Quantum Active Learning

Abstract

Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled training set for supervised learning. Human annotators, often experts, provide labels for samples through additional experiments, adding to the training cost. To mitigate this expense, there is a quest for methods that maintain model performance over fully labeled datasets while requiring fewer labeled samples in practice, thereby extending few-shot learning to the quantum realm. Quantum active learning estimates the uncertainty of quantum data to select the most informative samples from a pool for labeling. Consequently, a QML model is supposed to accumulate maximal knowledge as the training set comprises labeled samples selected via sampling strategies. Notably, the QML models trained within the QAL framework are not restricted to specific types, enabling performance enhancement from the model architecture's perspective towards few-shot learning. Recognizing symmetry as a fundamental concept in physics ubiquitous across various domains, we leverage the symmetry inherent in quantum states induced by the embedding of classical data for model design. We employ an equivariant QNN capable of generalizing from fewer data with geometric priors. We benchmark the performance of QAL on two classification problems, observing both positive and negative results. QAL effectively trains the model, achieving performance comparable to that on fully labeled datasets by labeling less than 7\% of the samples in the pool with unbiased sampling behavior. Furthermore, we elucidate the negative result of QAL being overtaken by random sampling baseline through miscellaneous numerical experiments. (character count limit, see the main text)

12 pages, 10 figures

References in corpus (35)

• Quantum Machine Learning
• Variational Quantum Algorithms
• Barren plateaus in quantum neural network training landscapes
• Quantum support vector machine for big data classification
• Quantum Convolutional Neural Networks
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Data re-uploading for a universal quantum classifier
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• Active learning machine learns to create new quantum experiments
• Provably efficient machine learning for quantum many-body problems
• Efficient Symmetry-Preserving State Preparation Circuits for the Variational Quantum Eigensolver Algorithm
• Exploiting symmetry in variational quantum machine learning
• Group-Invariant Quantum Machine Learning
• Variational Quantum Eigensolver with Fewer Qubits
• Theory for Equivariant Quantum Neural Networks
• Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
• Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
• Optimal experiment design revisited: fair, precise and minimal tomography
• Constraining the Parameters of High-Dimensional Models with Active Learning
• Quantum Capsule Networks
• Efficient Estimation of Material Property Curves and Surfaces via Active Learning
• Retrieving Quantum Information with Active Learning
• Representation of binary classification trees with binary features by quantum circuits
• Adaptive Quantum State Tomography with Active Learning
• Active Learning Algorithm for Computational Physics
• Symmetry breaking in geometric quantum machine learning in the presence of noise
• Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning
• Active Learning of Quantum System Hamiltonians yields Query Advantage
• A quantum active learning algorithm for sampling against adversarial attacks
• Quantum process tomography via optimal design of experiments
• Picking the low-hanging fruit: testing new physics at scale with active learning
• Upper bounds for relative entropy of entanglement based on active learning
• Active Learning in Physics: From 101, to Progress, and Perspective
• Active Learning for Quantum Mechanical Measurements