Resource Optimization

Quantum computing hit a wall. Photonics became the way around it. Just published in Laser Focus World my latest analysis on why quantum networking isn't just the future—it's the make-or-break technology happening RIGHT NOW. Key insights from Global Quantum Intelligence, LLC's research: 💡 Module size limits are non-negotiable: Every quantum platform hits a hard ceiling for how many qubits can fit in a single module. Superconducting circuits face cooling constraints at ~3,000 qubits per fridge. Trapped ions destabilize beyond 100-qubit 1D chains. Neutral atoms run into optical aperture limits at 10,000. Silicon spins promise millions on paper but haven't proven thermal management. The message is clear: scaling requires networking modules, not building bigger ones. 🔗 The modular revolution arrived faster than expected: While the industry chased monolithic designs, we called the distributed future in our May 2024 report: https://lnkd.in/gkbB7Txu Twelve months later, the evidence is overwhelming: Xanadu networked quantum modules across 13km of urban fiber. PsiQuantum achieved 99.72% chip-to-chip fidelity. IonQ transformed from a compute-only player into a full-stack quantum networking company through strategic acquisitions. 💰 Capital followed the technical breakthroughs: Welinq hit 90% quantum memory efficiency. Nu Quantum shipped the first rack-mounted QNU. Sparrow Quantum raised €21.5M for deterministic photon sources. Cisco jumped in with room-temperature chips producing 200 million entangled photon pairs per second. This isn't early-stage speculation—it's a race to build infrastructure. Players making it happen: Xanadu PsiQuantum Nu Quantum Welinq Sparrow Quantum Lightsynq IonQ Cisco Oxford Ionics ID Quantique Photonic Inc. QphoX Oxford Quantum Circuits (OQC) SilQ Connect Qunnect memQ Single Quantum Quantum Opus LLC Aegiq ORCA Computing Quandela QuiX Quantum Quantum Source If you're in photonics, this is it. You're not just making components anymore—you're building the backbone that makes million-qubit machines possible. Miss this wave, and you're watching from the sidelines. Full article: https://lnkd.in/g3pYEeqc #QuantumComputing #Photonics #QuantumNetworking #DeepTech #Innovation #FutureOfComputing

AI is now turning decades of "fragmented reports" into a foundation for global resilience. For many climate hazards, the high-fidelity historical data needed to train predictive models simply didn't exist. Today, Google Research is introducing Groundsource to bridge that gap. While we are starting with urban flash floods, the broader opportunity is to create a rigorous scientific baseline for hazards that traditional sensors often miss. By using Google Gemini to synthesise over 25 years of public information in 80 languages, we’ve demonstrated a scalable way to turn unstructured history into actionable intelligence. How this AI-driven methodology scales climate adaptation: 🧩 Solving the Data Gap: It creates a "ground truth" for regions lacking physical infrastructure, ensuring that no community is left behind in the era of AI-driven resilience. 🗺️ A Scalable Blueprint: This framework is a catalyst; while we've mapped 2.6 million flood events, the same methodology can be applied to landslides, heat waves, and other climate-related threats. 🔮 Predictive Power: This research is already powering 24-hour lead times for flash flood alerts on Flood Hub, giving cities a critical head start. By open-sourcing this benchmark, we are inviting the global sustainability community to help turn the records of the past into a more resilient future. https://lnkd.in/eSRvneuE #ClimateResilience #Sustainability #GoogleResearch #FlashFlood #Gemini #Adaptation

⚛️ Sequential Quantum Computing 📑 We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome the limitations of each type of quantum computer by combining their complementary strengths. Current quantum devices, including analog quantum annealers and digital quantum processors, offer distinct advantages, yet face significant practical constraints when individually used. SQC addresses this by efficient inter-processor transfer of information through bias fields. Consequently, measurement outcomes from one quantum processor are encoded in the initial-state preparation of the subsequent quantum computer. We experimentally validate SQC by solving a combinatorial optimization problem with interactions up to three-body terms. A D-Wave quantum annealer utilizing 678 qubits approximately solves the problem, and an IBM’s 156-qubit digital quantum processor subsequently refines the obtained solutions. This is possible via the digital introduction of non-stoquastic counterdiabatic terms unavailable to the analog quantum annealer. The experiment shows a substantial reduction in computational resources and improvement in the quality of the solution compared to the standalone operations of the individual quantum processors. These results highlight SQC as a powerful and versatile approach for addressing complex combinatorial optimization problems, with potential applications in quantum simulation of many-body systems, quantum chemistry, among others. ℹ️ Romero et al - 2025

230 million people just learned the hard way that "I hope the forecast is wrong" is not a resilience plan. One winter storm, 2000 miles wide, just pressure tested North America. From northern Mexico, to Atlantic Canada: • record snow from Arkansas to Ontario • "catastrophic" ice across the South • freeze warnings as far as the Gulf Early damage estimates are in the tens of billions of dollars, with some projections cracking $100B On paper, a lot of this was "unlikely". But in reality, it was inevitable. Inevitable because we plan to the average in a world that’s ruled by extremes. We design subdivisions, houses, farms, and infrastructure using 30‑year normals and “100‑year” events… …in a climate that is no longer normal, and where “unprecedented” is getting precedent. We lean on: • 30 year climate "normals" • 100-year storm curves • historical snow loads and frost dates But these distributions are dynamic and shifting, and the tails of those curves are getting bigger. We've historically been optimizing for the middle of the bell curve, and pretend the edges are someone else's problem. Rather than asking: "What's typical here?" We need to be asking: "What's the worst plausible combinations this site could see in its lifetime?" If we want land systems that actually hold under pressure, we have to flip the script. We need to treat extremes as the design teacher and averages as background noise. We no longer have the luxury of designing for "normal". The pattern is already here, in plain sight. "Unprecedented" events are starting to arrive on a schedule. So, do we: keep designing for "normal" and acting surprised every time, or start designing for extremes and finally call it resilience? (screenshot credit: earth . nullschool . net -> link in comments)

Parity Quantum Optimization: Encoding Constraints “Constraints to optimization problems are crucial for many problems that are encountered in science, technology, and industry, ranging from scheduling problems to quantum chemistry. Quantum computing as a new paradigm of computing, which aims, among other things, at enhancing optimization algorithms by making use of quantum phenomena, may improve upon existing algorithms to solve these kinds of problems. However, quantum computers are limited in coherence, control, and connectivity which makes encoding of optimization problems one of the current grand challenges in the field. Constraints are an additional complication to the encoding challenge and they are typically encoded via large energy penalties given as quadratic terms leading to fully connected interactions. “ “To encode constraints we introduce a combination of exchange interactions and spin-flip terms in combination with the parity encoding. The parity trans-formation encodes optimization problems in a lattice gauge model with local 3-body and 4-body interac-tions on a square lattice. We introduce exchange terms that only act on qubits that are part of the constraints and spin-flip terms that act on the rest of the qubits. Using a compiler , qubits can be arranged on the square lattice with flexibility.”   By Maike Drieb-Schön , Kilian Ender , Younes Javanmard, and Wolfgang Lechner   ParityQC Universität Innsbruck Link https://lnkd.in/dJZknhiN

Quantum Scaling Recipe: ARQUIN Provides Framework for Simulating Distributed Quantum Computing Systems Key Insights: • Researchers from 14 institutions collaborated under the Co-design Center for Quantum Advantage (C2QA) to develop ARQUIN, a framework for simulating large-scale distributed quantum computers across different layers. • The ARQUIN framework was created to address the “challenge of scale”—one of the biggest hurdles in building practical, large-scale quantum computers. • The results of this research were published in the ACM Transactions on Quantum Computing, marking a significant step forward in quantum computing scalability research. The Multi-Node Quantum System Approach: • The research, led by Michael DeMarco from Brookhaven National Laboratory and MIT, draws inspiration from classical computing strategies that combine multiple computing nodes into a single unified framework. • In theory, distributing quantum computations across multiple interconnected nodes can enable the scaling of quantum computers beyond the physical constraints of single-chip architectures. • However, superconducting quantum systems face a unique challenge: qubits must remain at extremely low temperatures, typically achieved using dilution refrigerators. The Cryogenic Scaling Challenge: • Dilution refrigerators are currently limited in size and capacity, making it difficult to scale a quantum chip beyond certain physical dimensions. • The ARQUIN framework introduces a strategy to simulate and optimize distributed quantum systems, allowing quantum processors located in separate cryogenic environments to interact effectively. • This simulation framework models how quantum information flows between nodes, ensuring coherence and minimizing errors during inter-node communication. Implications of ARQUIN: • Scalability: ARQUIN offers a roadmap for scaling quantum systems by distributing computations across multiple quantum nodes while preserving quantum coherence. • Optimized Resource Allocation: The framework helps determine the optimal allocation of qubits and operations across multiple interconnected systems. • Improved Error Management: Distributed systems modeled by ARQUIN can better manage and mitigate errors, a critical requirement for fault-tolerant quantum computing. Future Outlook: • ARQUIN provides a simulation-based foundation for designing and testing large-scale distributed quantum systems before they are physically built. • This framework lays the groundwork for next-generation modular quantum architectures, where interconnected nodes collaborate seamlessly to solve complex problems. • Future research will likely focus on enhancing inter-node quantum communication protocols and refining the ARQUIN models to handle larger and more complex quantum systems.

QUANTUM COMPUTERS RECYCLE QUBITS TO MINIMAZE ERRORS AND ENHANCE COMPUTATIONAL EFFICIENCY Quantum computing represents a paradigm shift in information processing, with the potential to address computationally intractable problems beyond the scope of classical architectures. Despite significant advances in qubit design and hardware engineering, the field remains constrained by the intrinsic fragility of quantum states. Qubits are highly susceptible to decoherence, environmental noise, and control imperfections, leading to error propagation that undermines large‑scale reliability. Recent research has introduced qubit recycling as a novel strategy to mitigate these limitations. Recycling involves the dynamic reinitialization of qubits during computation, restoring them to a well‑defined ground state for subsequent reuse. This approach reduces the number of physical qubits required for complex algorithms, limits cumulative error rates, and increases computational density. Particularly, Atom Computing’s AC1000 employs neutral atoms cooled to near absolute zero and confined in optical lattices. These cold atom qubits exhibit extended coherence times and high atomic uniformity, properties that make them particularly suitable for scalable architectures. The AC1000 integrates precision optical control systems capable of identifying qubits that have degraded and resetting them mid‑computation. This capability distinguishes it from conventional platforms, which often require qubits to remain pristine or be discarded after use. From an engineering perspective, minimizing errors and enhancing computational efficiency requires a multi‑layered strategy. At the hardware level, platforms such as cold atoms, trapped ions, and superconducting circuits are being refined to extend coherence times, reduce variability, and isolate quantum states from environmental disturbances. Dynamic qubit management adds resilience, with recycling and active reset protocols restoring qubits mid‑computation, while adaptive scheduling allocates qubits based on fidelity to optimize throughput. Error‑correction frameworks remain central, combining redundancy with recycling to reduce overhead and enable fault‑tolerant architectures. Algorithmic and architectural efficiency further strengthens performance through optimized gate sequences, hybrid classical–quantum workflows, and parallelization across qubit clusters. Looking ahead, metamaterials innovation, machine learning‑driven error mitigation, and modular metasurface architectures promise to accelerate progress toward scalable systems. The implications of qubit recycling and these complementary strategies are substantial. By enabling more complex computations with fewer physical resources, they can reduce hardware overhead and enhance reliability. This has direct relevance for domains such as cryptography, materials discovery, pharmaceutical design, and large‑scale optimization.

How can we minimize the entanglement distribution delay in quantum networks incorporating quantum switches that have limited resources and can perform entanglement distillation? Our recent work addresses this key question: https://lnkd.in/eybzxsTe In particular, we develop a solution that explicitly incorporates loss, quantum noise, and imperfections during entanglement generation, transmission, and storage. By managing quantum memories to meet diverse user requirements and exploring practical deployment scenarios using NV centers in diamond, our framework accounts for realistic impacts and optimizes based on isotopic decomposition and nuclear spin interactions. This work is a key step toward practical, physics-informed developments in quantum networks that enable future applications in quantum computing and communications. Mahdi Chehimi #quantumcommunications #quantumcomputing

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

Most people learning quantum software start with circuits. That is useful. But for fault-tolerant quantum computing, I think one skill is becoming just as important: Resource Estimation. Because a quantum algorithm is not only a circuit. It is also a set of engineering tradeoffs: - How many logical qubits? - How many physical qubits? - How deep is the computation? - What error-correction assumptions are being made? - Which part of the workflow is actually the bottleneck? This matters because a small algorithm on paper can become a very large system-level problem once you ask what it takes to run reliably. That is the mental model shift. Quantum software is moving from: "Can I write the circuit?" to: "Can I understand what this circuit would cost at a fault-tolerant scale?" That is why tools for circuit design, simulation, and resource analysis matter. They help developers ask better questions before useful hardware is fully here. The future quantum developer may need to know not only gates and algorithms. They may also need to think like a systems engineer: - estimate resources - identify bottlenecks - compare architectures - understand error correction - connect algorithms to real-world constraints Hardware gets the headline. Resource estimation tells you whether the idea has a path to becoming useful. If you are learning quantum software today, do not stop at "how do I build this circuit?" Also ask: "What would it take to run this reliably?" That question is where quantum software starts becoming engineering. #QuantumComputing #QuantumSoftware #FaultTolerantQuantum #DeepTech