Applying Variational Quantum Algorithms in Data Modeling

Interesting new study: "EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data." The authors introduce a novel framework to address the limitations of traditional amplitude embedding (AE) [GitHub repo included]. Traditional AE methods often involve deep, variable-length circuits, which can lead to high output error due to extensive gate usage and inconsistent error rates across different data samples. This variability in circuit depth and gate composition results in unequal noise exposure, obscuring the true performance of quantum algorithms. To overcome these challenges, the researchers developed EnQode, a fast AE technique based on symbolic representation. Instead of aiming for exact amplitude representation for each sample, EnQode employs a cluster-based approach to achieve approximate AE with high fidelity. Here are some of the key aspects of EnQode: * Clustering: EnQode begins by using the k-means clustering algorithm to group similar data samples. For each cluster, a mean state is calculated to represent the central characteristics of the data distribution within that cluster. * Hardware-optimized ansatz: For each cluster's mean state, a low-depth, machine-optimized ansatz is trained, tailored to the specific quantum hardware being used (e.g., IBM quantum devices). * Transfer Learning for fast embedding: Once the cluster models are trained offline, transfer learning is used for rapid amplitude embedding of new data samples. An incoming sample is assigned to the nearest cluster, and its embedding circuit is initialized with the optimized parameters of that cluster's mean state. These parameters can then be fine-tuned, significantly accelerating the embedding process without retraining from scratch. * Reduced circuit complexity: EnQode achieved an average reduction of over 28× in circuit depth, over 11× in single-qubit gate count, and over 12× in two-qubit gate count, with zero variability across samples due to its fixed ansatz design. * Higher state fidelity in noisy environments: In noisy IBM quantum hardware simulations, EnQode showed a state fidelity improvement of over 14× compared to the baseline, highlighting its robustness to hardware noise. While the baseline achieved 100% fidelity in ideal simulations (as it performs exact embedding), EnQode maintained an average of 89% fidelity when transpiled to real hardware in ideal simulations, which is considered a good approximation given the significant reduction in circuit complexity. Here the article: https://lnkd.in/dQMbNN7b And here the GitHub repo: https://lnkd.in/dbm7q3eJ #qml #datascience #machinelearning #quantum #nisq #quantumcomputing

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?

I have been exploring how classical deep learning models and quantum circuits can be combined to solve machine learning problems more effectively. Rather than replacing classical approaches, this work focuses on leveraging the strengths of both paradigms. Below is a high-level overview of a hybrid quantum–classical classifier trained on the MNIST dataset (binary classification: digits 0 vs 1). 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 (𝗣𝘆𝗧𝗼𝗿𝗰𝗵) A standard convolutional neural network processes the 28×28 MNIST images and extracts high-level features. This step reduces the dimensionality of the input while preserving essential information. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗛𝗮𝗻𝗱𝗼𝗳𝗳 The CNN outputs two features, chosen to match the number of qubits used in the quantum circuit. 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 (𝗤𝗶𝘀𝗸𝗶𝘁) The features are encoded into a quantum state using a ZZFeatureMap. A parameterized variational circuit (RealAmplitudes) transforms this state, and the expectation value of a measurement operator is used for classification. 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Using Qiskit’s TorchConnector, the quantum and classical components are trained jointly. Gradients from the quantum circuit are computed using the parameter-shift rule and integrated with PyTorch’s automatic differentiation. 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗦𝗲𝘁𝘂𝗽 • PyTorch and Qiskit Machine Learning • Training performed on the Aer simulator, with compatibility for quantum hardware via primitives On small subsets of the dataset, this hybrid approach achieves perfect classification accuracy, demonstrating how classical feature extraction combined with quantum decision layers can be effective even with limited qubit resources. Hybrid quantum–classical models provide a practical direction for near-term quantum machine learning, especially when classical preprocessing reduces problem complexity before quantum evaluation. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 https://lnkd.in/gkwjwp-r https://lnkd.in/g7ge8MHD https://lnkd.in/gApuMGHE ♻️ Repost if you found this valuable! ➕ Follow me https://lnkd.in/gGhxx66A for more insights on the cutting edge of AI and Quantum. #QuantumComputing #QuantumMachineLearning #MachineLearning #PyTorch #Qiskit #ArtificialIntelligence #HybridModels