Hybrid Algorithm Strategies for Quantum Network Engineers

⚛️ Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment 🧾 Achieving a practical quantum advantage for near-term applications is widely expected to rely on hybrid classical-quantum algorithms. To deliver this practical advantage to users, high performance computing (HPC) centers need to provide a suitable software and hardware stack that supports algorithms of this type. In this paper, we describe the world’s first implementation of a classical-quantum environment in an HPC center that allows multiple users to execute hybrid algorithms on multiple quantum processing units (QPUs) and GPUs. Our setup at the Poznan Supercomputing and Networking Center (PCSS) aligns with current HPC norms: the computing hardware including QPUs is installed in an active data center room with standard facilities; there are no special considerations for networking, power, and cooling; we use Slurm for workload management as well as the NVIDIA CUDA-Q extension API for classical-quantum interactions. We demonstrate applications of this environment for hybrid classical-quantum machine learning and optimisation. The aim of this work is to provide the community with an experimental example for further research and development on how quantum computing can practically enhance and extend HPC capabilities. ℹ️ Slysz et al - 2025

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

> Sharing resource < Very nice paper showing the importance of fine-tuning encoding and ansatzes: "On the Importance of Fundamental Properties in Quantum-Classical Machine Learning Models" by Silvie Illésová, Tomasz Rybotycki, Piotr Gawron, Martin Beseda Abstract: We present a systematic study of how quantum circuit design, specifically the depth of the variational ansatz and the choice of quantum feature mapping, affects the performance of hybrid quantum-classical neural networks on a causal classification task. The architecture combines a convolutional neural network for classical feature extraction with a parameterized quantum circuit acting as the quantum layer. We evaluate multiple ansatz depths and nine different feature maps. Results show that increasing the number of ansatz repetitions improves generalization and training stability, though benefits tend to plateau beyond a certain depth. The choice of feature mapping is even more critical: only encodings with multi-axis Pauli rotations enable successful learning, while simpler maps lead to underfitting or loss of class separability. Principal Component Analysis and silhouette scores reveal how data distributions evolve across network stages. These findings offer practical guidance for designing quantum circuits in hybrid models. All source codes and evaluation tools are publicly available. Link: https://lnkd.in/e3eVyE8w #quantummachinelearning #quantumcomputing #ansatz #vqa #research #paper