Software Between Quantum and Machine Learning -- And Down to Pulses

arXiv:2605.21286 · quant-ph · Submitted 2026-05-20 · Read on arXiv

quant-ph

Submitted: 2026-05-20

Updated: 2026-06-01

Code: https://github.com/cirKITers/qml-essentials

Terminology

• Entanglement — 32× in this paper · explained in 4 episodes
• Quantum Circuits — 9× in this paper · explained in 2 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Abstraction — 5× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Multipartite Entanglement — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Ancillary Qubits — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Algorithmic Primitives — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• International Conference on Quantum Computing and Engineering — 1× in this paper
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Variational Quantum Eigensolver (VQE) — 1× in this paper · explained in 5 episodes

Sources

• Learning Fourier series with parametrized quantum circuits
• Mind the gaps: The fraught road to quantum advantage
• Generative quantum advantage for classical and quantum problems
• Data re-uploading for a universal quantum classifier
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
• A Quantum Engineer's Guide to Superconducting Qubits
• Gate-free state preparation for fast variational quantum eigensolver simulations: ctrl-VQE
• On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
• Approximating under the Influence of Quantum Noise and Compute Power
• Make Some Noise! Measuring Noise Model Quality in Real-World Quantum Software
• Fourier Fingerprints of Ansatzes in Quantum Machine Learning
• Quantum computing with Qiskit
• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• TensorFlow Quantum: A Software Framework for Quantum Machine Learning
• Qibo: a framework for quantum simulation with hardware acceleration
• Qiboml: towards the orchestration of quantum-classical machine learning
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• QEF: Reproducible and Exploratory Quantum Software Experiments

Related papers

• Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
• Encrypted clones can leak: Classification of informative subsets in Quantum Encrypted Cloning
• Polynomial-time classical and quantum simulation of quantum impurity models
• Theory of quantum-enhanced interferometry with general Markovian light sources
• A convergent hierarchy of spectral gap certificates for qubit Hamiltonians
• Universal Bound and Phase Transition in Many-Body Fermionic Non-Gaussianity