Telefónica Tech partnered with three European companies, Qilimanjaro Quantum Tech, Multiverse Computing, and Qcentroid, setting new waves of quantum AI projects that will change model development across distributed environments, while its parent company, Telefónica pushes automation and network upgrades to unlock new revenue streams.
The collaboration will advance quantum AI projects that incorporate cutting-edge computation with live deployment.
The initiative targets more efficient and compact AI systems capable of operating closer to where data is generated by combining quantum hardware, advanced algorithms, and orchestration platforms, reinforcing quantum telecom ambitions.
The news mimics the European push to scale quantum computing in AI all while minimizing dependence on centralized, resource-heavy AI models and unlocking quantum AI industry potential.
Quantum-AI Convergence Meets Enterprise Demand
The integration of quantum computer and AI can alleviate the computational burden associated with large-scale AI models. Faster processing speeds could not only enhance accuracy but also significantly reduce the time needed to solve complex problems, from simulations to rapid prototyping, further strengthening quantum AI projects and accelerating AI quantum investment.
Each partner brings specialized expertise: quantum systems, software optimization, and deployment frameworks. Together, they will enable AI systems that are not only powerful, but also deployable across edge environments, a key milestone for AI quantum evolution.
It could unlock new enterprise use cases where latency, energy efficiency, and data sovereignty are critical, reinforcing the quantum computing AI potential and expanding quantum AI projects.
Telefónica Tech’s role is to integrate these capabilities into enterprise-ready solutions, positioning itself as a key orchestrator in the next wave of digital transformation driven by quantum computer and AI .
The initiative aligns with growing demand for localized AI infrastructure that can operate securely within national or regional boundaries, highlighting the rise of these ecosystems and the expansion of quantum computing in AI strategies.
Automation Upgrades for Monetization
Alongside its AI quantum push, Telefónica is accelerating efforts to modernize its network infrastructure to address a persistent challenge: rising traffic with stagnant revenues.
Speaking at the Network Operator Summit at OFC, Chief Technology Officer Cayetano Carbajo Martin outlined a strategy centered on automation, transport upgrades, and optical innovation.
“We are trying to implement the technical lever for increasing our revenue,” Martin stated. “We are pushing our network for increasing monetization.”
A cornerstone of this effort is the Autonomous Network Journey (ANJ) launch of 2021, which aims to achieve Level 4 network autonomy by 2030. This would transition operations from manual and error-prone processes to self-optimized systems powered by AI and software-defined networking (SDN), complementing advances in quantum AI projects.
“Telefónica has been one of the few operators that has committed to reach Level 4 of autonomy by 2030,” Martin said, emphasizing the company’s public commitment to shareholders and analysts.
AI is already being integrated into operations through use cases such as natural language interfaces, automated root cause analysis, and digital twins. These technologies are expected to improve efficiency while enabling more proactive and predictive network management.
In parallel, Telefónica is undertaking a major transport network upgrade and AI quantum, by moving its backbone to 400G and backhaul to 100G to support growing data demands. The company is also accelerating its shift toward IP over DWDM (IPoDWDM), aiming to migrate 60% of its optical capacity to coherent pluggable optics by 2027.
Together, these initiatives reflect a dual strategy, harnessing next-generation computing while transforming network infrastructure to sustain growth in the AI era, with quantum AI projects at the center of long-term innovation.
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