Applying Quantum Mechanics to Programming Techniques

Exciting breakthrough at the intersection of AI and quantum physics! 🧠💻⚛️ I've been diving deep into how we can interpret transformer models through the lens of quantum many-body problems, and the results are mind-blowing. Recent work by Shai et al. (https://lnkd.in/dNDwDenT) shows that transformers encode belief state geometry in their residual stream. Building on this, I've found fascinating parallels with computational graphs approaches as Feynman diagrams in QFT (https://lnkd.in/diZ379br). Building the Bridge: Hidden States as Coupling Constants Recent work by Shai et al. [1] suggests that transformers encode belief state geometry within their residual stream. Here's where the quantum connection gets exciting: these hidden states might hold a key parallel to coupling constants in QFT. Coupling the Analogy: Coupling Constants: In quantum mechanics, coupling constants define the strength of interaction between particles. A higher value signifies a stronger influence. Hidden States as "Effective Coupling Constants": In transformers, the values within the hidden state could be seen as a measure of the "strength" of the connections between the current input and the model's belief about the entire future sequence. Stronger hidden state values could indicate a stronger "belief" or connection between the present input and the model's prediction about the future. Weaker hidden state values might suggest a weaker connection or less influence on the prediction from the current input in the context of the broader future sequence. Key insights: 1. Transformer belief states and Feynman diagrams both exhibit fractal structures 2. Hidden states in transformers correlate with coupling constants in QFT 3. Transformer depth ~ energy scale in renormalization group flow 4. Attention mechanisms ~ interaction propagators in many-body systems This framework opens up new possibilities for optimizing transformer architectures and deepening our understanding of how they capture complex language patterns. To my physicist friends: Imagine treating language as a many-body problem, with words as interacting particles! To my ML colleagues: We might be able to leverage QFT techniques to build more efficient language models! I'm still refining these ideas and would love your input. What implications do you see for your field? Any challenges or opportunities I'm missing? Let's push the boundaries of interdisciplinary science together! 🚀 #AI #QuantumPhysics #NLP #MachineLearning #FeynmanDiagrams

🔗✨ Exploring the Future of Quantum Computing with Physics-Informed Neural Networks (PINNs) ✨🔗 Excited to highlight the pioneering work by Stefano Markidis that dives deep into the potential of Quantum Physics-Informed Neural Networks (Quantum PINNs) for solving differential equations on hybrid CPU-QPU systems! 📘 What’s this about? Physics-Informed Neural Networks (PINNs) have proven their versatility in addressing scientific computing challenges. This study extends PINNs into the quantum realm using Continuous Variable (CV) Quantum Computing, offering a new approach to solving Partial Differential Equations (PDEs) with quantum hardware. Key Highlights: ✅ Quantum Meets Physics: The framework combines CV quantum neural networks with classical methods to tackle PDEs like the 1D Poisson equation. ✅ Optimizer Insights: Traditional optimizers like SGD outperformed adaptive methods in this quantum landscape, highlighting the unique challenges of quantum optimization. ✅ Scalability: Explores batch processing and neural network depth for more effective performance on quantum systems. ✅ Programming Ease: Tools like Strawberry Fields and TensorFlow simplify the integration of quantum and classical computations. 💡 Why it matters: This research doesn't just apply PINNs to quantum computing—it highlights the differences between classical and quantum approaches, paving the way for advancements in quantum PINN solvers and their real-world applications in computational physics, electromagnetics, and more. 📖 Dive deeper: Access the full study here: https://lnkd.in/dZm3F3CR Source code available: https://lnkd.in/dAsXxnbN What are your thoughts on combining quantum computing with AI for scientific breakthroughs? Let’s discuss! 🚀 #QuantumComputing #PhysicsInformedNeuralNetworks #ScientificComputing #HybridAI #PDEsolvers #Innovation

I've been tackling the "barren plateaus" problem in QML, where training stalls inside vast search spaces. My latest experiment in fraud detection revealed a fascinating, counterintuitive solution. I discovered that increasing my quantum circuit's entanglement didn't smooth the path to a solution, but it created a more complex and rugged loss landscape (using a dressed quantum circuit scheme). Taking advantage of the hyvis library, I visualized this effect (thanks to the colleagues of JoS QUANTUM for putting this together), as shown in the first image of the post. The landscape evolves from a simple valley to a rich, expressive terrain (but potentially more complex for an optimizer). But did this complexity hurt performance? Usually that should be the case, but the exact opposite happened. The image shows the model with the most complex landscape (8 CNOTs by layer) not only learned faster (lower loss) but also achieved the highest accuracy (AUC) on the validation set and later in the test set. There is no free lunch on this. We can't generalize from these examples. This added complexity, or "expressivity," is precisely what allowed the model to find a superior solution in this case and avoid getting stuck, but it is not the norm. My biggest conclusion here It seems that for QML, the key to real-world performance isn't avoiding complexity, but leveraging it. To be able to extract permanent benefits, we should follow approaches like what Dra. Eva Andres Nuñez is researching by finding the way to use the extra complexity of entanglement to be able to find the global minima and not get stuck in our quantum optimization procedures using the theory behind SNNs. Here details about the hyvis library in GitHub: https://lnkd.in/dzqcFvDE An insightful paper from Eva about mixing SNNs and quantum: https://lnkd.in/dXDiuCBH Same subject from Jiechen Chen: https://lnkd.in/d-Uyngef #quantumcomputing #machinelearning #ai #datascience #frauddetection #ml #qml

#QuantumTuesday What if the key to unlocking quantum computing's full potential lies not in brute force but in elegant simplicity? As the GoTo Fractional Quantum Chief Intellectual Property Officer, I constantly explore the intersection of innovation, strategy, and disruptive technologies. Today, I’m thrilled to share insights from an extraordinary paper: "Tensor Quantum Programming" by A. Termanova et al. This work brilliantly merges tensor networks (TNs) and quantum computing, opening doors to solving some of the most complex computational problems of our time. Imagine tackling partial differential equations, quantum chemistry simulations, or machine learning models not with overwhelming computational resources but by leveraging tensor efficiency and the unique strengths of quantum circuits. This hybrid approach - classical for simplicity, quantum for complexity - redefines the rules of computation. Key takeaways from this breakthrough: 🔑 Efficiency Redefined: TNs are mapped to quantum circuits, creating a paradigm where high-dimensional problems scale linearly in complexity. Yes, you read that right - linear scalability in quantum circuits for problems that traditionally overwhelmed classical systems. 🔑 Applications Everywhere: - Simulating Hamiltonians for quantum systems. - Optimizing black-box functions with precision. - Revolutionizing quantum chemistry, from molecular dynamics to electron correlations. - Enhancing machine learning models by encoding TN architectures directly onto quantum platforms. 🔑 The Future Is Here: By bridging the gap between classical and quantum resources, Tensor Quantum Programming paves the way for solving real-world problems, from innovation-driven industries to fundamental research. This paper highlights an important truth: quantum computing isn't about doing more of the same; it’s about doing what was previously impossible. For those of us in the business of strategy and intellectual property, such breakthroughs represent not just scientific progress but entirely new frontiers for value creation. As an IP Alchemist, this inspires me to think about how we can protect and leverage these innovations to shape industries and fuel growth. How do we ensure that the architectures we build today are not just protected but optimized for tomorrow’s quantum future? What are your thoughts on the role of hybrid approaches like this in quantum computing? Let’s connect and dive into the possibilities. 🚀 #QuantumComputing #TensorNetworks #InnovationStrategy #IPManagement #DeepTechDisruption Terra Quantum AG Markus Pflitsch Artem Melnikov Aleksandr Berezutskii Roman Ellerbrock Michael Perelshtein

Quantum whispers in the GPU roar For Wall Street, more AI means more GPUs, more datacenters, more cloud contracts. And OpenAI–NVIDIA $100B deal locks it in. But quieter signals from research point to a second axis of scaling: not just more metal, but smarter math. It’s about quantum. Let me give you some notable examples from the last week research: 1. Compression: QKANs and quantum activation functions Paper: Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks Offers replacing fixed nonlinearities with single-qubit variational circuits (DARUANs). These tiny activations generate exponentially richer frequency spectra → so we get same power with exponentially fewer parameters. Quantum KANs (QKANs), built on this idea, already outperformed MLPs and KANs with 30% fewer parameters. 2. Exactness: Coset sampling for lattice algorithms Paper: Exact Coset Sampling for Quantum Lattice Algorithms Proposes a subroutine that cancels unknown offsets and produces exact, uniform cosets, making subsequent Fourier sampling provably correct. Injecting mathematically guaranteed steps into probabilistic workflows means precision: fewer wasted tokens, fewer dead-end paths, less variance in cost per query. 3. Hybridization: quantum-classical models in practice Paper: Hybrid Quantum-Classical Model for Image Classification These models dropped small quantum layers into classical CNNs, showing that they can train faster and use fewer parameters than classical versions. ▪️ What does this mean for inference scaling? Scaling won’t only mean bigger clusters for bigger models. It might also be about: - extracting more from each parameter - cutting errors at the source - and blending quantum and classical strengths. Notably, this direction is not lost on the companies like NVIDIA. There are several signs: • NVIDIA's CUDA-Q – an open software platform for hybrid quantum-classical programming. • NVIDIA also launched DGX Quantum, a reference architecture linking quantum control systems directly into AI supercomputers.  • They are opening a dedicated quantum research center with hardware partners. • Jensen Huang is aggressively investing into quantum startups like PsiQuantum (just raised $1B, saying it’s computer will be ready in two years), Quantinuum, and QuEra through NVentures - a major strategic shift in 2025, validating quantum's commercial timeline. ▪️ So what we will see:  GPUs will remain central. But quantum ideas will be slipping into the story of inference scaling. They are still early, but it's the new axis worth paying attention to. What do you think about it?

Think Quantum, My Friends QHDC Quantum Hyperdimensional Computing (QHDC) is a novel computational paradigm that integrates classical Hyperdimensional Computing (HDC)—also known as Vector-Symbolic Architectures (VSA)—with quantum computing principles. It was introduced in a 2025/2026 paper by Fabio Cumbo and colleagues (primarily from Cleveland Clinic’s Computational Life Sciences group), with the full paper published in npj Unconventional Computing (May 2026). Background and Motivation Classical HDC is a brain-inspired approach that represents information as high-dimensional vectors (hypervectors, often thousands of dimensions). It uses simple operations like: Binding (e.g., element-wise multiplication or convolution) to associate items. Bundling (e.g., addition/normalization) to combine sets or classes. Permutation for ordering/sequences. Similarity (e.g., cosine) for querying/inference. These enable efficient, robust, noise-tolerant computation for tasks like classification, reasoning, and few-shot learning, with holographic (distributed) representations. QHDC maps these directly onto quantum hardware’s native operations (superposition, entanglement, phase encoding, etc.), aiming for a more “quantum-native” framework than many quantum machine learning (QML) approaches that adapt classical models. This addresses mismatches between classical architectures and quantum processors, potentially enabling efficient neuromorphic quantum algorithms suited for NISQ (Noisy Intermediate-Scale Quantum) devices. Key mappings include: Hypervectors → Quantum states (e.g., via phase encoding on uniform superposition). Bundling → Quantum averaging (using techniques like Linear Combination of Unitaries/LCU and Oblivious Amplitude Amplification). Binding → Quantum phase oracles. Permutation → Quantum Fourier Transform (QFT). Similarity → Quantum state fidelity (e.g., Hadamard Test). Key Research and Validation The foundational work (arXiv 2511.12664, published version 2026) includes: Two experiments: A symbolic analogical reasoning task and a supervised classification task (e.g., image/data classification). Implementations tested on: Classical Python (hdlib), ideal quantum simulators, and real hardware (IBM Heron r3 156-qubit processor). Performance highlights: Demonstrated physical realizability on NISQ hardware. In hybrid setups, it showed significant speedups (reported ~500× faster than compared quantum classifiers like VQC or QSVC in cross-validation timing), with competitive accuracy (e.g., F1 scores around 80% in one hybrid case). Resource efficiency analysis favoring near-term quantum advantage over some iterative QML methods. It positions QHDC as promising for biomedical applications (e.g., complex data in bioinformatics, where outcomes are hard to enumerate classically) due to its robustness and potential for cognitive/reasoning tasks. X5 Festival QE Channel #quantum #x5 #computing #AGI

Quantum computing promises to making LLMs more efficient. And it's already working on real hardware. Efficient fine-tuning of large language models remains a critical bottleneck in AI development, with most researchers focused on purely classical computing approaches. A new paper from Chinese researchers demonstrates how quantum computing principles can dramatically reduce the parameters needed while improving model performance. The team introduces Quantum Weighted Tensor Hybrid Network (QWTHN), which combines quantum neural networks with tensor decomposition techniques to overcome the expressive limitations of traditional Low-Rank Adaptation (LoRA). By leveraging quantum state superposition and entanglement, their approach achieves remarkable efficiency: reducing trainable parameters by 76% while simultaneously improving performance by up to 15% on benchmark datasets. Most importantly, this isn't just theoretical - they've successfully implemented inference on actual quantum computing hardware. This represents a tangible advancement in making quantum computing practical for AI applications, demonstrating that even current-generation quantum devices can enhance the capabilities of billion-parameter language models. The integration of quantum techniques into traditional deep learning frameworks might become standard practice for resource-efficient AI development in the future. More on Quantum Hybrid Networks and other AI highlights in this week's LLM Watch:

🚀 New Paper on arXiv! I’m excited to share our latest work: “Learning to Program Quantum Measurements for Machine Learning” 📌 arXiv: https://lnkd.in/euRhBQJM 👥 With Huan-Hsin Tseng (Brookhaven National Lab), Hsin-Yi Lin (Seton Hall University), and Shinjae Yoo (BNL) In this paper, we challenge a long-standing limitation in quantum machine learning: static measurements. Most QML models rely on fixed observables (e.g., Pauli-Z), limiting the expressivity of the output space. We take this one step further--by making the quantum observable (Hermitian matrix) a learnable, input-conditioned component, programmed dynamically by a neural network. 🧠 Our approach integrates: 1. A Fast Weight Programmer (FWP) that generates both VQC rotation parameters and quantum observables 2. A differentiable, end-to-end architecture for measurement programming 3. A geometric formulation based on Hermitian fiber bundles to describe quantum measurements over data manifolds 🧪 Experiments on noisy datasets (make_moons, make_circles, and high-dimensional classification) show that our dual-generator model outperforms all traditional baselines—achieving faster convergence, higher accuracy, and stronger generalization even under severe noise. We believe this work opens the door to adaptive quantum measurements and paves the way toward more expressive and robust QML models. If you're working on QML, differentiable quantum programming, or quantum meta-learning, I’d love to connect! #QuantumMachineLearning #QuantumComputing #QML #FastWeightProgrammer #DifferentiableQuantumProgramming #arXiv #HybridAI #AI #Quantum