Quantum Technology Initiatives

Quantum computing is stepping out of theory and into operations, but scaling raw qubits alone will not get us there. At Cisco Live in San Diego, I spoke with Vijoy Pandey, SVP and GM leading Outshift by Cisco, about how Cisco is tackling the other side of quantum: not just more qubits, but the secure, practical networks that will let quantum processors work together in the real world. Here are a few takeaways from our conversation. 1️⃣ Cisco’s Quantum Network Entanglement Chip works at telecom wavelengths, runs at room temperature, and rides over existing fiber. No exotic lab conditions. No theoretical hurdles. It is built for deployment, not just demonstration. 2️⃣ Quantum networking is not only future-proofing computing for the million-qubit horizon. It is already improving classical systems today through quantum-secure communications, ultra-precise time synchronisation, and stronger trust in critical infrastructure. 3️⃣ Perhaps most importantly, Cisco’s design is vendor-neutral and hardware-agnostic. They are not picking a winner in the qubit race. They are building the roads that every quantum car can use. 4️⃣ If we want quantum computing to leave the lab behind, we need more than breakthroughs in physics. We need the same reliable connectivity and interoperability that scaled the internet in the first place. Cisco sees that as its role. Is your infrastructure ready for quantum? I would love to hear how your organisation is planning for quantum or if you are waiting to see who builds the roads first. Let’s discuss. https://lnkd.in/erAYkP27 #Technology #Future #Podcast

#Supercomputers are great at solving big problems, but some tasks like simulating molecules for drug discovery or optimizing supply chains become impossibly complex. That’s where #quantum computing (#QC) comes in! Instead of replacing traditional computing, quantum processors (QPUs) act as "assistants," speeding up specific calculations while supercomputers handle everything else. At Oak Ridge National Laboratory (ORNL) and the U.S. Department of Energy (DOE), researchers (ref. in comments below) are developing a flexible Quantum Framework (QFw) to connect quantum ("q- " in the whole post) and classical systems in a seamless way to allow: 1) Speed up simulations for e.g. drug discovery, artificial intelligence... 2) Use #QC in real-world applications without being q-experts. 3) Benchmark and compare different #QC platforms in a hardware-neutral way. How it works ❓ 👩💻Hardware-Agnostic Design 💫 The system is designed to work with any #QC platform, whether it’s a real q-device or a q-simulator (software that mimics a #QC on a traditional machine). 💫 Users can write their q-programs using different tools like Qiskit (IBM’s q-software), Pennylane (popular for q-machine learning), or CUDA-Q (NVIDIA’s q-toolkit). 💫 To ensure compatibility across different systems, the framework translates these programs into a common language called OpenQASM or QIR (similar to how websites work on different browsers). 👩💻 To use quantum computers efficiently this system: 1) Reserves "Classical" Computing Power (#HPC nodes) for regular code. 2) Reserves "Quantum" Processing Units (QPUs) for running q-parts. 3) Uses MPI (Message Passing Interface) to allow supercomputers and #QC to talk to each other and share results. 4) Uses SLURM (a scheduling system) to manage how computing resources are divided and used efficiently. 👩💻 Ways to Use #QC in Supercomputing 💫 In-Sequence Processing - program runs step-by-step, checking results in the middle and adjusting based on feedback. 💫 Single-Circuit Processing - #QC runs the same experiment many times to collect enough data for a reliable result. It's important as #QCs are prone to noise and errors. 💫 Ensemble-Circuit Processing - a batch of calculations runs in parallel, and all the results are combined at the end. 👩💻 Quantum Task Management: 1) Quantum Task Manager (QTM) prepares the q-calculations, breaking them down into smaller tasks if needed. 2) Quantum Platform Manager (QPM) converts q-tasks into specific instructions for different #QC. It also ensures that real q-hardware is properly calibrated and ready for execution. 👩💻 Simulating #QCs: 💫 Tensor Network Simulators (TN-QVM, ExaTN) help simulate how q-systems behave using advanced mathematics. 💫 State Vector Simulators (NWQ-Sim) track every possible state of a q-system, useful for testing small q-circuits. 😎 Any use cases you're excited about? Join Quantum Security and Defense Working Group and let’s discuss! ☕ #QSECDEF

Interesting one to watch: Iceberg Quantum just unveiled Pinnacle, a fault-tolerant architecture based on quantum LDPC codes that it claims could bring RSA-2048 breaking down from millions of physical qubits to under 100,000. What makes this notable beyond the headline claim is the vendor-agnostic approach - they're already partnering with PsiQuantum (photonics), Diraq (spin qubits), and IonQ (trapped ions), all of which have projected timelines to build systems at this scale within 3–5 years. If these overhead reductions hold under real hardware conditions, it meaningfully compresses the timeline to cryptographically relevant quantum computers - which has direct implications for anyone planning post-quantum migration strategies. Particularly relevant in the #YQS Matthew Cimaglia The $6M seed round was led by LocalGlobe with Blackbird and DCVC (nice one Prineha Narang!) https://lnkd.in/g6rnmEHm

QNodeOS, the first operating system designed specifically for quantum networks, represents a major step toward practical distributed quantum computing. Developed by members of the Quantum Internet Alliance (QIA)—including TU Delft, QuTech, University of Innsbruck, INRIA, and CNRS—this new system aims to standardize and simplify quantum network development, much like classical operating systems did for traditional computing. Unlike quantum computers, which perform calculations using quantum bits (qubits) with properties like superposition and entanglement, quantum networks are designed to connect these computers, enabling secure communication, distributed computing, and advanced quantum protocols. Until now, quantum network software has been hardware-specific and fragmented, limiting the scalability of quantum applications. QNodeOS solves this by introducing a hardware-agnostic, platform-independent framework, allowing developers to create quantum applications without needing deep knowledge of the underlying hardware. The operating system’s key functions include managing quantum information flow, synchronizing entanglement across multiple nodes, and coordinating devices in a quantum network. By abstracting away low-level quantum operations, QNodeOS provides a high-level programming environment, making it easier to develop, test, and deploy quantum network applications. This breakthrough lays the groundwork for the future of distributed quantum computing, where quantum devices can work together over vast distances. As quantum internet technology advances, QNodeOS could play a critical role in enabling applications such as ultra-secure quantum communication, cloud-based quantum computing, and advanced quantum sensing networks. With this development, the vision of a fully functional quantum internet is moving closer to reality.

𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗼𝗿-𝗯𝗮𝘀𝗲𝗱 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿 𝗮𝗻 𝗔𝗪𝗚 𝗶𝘀 𝗹𝗶𝗸𝗲 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗮 𝗖𝗣𝗨 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿. Sure, both output waveforms—but only one runs logic, adapts in real time, and responds to live data. Yet, many systems still treat waveform generation as static: precompiled and uploaded in advance, executed without embedded logic or adaptability. 𝗧𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀—𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝘁𝗿𝘆 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲. Because waveforms don’t scale. Logic does. Once you need mid-circuit feedback, fast recalibration, or adaptive sequences, the AWG mindset starts to crack. What’s the alternative? 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 𝘄𝗮𝘃𝗲𝗳𝗼𝗿𝗺 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻—and it’s not just a convenience, it’s an architectural necessity. Rather than uploading every waveform variation from a host PC, you define dynamic pulse envelopes and modulate them with tunable parameters like amplitude, frequency, phase, or duration—directly at runtime. This enables: ✅ Real-time feedback and control flow ✅ Embedded calibration & adaptive sequences ✅ Minimal overhead and maximal flexibility Waveform generation becomes logic—not playback. This shift from waveform playback to real control is what enables dynamic algorithms, mid-circuit resets, real-time error correction—and ultimately, fault tolerance. It's time to retire the AWG mindset.

Under the streets of Manhattan and Brooklyn. Through 60 Hudson, one of the most connected carrier hotels in the world. Real quantum entanglement at scale on 17.6 km of standard telecom fiber. With swapping rates 3+ orders of magnitude beyond prior efforts and fidelity above 99%. This is the full quantum networking stack coming together — hardware, protocol, control, orchestration. Most importantly, we ran this without the shared laser crutch that makes lab experiments unscalable by design. This real-world demo used fully independent quantum sources at each endpoint. With Cisco's quantum software stack handling timing coordination at picosecond precision across three geographically separated nodes using the White Rabbit protocol. Qunnect's room-temperature hardware at the edges. And cryogenic equipment only at the hub for efficiency. Any new nodes could be added to this network without touching the sync infrastructure. And with clean control and data plane separation.   Applying design patterns that scaled the classical internet to quantum networking. I wrote about what this milestone means and how it leads us one step closer to our vision of a quantum data center network, on the Cisco blog today. 🔗 Link in comments. 📸 Photo of Manhattan from the Brooklyn end, by me.

⚛️ Quantum Networking Fundamentals: From Physical Protocols to Network Engineering 📜 The realization of the Quantum Internet promises transformative capabilities in unconditionally secure communication, distributed quantum computing, and high-precision quantum metrology. However, transitioning from isolated laboratory experiments to a scalable, multi-tenant network utility introduces deep orchestration challenges. Current development is largely siloed within the physics and optics communities, prioritizing hardware fidelities and photon sources, while theclassical networking community lacks the architectural models required to dynamically manage these fragile quantum resources. This tutorial bridges this disciplinary divide by providing a comprehensive, network-centric view of quantum networking. We systematically dismantle the idealized assumptions prevalent in current network simulators to directly address the “simulation–reality gap,” and we recast them as explicit control-plane constraints. To bridge this gap, we establish Software-Defined Quantum Networking (SDQN) not merely as an evolutionary management tool, but as a mandatory prerequisite for scale, and we prioritize the orchestration of a symbiotic, dual-plane architecture in which classical control dictates quantum data flow. Specifically, we synthesize reference models for SDQN and the Quantum Network Operating System (QNOS) for hardware abstraction, and we adapt a Quantum Network Utility Maximization (Q-NUM) framework as a unifying mathematical lens to help network engineers reason about the inherent trade-offs between entanglement routing, scheduling, and fidelity targets. Furthermore, we analyze Distributed Quantum AI (DQAI) over imperfect networks as a case study, illustrating how physical constraints such as probabilistic stragglers and decoherence fundamentally dictate application-layer viability. Ultimately, this tutorial equips network engineers with the operational mindset and architectural tools required to transition quantum networking from a bespoke physics experiment into a programmable, multi-tenant global infrastructure. ℹ️ A. Gkelias et al - EEE Department, Imperial College, London, UK -2026

Exciting news from Cisco / Outshift by Cisco Quantum Labs : I just published a blog on our prototype network-aware Quantum Compiler, engineered for distributed quantum data centers (QDCs). This is not just another compiler — it’s built with network connectivity, error correction, scheduling, and cross-device orchestration all baked in. Outshift by Cisco 🔍 Why this matters: Quantum hardware is advancing, but single QPUs alone won’t get us to useful, large-scale quantum workloads. Outshift by Cisco A QDC architecture lets us interconnect multiple QPUs across a network, but that demands new software that can reason about communication, locality, entanglement, and fault tolerance. Outshift by Cisco Our network-aware compiler introduces innovations in: Circuit partitioning with communication awareness Qubit mapping across devices Advanced scheduling of entanglement & gate operations Multi-tenancy & resource allocation in shared quantum compute environments Supprot for distributed error correction you can read my blog here : https://lnkd.in/ey5nuz95 #quantum #quantumcomputing #quantumnetworkign #quantumcompiler

From NVLink to NVQLink: Wiring Quantum Processors into AI Supercomputers NVIDIA just unveiled NVQLink - an open interconnect + software stack that tightly couples quantum processors (QPUs) with AI supercomputers for real-time hybrid workflows like calibration and quantum error correction (QEC). It's not a quantum computer from NVIDIA, it's the missing fast path between QPUs and today's accelerated systems so the two can work as one. ✅ What is NVQLink exactly? A hardware + software integration path that links QPUs to NVIDIA GPU/CPU systems with low-latency, high-throughput data movement and real-time control via CUDA-Q (formerly CUDA-Quantum). Performance (NVIDIA-stated): up to 400 Gb/s GPU↔QPU throughput and <4 μs minimum round-trip latency in a reference (FPGA→GPU→FPGA) loop, sized for fast feedback tasks like QEC decoders and calibration. ✅ Why do we need NVQLink? Quantum isn't standalone: to be useful, QPUs depend on classical compute for: 🔹 Calibration and drift tracking, 🔹 Real-time QEC decoding and control, 🔹 Logical program orchestration (dynamic routing, lattice surgery, just-in-time compilation). All three are latency-critical control loops. NVQLink provides the speed/scale so GPUs can run these loops in real time while QPUs stay coherent. NVIDIA's message is hybrid is the future: supercomputers + QPUs co-evolve. quantum doesn't replace GPU systems. ✅ How does NVQLink work? 🔹 A QPU (the quantum chip) is driven by nearby control electronics that send precise pulses and read measurements. 🔹 NVQLink is the fast lane between that controller and the GPU, so results from the QPU reach the GPU in microseconds and new commands go back just as fast. 🔹 CUDA-Q is the programming layer: you write one hybrid program where the QPU does the quantum steps, and the GPU does the heavy classical math (like error-correction and optimization). 🔹 Inside the AI node, NVLink/NVSwitch connects GPU↔GPU at very high bandwidth. NVQLink connects QPU↔GPU for tight, real-time control. ✅ Where does it fit inside today's GPU systems? In a Blackwell/NVLink-5 cluster (or CPU+GPU nodes), GPUs already share data over NVLink/NVSwitch at TB/s. NVQLink brings the QPU/control side into that world: measurement results flow quickly to GPUs. GPU decoders/control kernels send decisions back within microseconds, the rest of the AI stack (simulation, scheduling, ML-based decoders) runs on the same accelerated node. Think of NVQLink as the southbridge to quantum: it's the tight, deterministic path between the quantum device and the GPU side where the heavy classical algorithms live. Nvidia NVQLink: https://lnkd.in/gYr4xZk3