- Infleqtion goes public on the NYSE under INFQ. First neutral-atom quantum company to list publicly, validating the technology, accelerating R&D and team growth, and signalling real commercial potential
- Nu Quantum opens Trapped-Ion Qubit Networking Lab in Cambridge. Following their $60M Series A, the lab doubles research infrastructure for multi-node trapped-ion networks using Qubit‑Photon Interface (QPI) technology.
- Rigetti Computing lands $8.4M purchase order in India. Will deliver a 108-qubit quantum computer to the Centre for Development of Advanced Computing (C-DAC) in Bengaluru, expanding access to high-performance quantum hardware.
- Equal1 raises $60M for Bell‑1 silicon quantum server. Irish semiconductor company brings total funding to $85M, accelerating deployment of its silicon-based quantum server into high-performance computing environments and moving from development to real-world adoption.
- Q*Bird scales secure quantum networks in Europe. Netherlands-based MDI-QKD provider secures €2.5M grant plus €5M equity from the EIC Accelerator to expand next-gen secure quantum communications, enabling detector-independent, commercially deployable quantum networks.
- Firgun Ventures makes first investment in Photonic Inc. London-based $250M quantum fund backs DARPA-selected Photonic Inc., supporting silicon-based qubits with native photonic connectivity.
- IQM Quantum Computers is going public via a SPAC merger with Real Asset Acquisition Corp.
“Instead of waiting for quantum hardware to mature, we asked: where can we apply advanced computational methods today and create immediate value?” Yianni explains.
With a background at QC Ware, working at the forefront of quantum computing and AI for Fortune 100 companies, he developed the idea of “quantum insurance”, helping organizations prepare for future technological disruption. Quantum Signals builds on this philosophy, combining deep computational expertise with practical, deployable solutions, applying transformer-based AI to order book dynamics to uncover short-term market inefficiencies and provide traders with a new edge.
For this edition of Qbit Curious, I sat down with Yianni Gamvros, the CEO & Co-Founder of Quantum Signals, a Paris-based startup focused on AI-driven trading signals for the financial industry. Founded in 2024 by Yianni Gamvros and Iordanis Kerenidis, and supported by investor Quantonation, the company is developing tools that bring next-generation computational techniques to real-world trading workflows.
You spent many years at QC Ware leading business development and AI initiatives. What inspired you to start Quantum Signals, and how did that experience shape your approach?
Yianni explains that the origins of Quantum Signals are deeply tied to his time at QC Ware and to the lessons learned from working at the frontier of an emerging technology. “When we joined QC Ware in 2018, quantum computing was still in its early commercial phase,” he says. “There was enormous interest from industry, especially from large financial institutions, but there wasn’t a clear roadmap for how it would all evolve.”
Very early on, Yianni developed what he describes as a “quantum insurance” framework for engaging clients. “We would tell companies: quantum computing might become the next major technological disruption. You don’t know exactly when it will arrive or how it will impact you, but you need to prepare.”
The idea was similar to an insurance premium. Companies would invest early in understanding quantum’s potential impact. QC Ware would sit down with them, analyze their day-to-day processes, identify computational bottlenecks, and determine which problems quantum could realistically address and which it could not. “We would take large, real-world optimization or risk problems and boil them down into smaller, toy versions that could run on the limited quantum hardware available at the time,” Yianni explains. “Then we’d extrapolate if the hardware scales, here’s what the impact might look like.”
Much of this work was done with Fortune 100 companies and predominantly with major banks. Yianni notes that roughly 60–70% of their engagements were with large financial institutions across the US and Europe. QC Ware published research alongside institutions such as Goldman Sachs, JP Morgan Chase, Itaú Unibanco, and others.
That experience gave him two important insights.
First, transformative technologies often take longer than early forecasts suggest. In 2019, there were expectations that production-level quantum applications might emerge within five years. Today, even optimistic projections place that timeline several years out. “We realized that while the field was progressing, it wasn’t evolving as quickly as many early reports suggested,” Yianni says. “You have to respect how hard these breakthroughs actually are.”
Second and perhaps more importantly, he observed a widening technology gap inside large financial institutions. “Through our work with banks, we saw that the latest AI methods weren’t always being deployed in production environments,” he explains. “There are structural gaps, whether in talent, budget, internal alignment, or persistence that make it difficult for large institutions to build cutting-edge systems from scratch.”
That realization became the catalyst for Quantum Signals. “Instead of waiting for quantum hardware to mature, we asked: where can we apply advanced computational methods today and create immediate value?” The answer was short-term market prediction using microstructure data.
Rather than selling “insurance” for a future technological wave, Quantum Signals would build and productize advanced predictive models that institutions could deploy immediately off the shelf. “Buy-side and sell-side institutions often don’t have the time or resources to build something this specialized internally,” Yianni explains. “So we build it properly, productize it, and bring it to market.”
In many ways, Quantum Signals reflects both the ambition and the realism Yianni developed during his quantum computing years: combine deep computational thinking with practical delivery and focus on impact that can be measured today.
Can you summarize Quantum Signals’ mission and what makes your approach unique?
Yianni explains that Quantum Signals was built around a simple but powerful observation: most short-term prediction models don’t fully leverage the richest data in the market, the order book itself. “Our mission,” he says, “is to improve short-term market prediction by modeling the actual mechanics of supply and demand as they unfold in real time.”
Rather than relying purely on price histories or traditional quant factors, Quantum Signals focuses on detailed order book dynamics, how liquidity builds, disappears, and shifts. According to Yianni, that’s where short-term inefficiencies live. “We’re not just modeling price. We’re modeling the pressure behind price.” He describes it as moving one layer deeper, from observing outcomes to analyzing the micro-decisions happening inside the market.
How does analyzing detailed order book data give you an edge over traditional quant models?
Yianni explains that this is one of the first questions investors and clients ask. “There have been decades of quant models built by physicists and computer scientists,” he says. “So the real question is: what’s different now?”
The difference lies in transformer architectures, the same breakthrough behind large language models (LLMs). “Transformers introduced attention mechanisms that allow models to detect complex patterns across long sequences,” Yianni explains. “In language, that means predicting the next word. In markets, it means predicting the next structural move.”
Instead of applying transformers to text, Quantum Signals applies them to order book data, real-time sequences of prices, volumes, and liquidity shifts. “We’re feeding the model the evolving supply and demand dynamics of the market,” he says. “Order flow is sequential. Liquidity changes are sequential. Transformers are built to understand sequences.” While transformer-based models have been explored in high-frequency or macro settings, Yianni identified a gap in the minutes-to-hours intraday horizon, a critical window for many traders.
“That’s where we saw an opportunity,” he explains. “There was promising research, but limited production deployment.” Working with design partners such as Société Générale, the team developed and validated the approach before building a real-time system that delivers live predictive signals. The result is an enhancement layer for traders, improving execution timing and intraday alpha generation alongside existing models. “We’re not replacing legacy systems,” Yianni says. “We’re adding a new edge, powered by modern AI, to liquidity-level data.”
In short, the advantage isn’t just access to order book data. It’s applying next-generation attention-based AI to extract patterns that older models simply weren’t designed to capture.
What are some examples of how your predictions are currently being applied by traders or quant research teams?
Yianni explains that Quantum Signals’ models are actively being used by institutional clients today. “We work with hedge funds, banks, and execution firms who are integrating our product into their workflows,” he says. “They’re using it to enhance intraday trading strategies and improve order execution.” He notes that, for now, most of these clients are private. “None of our references are public yet,” Yianni explains. “But as we move further into 2026, we’ll be able to announce specific partnerships.”
One example he can share publicly is Société Générale, where the team has explored the model in a research and prototype setting. “The signals are not meant to replace existing models,” Yianni emphasizes. “They complement them, helping traders refine timing, improve intraday alpha, and optimize execution.”
In short, the models serve as a real-time enhancement layer, giving clients better insight into short-term market microstructure and improving returns when incorporated into live trading strategies.
Since launching Quantum Signals, what milestones or achievements are you most proud of?
Yianni explains that several key milestones have marked the journey of Quantum Signals so far.
“The first major milestone was raising our pre-seed round,” he says. “We are VC-backed by Quantonation, which has supported several quantum and quantum-inspired companies in this space.” Next came their early work with design partners. “Our collaboration with Société Générale was critical,” Yianni explains. “We worked closely on an early prototype of our system, exploring how real-time order book predictions could be applied in a practical trading setting.”
The most recent milestones, he notes, have been reaching production readiness. “Just in January, we released our first real-time predictions product,” Yianni says. “This system consumes market data in real time, feeds it into our trained models, and generates actionable predictions for traders.” Alongside the product launch, Quantum Signals onboarded its first paying customers. “That was another big milestone,” Yianni emphasizes. “Seeing the system actively used by institutional clients and having them rely on our predictions, is incredibly rewarding.”
For Yianni, these milestones reflect both validation of the technology and the company’s ability to deliver tangible impact in live trading environments.
Looking ahead, how do you see AI and quantum computing shaping the future of short-term market predictions?
Yianni explains that Quantum Signals sees a strong intersection between AI and quantum computing, but emphasizes timing and practical impact. “We are firm believers that quantum computing will shape what’s possible through AI,” he says. “The big question is: when is the right time to start, and when will the technology actually be ready?”
He points out that the pace of hardware development, especially fault-tolerant qubits and larger machines, will determine when quantum-enhanced AI can deliver meaningful improvements. “The challenge for software startups and application developers in quantum computing,” Yianni explains, “is that it’s hard to extrapolate results from small machines to larger ones. You might run a machine learning algorithm on a 30-qubit system and get results, but that doesn’t guarantee it will outperform classical GPUs on a 3000-qubit machine.”
Yianni stresses the importance of evaluating quantum breakthroughs not just through technical benchmarks, but through real-world business impact. “Supremacy experiments, showing a quantum algorithm outperforming a classical benchmark are valuable,” he says. “But the true benchmark is putting a product in the market and seeing if it provides better insights or decision-making than existing classical solutions. That’s when you’re really getting ahead.”
In other words, he sees the future as a combination of rigorous AI innovation and pragmatic deployment: “Technical experiments are one thing,” Yianni explains. “But the ultimate test is cost-benefit and real production use. If a quantum-enhanced system helps you make better decisions in the market, not just faster calculations, that’s when it truly adds value.”
For short-term market predictions, he concludes, AI will drive near-term gains through advanced modeling of microstructure data, while quantum computing holds promise for longer-term leaps, provided it’s evaluated in a practical, market-facing context.
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