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Quantum Computing

Quantum Computing Telecommunication Use New Technologies

Posted on October 24, 2025 by Jettipalli Lavanya5 min read
Quantum Computing Telecommunication Use New Technologies

Telecommunications Companies Use Emerging Technologies to Protect Margin and Innovate in the Quantum Leap.

Quantum computing telecommunication

A phase of structural recalibration is currently underway in the global telecommunications sector, which is frequently referred to as the invisible architecture of the modern world. Due to the saturation of traditional connectivity-based business models, top operators are being forced to recast themselves as technology firms by incorporating cloud services, artificial intelligence, and, more and more, quantum technologies. This change is crucial for strategic differentiation, cybersecurity defences, and new efficiency in addition to protecting market dominance.

Even while networks are quicker and denser than ever, the industry’s basis is encountering scalability issues. Global telecom service revenue is predicted to grow 2.9% annually until 2028, according to Global Telecom Outlook 2024-2028. This rate is usually lower than expected inflation in large economies, indicating structural stagnation. Increased competition from hyperscalers like AWS, Google Cloud, and Microsoft Azure, which are providing edge computing solutions that can circumvent traditional operators, and significant capital expenditures (CapEx) on 5G densification and fibre rollout that do not yield proportionate returns are contributing factors.

Also Read About Introduction To Loop Quantum Gravity & String Theory Vs LQG

Market Stagnation Forces Strategic Overhaul

Competitive distinction in this context has been reduced to fundamental elements like cost, coverage, and dependability. Operators now need to look for value generation further up the value chain, which calls for extensive knowledge of cybersecurity, cloud architecture, and artificial intelligence. In three areas that are directly related to telecom operations, data analytics, security, and network optimization, quantum technology is showing promise as a differentiator.

Quantum: The Tri-Part Differentiator

For some kinds of computationally costly, or NP-hard, tasks, including network design, resource scheduling, and traffic management, quantum computing is anticipated to outperform classical computers in network optimisation. Vodafone, for instance, recently showed how to optimize fibre routing using a photonic quantum computer.

Quantum capabilities are driving the shift to quantum-safe communications in the cybersecurity space. This includes two methods: Quantum Key Distribution (QKD), which uses quantum mechanics to secure key exchange at the physical layer, and Post-Quantum Cryptography (PQC), which uses mathematically secure algorithms implemented via software upgrades.

Regional efforts are already showing promise: BT operates a commercial QKD-secured metro network in London, while SK Telecom operates one of the largest QKD networks in the world, linking 48 public-sector organisations.

Quantum machine learning (QML) may improve anomaly identification and predictive maintenance in complicated networks, according to first tests in data analytics. Because minor network losses can result in expensive service interruptions, these systems are essential. A QML model that can detect network performance decline more quickly than conventional deep learning methods has been piloted by Telstra in Australia.

Boosting Efficiency and Intelligence with Hybrid Systems

Researchers from Ericsson have discussed the possible benefits of quantum computing in telecom networks, looking into multi-chip quantum processors and quantum techniques for a range of telco workloads. In order to provide a computational advantage to network planning, control, and execution, they envision quantum computers residing in data centres and co-processing alongside classical computers.

Peak-to-Average Power Ratio (PAPR) minimisation and Maximum Likelihood MIMO (Multiple-Input, Multiple-Output) detection are two specific problems studied in the radio domain. A computational benefit over classical approaches was seen when small instances of these optimisation tasks were translated to a quantum annealer. Modern dual-socket servers running parallel simulated annealing could counteract the 29x speedup obtained for PAPR minimisation in a 2×2 MIMO system when compared to a single-threaded classical QUBO solution.

Additionally, researchers used quantum approaches to optimise antenna tilt, a challenging challenge that aims to balance coverage, quality, and capacity. When a Quantum Neural Network (QNN) was used in place of a traditional deep-Q network, it was able to achieve comparable prediction accuracy to a classical artificial neural network while using 20 times fewer trainable parameters and fewer data points. Training machine learning models in the quantum realm may lower training overhead, as indicated by the decrease in trainable parameters and data points.

The Roadmap to Quantum-Native Infrastructure

Ericsson suggests using quantum computers as cloud-native coprocessors in order to overcome the limitations of the Noisy Intermediate-Scale Quantum (NISQ) era, where qubits are not yet fault-tolerant. These coprocessors might be multi-chip Quantum Processing Units (QPUs), which provide higher computational fidelity than single-chip systems by transferring information between chips over a quantum communication channel. The cooperation between classical and quantum computers in this hybrid technique confirms that the quality of solutions for huge problem instances may increase.

Three overlapping phases are anticipated to be experienced by the industry as it integrates quantum technology:

  1. Pilot Phase (2023–2026): Concentrates on quantum optimization and proofs-of-concept for QKD.
  2. Hybrid Integration (2026–2030): This entails testing quantum-enhanced AI models and integrating PQC standards into network software.
  3. Quantum-Native Infrastructure (Beyond 2030): Beyond 2030, Quantum-Native Infrastructure will incorporate computing nodes and specialised quantum communication channels straight into telecom networks.

The successful operational integration of quantum and AI capabilities at scale will ultimately determine the telecom industry’s success over the next ten years. Although it is not anticipated that quantum technologies will revolutionise networks right away, they are propelling the next phase of competition in which price and bandwidth are less important differentiators than flexibility, efficiency, and trust. Ericsson’s study concludes that only when quantum computers are scalable and fault-tolerant will they be able to contribute to the next generation of telco network infrastructure.

Also Read About What Are Subatomic Particles And How It Is Used As Qubits

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Written by

Jettipalli Lavanya

Jettipalli Lavanya is a technology content writer and a researcher in quantum computing, associated with Govindhtech Solutions. Her work centers on advanced computing systems, quantum algorithms, cybersecurity technologies, and AI-driven innovation. She is passionate about delivering accurate, research-focused articles that help readers understand rapidly evolving scientific advancements.

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