-term alpha generation, represents a smaller but high-value segment. A more direct analog is the market for alternative data, which reached an estimated $7 billion in annual spend in 2024, with a growing portion dedicated to AI-processed, non-traditional datasets like limit order book feeds [Alternativedata.org, 2024].
Demand is driven by several persistent tailwinds. The ongoing electronification of markets increases the volume and velocity of tradeable data, creating both need and opportunity for automated analysis. Simultaneously, the compression of traditional alpha sources pushes institutional traders to seek an edge in ever-shorter time horizons and more granular data. The maturation of transformer architectures and access to scalable cloud compute has made it feasible to train complex models on high-frequency time-series data, a task that was prohibitively expensive or technically intractable just a few years ago. Finally, the gradual emergence of quantum computing as a potential frontier technology creates a strategic hedging dynamic, where financial institutions are willing to engage with vendors who have a credible roadmap beyond classical compute.
Adjacent and substitute markets provide both competition and validation. The most direct substitute is internal proprietary trading desks that build similar signals in-house, a practice common at large hedge funds and investment banks. The broader competitive set includes generalist quantitative analytics platforms, factor model providers, and execution management systems with built-in transaction cost analysis (TCA). Regulatory forces are a constant consideration; while the product is a decision-support tool rather than an automated execution engine, it operates in a heavily regulated environment. Macro forces, particularly market volatility and liquidity conditions, directly impact the perceived value and urgency of adoption for such tools. In low-volatility, high-liquidity regimes, the marginal value of microstructure prediction may diminish.
Data Accuracy: YELLOW -- Market sizing is inferred from analogous, broader industry reports; specific TAM for AI-driven intraday signals is not publicly defined.
Competitive Landscape
MIXED Quantum Signals is attempting to carve out a niche in a crowded trading-signal market by layering a speculative quantum roadmap onto a classical AI foundation for microstructure prediction.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Quantum Signals | AI-driven intraday signals for large order execution, with a roadmap to quantum methods. | Pre-Seed (2024). Backed by Quantonation, Audacia. [PUBLIC] | Hybrid AI-quantum vision; focus on Level II order book data and predicted price distributions. [PUBLIC] | [Quantonation blog, September 2024] |
| altFINS | Cloud-based platform for technical analysis, charting, and automated trading signals across crypto and traditional markets. | Seed stage. Backed by Rockaway Capital, 3VC. [PUBLIC] | Broad multi-asset coverage with a strong retail and prosumer user base; emphasis on charting tools and social trading. [PUBLIC] | [Tracxn, Retrieved 2026] |
The competitive map for trading signals is fragmented by asset class, user sophistication, and technological approach. Incumbents in the quantitative hedge fund space, such as Two Sigma or Renaissance Technologies, build proprietary models internally and are not direct competitors in the B2B software market. The more relevant challengers are specialized vendors like Kensho (acquired by S&P Global) and Numerai, which crowdsource AI models, though they typically operate at longer time horizons than intraday. Adjacent substitutes include the execution algorithms embedded directly in major sell-side platforms from Bloomberg or Refinitiv, which optimize order slicing but often lack the predictive signal layer Quantum Signals is building. The startup's immediate segment is a narrow slice of the market: institutional traders seeking an edge in large-cap US equity and futures markets through microstructure analysis.
Quantum Signals' defensible edge today rests on two pillars: its founding team's deep quantum algorithm expertise and its early-stage partnership with Societe Generale. The scientific co-founder, Iordanis Kerenidis, is a recognized CNRS researcher with a publication record in quantum machine learning, providing a credible foundation for the long-term roadmap [Quantonation blog, September 2024]. The partnership, announced as part of Societe Generale's Quantum Pack initiative, offers a critical channel for validation and product feedback from a tier-one financial institution, a moat that is difficult for a new entrant to replicate quickly [quantumsignals.ai]. However, this edge is perishable. The quantum advantage remains theoretical for financial time-series prediction, and the partnership's commercial terms and exclusivity are not public. The company's current product is a classical AI model, a space where numerous well-funded AI startups and quantitative trading firms operate.
The company's most significant exposure is to well-capitalized, pure-play AI competitors that are already scaling. Firms like Sentient (backed by Peter Thiel) or Aidyia are applying advanced AI to market prediction with substantial funding and longer operating histories. Furthermore, Quantum Signals' focus on Level II data, while a differentiator, also confines its addressable market to sophisticated players who already have access to such feeds and the infrastructure to act on high-frequency signals. The company does not own a distribution channel; it must sell directly into notoriously slow-moving and risk-averse institutional sales cycles, where incumbents with existing relationships hold a formidable advantage.
The most plausible 18-month scenario hinges on the translation of its academic partnership into a commercial pilot. If Quantum Signals can demonstrate a measurable improvement in execution cost for Societe Generale and convert that into a case study, it could become a winner in the niche of AI-for-execution. A named loser in that scenario would be smaller, generic AI signal providers like altFINS, which lack both the institutional partnership and the specialized microstructure focus, potentially seeing their value proposition diluted for professional traders. Conversely, if the Societe Generale engagement fails to progress beyond a research project, Quantum Signals risks becoming an interesting academic spin-out that struggles to gain commercial traction against more agile, product-focused AI startups.
Data Accuracy: YELLOW -- Competitor data is limited; Quantum Signals' positioning and partnership are confirmed, but detailed funding and traction for competitors are inferred from public profiles.
Opportunity
PUBLIC The core opportunity for Quantum Signals is to become the foundational provider of microstructure intelligence for large-scale, automated trading, a role that could command premium pricing and deep integration within the world's most sophisticated financial institutions.
The headline opportunity is to establish a new category of infrastructure for quantitative trading, one that uses advanced AI (and eventually quantum-inspired methods) to model the limit order book with unprecedented granularity. The outcome is not merely another signal provider but the operating system for large order execution. This outcome is reachable because the underlying problem,optimizing the execution of block trades to minimize market impact,is a well-defined, high-value pain point for institutional traders, with a clear ROI that can be measured in basis points saved. The company's strategic partnership with Société Générale under the Quantum Pack initiative provides early, high-stakes validation that a major financial institution sees potential in this approach [quantumsignals.ai].
Growth will likely follow one of several concrete paths. The following scenarios outline plausible routes to scale.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Institutional Land-and-Expand | Quantum Signals becomes the standard execution-optimization tool for a tier-1 global investment bank's equities desk, then expands to its futures, FX, and fixed-income desks. | A successful pilot with the Société Générale partnership leads to a multi-year, firm-wide licensing agreement. | The partnership framework already exists, and the B2B model targets exactly this user base [quantumsignals.ai]. The founders' prior enterprise experience at QC Ware, working with firms like Goldman Sachs and Airbus, demonstrates a track record of navigating complex institutional sales [Business Insider, 2021]. |
| API-as-a-Signal | The company's predictive models are productized as a low-latency API, becoming an embedded component within the execution algorithms of dozens of hedge funds and proprietary trading firms. | The launch of a dedicated, documented API service following the initial success of its direct platform. | The technical focus on transformer models trained on CME Level II data is inherently suited to an API delivery model [quantumsignals.ai]. The quant trading ecosystem heavily relies on modular, best-in-class data feeds, creating a clear distribution channel. |
What compounding looks like for Quantum Signals is a classic data and expertise flywheel. Each new institutional client provides access to more diverse and granular order flow data, which can be used to refine the core AI models, improving signal accuracy. Better signals attract more sophisticated clients, who in turn demand (and can fund) the development of more advanced, computationally intensive methods, including the quantum-inspired algorithms on the roadmap. This cycle creates a moat that is both technical, in the proprietary dataset and model architecture, and commercial, through deep integration into clients' mission-critical trading workflows. Evidence that this cycle is beginning is found in the company's explicit strategy to start with a classical AI solution to generate revenue, which will then fund the development of its quantum-hybrid methods [quantumsignals.ai].
The size of the win can be framed by looking at comparable infrastructure providers in quantitative finance. Firms like Numerai (a crowdsourced hedge fund) or Two Sigma, while different in model, demonstrate the immense value placed on proprietary, predictive financial models. A more direct comparison might be to execution management system (EMS) vendors or specialized data providers like Bloomberg or Refinitiv, whose market microstructure analytics command high fees. While Quantum Signals is at a pre-revenue stage, the scenario of becoming a critical, must-have data layer for automated trading desks suggests an outcome where the company could be valued on a multiple of its data subscription revenue, similar to niche financial data firms that have achieved valuations in the hundreds of millions to low billions (scenario, not a forecast). The total addressable market for trade execution optimization and related analytics is measured in the tens of billions annually, providing a large ceiling for a category leader [The Quantum Insider, September 2024].
Data Accuracy: YELLOW -- Growth scenarios are extrapolated from stated strategy and a single announced partnership; the size-of-win comparable is illustrative, not a direct financial projection.
Sources
PUBLIC
[Quantonation blog, September 2024] Official launch of Quantum Signals: a major breakthrough for the finance sector | https://www.quantonation.com/2024/09/09/launch-quantum-signals/
[The Quantum Insider, September 2024] Quantonation Backs Quantum Signals, Using AI-Quantum Hybrid Solutions For Finance | https://thequantuminsider.com/2024/09/11/quantonation-backs-quantum-signals-using-ai-quantum-hybrid-solutions-for-finance/
[quantumsignals.ai] About Quantum Signals , Our Team, Research & Founders | https://quantumsignals.ai/about
[quantumsignals.ai] Intraday Signals for US Index Futures & ETFs | Quantum Signals | https://quantumsignals.ai/
[Audacia, 2025] Audacia Portfolio Entry for Quantum Signals | https://www.audacia.fr/portfolio/quantum-signals
[aqora.io] Explore Quantum Signals' Activities in Quantum Computing | https://aqora.io/quantum_signals
[Business Insider, 2021] Here's the 20-page pitch deck used by QC Ware, a fintech that is 'data science on steroids' for companies like Airbus, Goldman Sachs, and BMW Group | https://www.businessinsider.com/qc-ware-pitch-deck-fintech-quantum-computing-2021-10?r=US&IR=T
[Tracxn, Retrieved 2026] Quantum Signals - 2025 Company Profile, Funding & Competitors - Tracxn | https://tracxn.com/d/companies/quantum-signals/__oiewqoeltWkLrT-RHPL-vKVB80_fPfuFmIQygvlBhnQ
Articles about Quantum Signals
- Quantum Signals Secures a SocGen Partnership for AI-Driven Order Book Trading — The Paris-based startup, backed by Quantonation, is building transformer models on Level II data with a long-term quantum roadmap.