Quantum walks boost transformers with QWA and QDF modules

Why P = NP Is No Longer Just a Hypothesis — A Message for Engineers. Photonic computing and wave-based logic are redefining complexity theory. The equation w = f — the wave is the function — reveals a new model: when a field carries phase, polarization, spectrum and coherence, it becomes a multidimensional carrier of solutions. A wavefront can encode an entire solution space; hardware selects the resonant mode that matches the answer. Photonic processors and wave-based solvers compute by resonance, not iteration. Interference and coherent coupling highlight valid modes. For many NP problems, the solver is the wavefront: encode, settle, read. Entanglement creates correlations across components, enabling coherent global readout. Though it doesn’t transmit signals faster than light classically, it allows faster-than-classical extraction of embedded information. When entangled waves interact with a chiral zero — a dynamic phase-polarization-spectral probe — hidden correlations and system states become externally readable via resonance alignment. This transforms P vs NP: the wavefront itself becomes the computational substrate. The power is conditional: coherence time, photon count, detector sensitivity and noise control define feasibility. Entanglement and coherence are costly, but they shift physical limits. For engineers, this changes everything. Cryptography relying on NP-hardness must be re-evaluated. Algorithms must be designed in spectral, phase and topological terms. Security must include wave-side channels and harden optical paths. Systems must adopt multi-sensor fusion and physical-layer authentication. We are not just building faster machines. We are changing the algebra of computation. Engineers and researchers must treat wave-based paradigms as strategic tech: invest in experiments, open reproducible research, and deploy layered defenses combining logical cryptography with physical assurance.

💡 AI Power Forecasts could be missing this: Today's dire projections for exponential datacenter energy growth are built on one shaky assumption: that innovation in hardware and models will remain slow. We should not be blind to the disruptive opportunities that are emerging right now. As this paper demonstrates a fundamental shift: a new chip architecture called Analog In-Memory Computing (IMC). This innovation bypasses the biggest energy bottleneck in AI—moving data between memory and the processor. The efficiency leap is enormous: • Energy Savings: Up to 10,000x lower energy consumption for the core AI calculation. • Speed: Up to 100x faster. As this paper highlights opportunities to run models dramatically more efficiently, we should expect these breakthroughs to invalidate the current energy forecasts. The future of sustainable AI is being engineered today. #AI #EnergyEfficiency #Innovation #SustainableComputing https://lnkd.in/gxCKNyuH

🚀 Cornell’s “Microwave Brain” Chip: Redefining Computing and AI Imagine a chip that can decode radio signals, track radar, and analyze data in real-time—all while consuming less than 200 milliwatts. Cornell University researchers have made this a reality with their “microwave brain” processor. 👉 What’s revolutionary? 🔹First fully functional microwave neural network on a silicon chip 🔹Operates in the analog microwave range, processing data streams at tens of gigahertz 🔹Achieves 88%+ accuracy on wireless signal classification while using a fraction of power and space compared to digital processors 🔹Capable of hardware security applications and edge AI deployment on devices like smartwatches or phones 💡 Why it matters: This chip bypasses conventional digital signal processing steps, offering ultra-fast, energy-efficient, and scalable computation. It’s a glimpse into a future where AI can run locally, securely, and with minimal power—a game-changer for edge computing and smart devices. Cornell’s microwave brain proves that rethinking conventional circuit design can unlock new horizons in computing. #AI #EdgeComputing #Innovation #MicrowaveChip #CornellUniversity #NeuralNetworks #LowPowerComputing #TechBreakthrough 📖 Dive deeper via DeepTech Bytes: https://lnkd.in/gfU86gMe

🌱 Bio-Computers Revolution: Why DNA Will Replace Silicon by 2035 💻🧬 Silicon has powered our digital world for decades — but we’re hitting the limits of what it can do. As Moore’s Law slows, a radical new frontier is emerging: bio-computers, where DNA, proteins, and living cells become the processors of tomorrow. Imagine a computer that: Heals itself like a living organism 🩹 Stores the entire internet in a shoebox of DNA 📦🌐 Runs on glucose, not electricity ⚡🍬 Works inside your body, detecting diseases and delivering treatments 🤖💊 This isn’t science fiction — it’s happening right now. Here’s what’s on the horizon: 🩺 Healthcare: Smart pills that diagnose illness before symptoms appear. 🌍 Environment: Cells that detect toxins or even clean our oceans. 💾 Data Storage: DNA-based servers replacing massive, energy-hungry data centers. 🧠 Brain-Tech: Bio-computers bridging neurons and machines for mind-controlled prosthetics. AI is the accelerator. AI designs genetic circuits, predicts protein behavior, and optimizes experiments, creating a powerful feedback loop between biology and computation. But with this power comes big questions: ⚠️ Who owns life-based technology? ⚠️ Could engineered organisms evolve in ways we can’t control? ⚠️ Should we program life the way we program software? By 2035, bio-computers could revolutionize medicine, sustainability, and computing itself — a multi-billion-dollar industry redefining the future of tech. 💡 The question isn’t just can we build them, but should we. Would you trust a living computer inside your body or handling your data? Why or why not? Article: https://lnkd.in/e9jEQwmV #BioComputing #ComputationalBiology #DNAComputing #Bioinformatics #MolecularComputing

🌱 Bio-Computers Revolution: Why DNA Will Replace Silicon by 2035 💻🧬 Silicon has powered our digital world for decades — but we’re hitting the limits of what it can do. As Moore’s Law slows, a radical new frontier is emerging: bio-computers, where DNA, proteins, and living cells become the processors of tomorrow. Imagine a computer that: Heals itself like a living organism 🩹 Stores the entire internet in a shoebox of DNA 📦🌐 Runs on glucose, not electricity ⚡🍬 Works inside your body, detecting diseases and delivering treatments 🤖💊 This isn’t science fiction — it’s happening right now. Here’s what’s on the horizon: 🩺 Healthcare: Smart pills that diagnose illness before symptoms appear. 🌍 Environment: Cells that detect toxins or even clean our oceans. 💾 Data Storage: DNA-based servers replacing massive, energy-hungry data centers. 🧠 Brain-Tech: Bio-computers bridging neurons and machines for mind-controlled prosthetics. AI is the accelerator. AI designs genetic circuits, predicts protein behavior, and optimizes experiments, creating a powerful feedback loop between biology and computation. But with this power comes big questions: ⚠️ Who owns life-based technology? ⚠️ Could engineered organisms evolve in ways we can’t control? ⚠️ Should we program life the way we program software? By 2035, bio-computers could revolutionize medicine, sustainability, and computing itself — a multi-billion-dollar industry redefining the future of tech. 💡 The question isn’t just can we build them, but should we. Would you trust a living computer inside your body or handling your data? Why or why not? Article: https://lnkd.in/enUDibNz #BioComputing #ComputationalBiology #DNAComputing #Bioinformatics #MolecularComputing

Breaking the Memory Wall: Near-Memory and In-Memory Computing for Next-Gen AI One of the most pressing challenges in modern computing is the so-called “memory wall,” where the cost of moving data between processors and memory far exceeds the cost of performing the actual computation. To overcome this bottleneck, researchers and industry are exploring two complementary approaches: Near-Memory Computing (NMC) and In-Memory Computing (IMC). Near-Memory Computing places processors or accelerators physically close to memory, often using advanced 2.5D/3D integration, to reduce latency and energy consumption while still relying on traditional digital logic. In contrast, In-Memory Computing goes a step further by embedding computation directly within the memory arrays, allowing data to be processed where it resides and enabling massive parallelism. IMC, particularly with emerging non-volatile memory technologies, promises orders of magnitude improvement in efficiency for AI and machine learning workloads, though it faces challenges in precision and integration. Together, these paradigms represent a fundamental shift in AI system design, blurring the line between computation and storage to deliver the performance and energy gains demanded by future applications. Any thoughts?

Neuromorphic Computing: The Brain-Inspired Technology Reshaping AI 🧠 Traditional computing architectures are hitting fundamental limits, but neuromorphic processors that mimic human brain structures are opening entirely new possibilities for AI applications. CIOs who understand this paradigm shift are positioning their organizations for the next wave of computational breakthroughs. ⚡ Neuromorphic chips process information like biological neurons, enabling AI systems that learn continuously, consume minimal power, and operate in real-time without requiring cloud connectivity. Intel's Loihi and IBM's TrueNorth processors are already demonstrating capabilities that surpass traditional AI hardware in specific applications. 🚀 The most forward-thinking CIOs are exploring neuromorphic applications in autonomous systems, real-time analytics, and edge AI deployments where power efficiency and adaptive learning create competitive advantages. These brain-inspired processors excel at pattern recognition, sensory processing, and decision-making tasks that mirror human cognitive abilities. 💫 Neuromorphic computing represents the convergence of neuroscience and technology, creating AI systems that think more like humans while operating more efficiently than traditional computers. The organizations that master this technology will lead the next generation of intelligent systems. 🌟 #NeuromorphicComputing #CIO #ArtificialIntelligence #BrainInspiredTech #Innovation #FutureTech #EdgeAI #TechLeadership #LurdezConsulting #NextGenComputing

🚀 What if I told you that the future of computing is not just faster, but fundamentally different? Quantum computing is no longer the stuff of science fiction. Recently, we've seen incredible breakthroughs that are pushing the boundaries of what's possible. 🔍 Here’s what’s exciting: 1. Complex Problem-Solving: Quantum computers can analyze massive datasets and solve problems in minutes—problems that traditional computers couldn't crack in years. 2. Drug Discovery: Imagine virtually simulating molecular interactions. Quantum tech is accelerating pharmaceutical research, promising faster cures. 3. Cybersecurity Innovations: With quantum advancements come new strategies to secure our data against threats in a digital-first world. 4. AI Collaborations: The fusion of AI with quantum computing could unleash a new era of intelligent systems capable of extraordinary tasks. 💡 Takeaway? Getting familiar with these advancements will not only future-proof your skills but also position you as a thought leader in your field. Are you ready to embrace the quantum shift? 👾 How do you think quantum computing will change your industry? #QuantumComputing #Innovation #FutureTech #AI

China Just Built AI That Runs 100x Faster on 2% of the Training Data... New neuromorphic model called Spiking Brain mimics biological neurons instead of brute-force computation. Result: 69% of calculations eliminated entirely. Linear scaling instead of exponential complexity. Trained on Chinese MetaX hardware, not Nvidia GPUs. ••• THE EFFICIENCY BREAKTHROUGH Traditional AI calculates everything continuously, even zeros. Spiking Brain only activates neurons when processing meaningful information...exactly like human brains. Your brain runs on 20 watts. Current AI models consume enough electricity to power 7 million homes annually. ••• THE TECHNICAL ADVANTAGE 450M parameter model processes 4 million tokens without memory collapse. Mixture of experts architecture activates only relevant specialists per task. Deployed successfully on mobile CPUs with minimal battery drain. ••• THE INFRASTRUCTURE IMPACT Current trajectory: AI data centers will double electricity demand within years. Companies resorting to methane generators and reactivating nuclear plants to power training. Neuromorphic computing offers 89% energy reduction while maintaining 95% computational accuracy! ••• THE STRATEGIC SHIFT We've hit the wall on Moore's Law. Can't make chips smaller indefinitely... Biology shows the path: massive parallelism, event-driven processing, sparse activation. Open source code accelerates adoption. Hardware from Intel, IBM, and Brainchip already optimized for spiking neural networks. This isn't incremental optimization. It's architectural transformation.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝘀𝗶𝗹𝗶𝗰𝗼𝗻, 𝗯𝘂𝘁 𝗗𝗡𝗔? Imagine a computer that replaces silicon chips with molecules found in every cell—processing information, making decisions, and even learning at the nanoscale!  DNA molecular computation is a truly groundbreaking technique, enabling scientists to encode data directly in DNA strands and use precise chemical reactions to perform logic operations. Unlike electronic circuits that rely on electrons, DNA computation harnesses billions of molecules working in parallel, making it incredibly powerful for specific complex tasks like bioinformatics, medical diagnostics, or cryptography.
What makes this approach so revolutionary? It uses the fundamental pairing rules of nucleotides (A, T, G, C) to build logic gates and networks capable of highly sophisticated chemical information processing—all in a test tube! DNA logic gates are programmable, adaptable, and can interact directly with biological systems. As research advances, we’re seeing DNA computers solve intricate problems using nothing but clever molecular design.
Recent reviews highlight DNA computing’s unique advantages: massive parallelism, ultra-dense data storage, and low energy consumption. It’s already being used to tackle NP-complete problems, optimize cryptography, and even store digital data in DNA molecules.
Here’s the real twist: now, scientists are pushing the boundaries by making these DNA circuits reusable and sustainable, moving us closer to “living” computers that operate beyond biological training.
Stay tuned for my next post as I unveil the game-changing innovation that’s making these molecular computers universally rechargeable! #DNAComputing #MolecularComputing #SyntheticBiology #Bioinformatics #LifeSciences #Biotechnology #Innovation #ArtificialIntelligence 𝘙𝘦𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴: 𝘚𝘰𝘯𝘨, 𝘛., & 𝘘𝘪𝘢𝘯, 𝘓. (2025). 𝘏𝘦𝘢𝘵-𝘳𝘦𝘤𝘩𝘢𝘳𝘨𝘦𝘢𝘣𝘭𝘦 𝘤𝘰𝘮𝘱𝘶𝘵𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘋𝘕𝘈 𝘭𝘰𝘨𝘪𝘤 𝘤𝘪𝘳𝘤𝘶𝘪𝘵𝘴 𝘢𝘯𝘥 𝘯𝘦𝘶𝘳𝘢𝘭 𝘯𝘦𝘵𝘸𝘰𝘳𝘬𝘴. 𝘕𝘢𝘵𝘶𝘳𝘦, 646, 315-321. 𝘩𝘵𝘵𝘱𝘴://𝘥𝘰𝘪.𝘰𝘳𝘨/10.1038/𝘴41586-025-09570-2 𝘡𝘩𝘢𝘯𝘨, 𝘔. (2023). 𝘊𝘰𝘯𝘤𝘦𝘱𝘵, 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵 𝘢𝘯𝘥 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘋𝘕𝘈 𝘤𝘰𝘮𝘱𝘶𝘵𝘢𝘵𝘪𝘰𝘯. 𝘚𝘤𝘪𝘦𝘯𝘤𝘦𝘋𝘪𝘳𝘦𝘤𝘵. 𝘛𝘢𝘬𝘪𝘨𝘶𝘤𝘩𝘪, 𝘚. (2024). 𝘏𝘢𝘳𝘯𝘦𝘴𝘴𝘪𝘯𝘨 𝘋𝘕𝘈 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘯𝘢𝘯𝘰𝘱𝘰𝘳𝘦 𝘥𝘦𝘤𝘰𝘥𝘪𝘯𝘨 𝘧𝘰𝘳 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴. 𝘊𝘩𝘦𝘮𝘪𝘤𝘢𝘭 𝘚𝘰𝘤𝘪𝘦𝘵𝘺 𝘙𝘦𝘷𝘪𝘦𝘸𝘴, 54(1), 1-20.𝘱𝘶𝘣𝘴.𝘳𝘴𝘤 𝘋𝘕𝘈 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨: 𝘋𝘕𝘈 𝘤𝘪𝘳𝘤𝘶𝘪𝘵𝘴 𝘢𝘯𝘥 𝘥𝘢𝘵𝘢 𝘴𝘵𝘰𝘳𝘢𝘨𝘦. (2025). 𝘕𝘢𝘯𝘰𝘴𝘤𝘢𝘭𝘦 𝘏𝘰𝘳𝘪𝘻𝘰𝘯𝘴, 10(8), 459-472. 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘋𝘕𝘈 𝘊𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨? 2025 𝘎𝘶𝘪𝘥𝘦. (2025). 𝘕𝘦𝘶𝘳𝘰𝘫𝘦𝘤𝘵.
𝘏𝘢𝘳𝘯𝘦𝘴𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘱𝘰𝘸𝘦𝘳 𝘰𝘧 𝘋𝘕𝘈 𝘧𝘰𝘳 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨. (2024). 𝘕𝘢𝘵𝘶𝘳𝘦 𝘉𝘪𝘰𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺, 42(11), 742-750.𝘯𝘢𝘵𝘶𝘳𝘦