Quantum Machine Learning
Few-Shot Transfer Learning Mitigates Cross-Device Quantum Noise Discrepancies on IBM Hardware
Transfer learning with a residual neural network trained on noisy circuit data from IBM's ibm_fez device enables noise-to-ideal outcome mapping. Zero-shot application to ibm_marrakesh yields KL divergence of 1.6706 versus 0.3014 in-domain, confirming device-specific noise. Fine-tuning with just 20 t…
Hybrid Quantum Neural Networks Boost Breast Cancer Thermography Classification via Quantum-Classical Fusion
HQNN integrates 4-qubit variational quantum circuits with strongly entangling layers and multi-head attention for quantum-aware feature encoding, paired with classical CNNs for pattern recognition in thermographic images. This hybrid model outperforms state-of-the-art classical architectures on brea…
Quantum-Enhanced Federated LSTM Outperforms QML Baselines on HEP Data with 100x Fewer Samples
Proposes a federated learning framework using hybrid quantum-classical QLSTM models for distributed training on large HEP datasets like SUSY. QLSTM integrates quantum variational circuits for feature relationships with classical LSTM for temporal correlations, enabling high performance on NISQ hardw…
Quantum Patches with Random Quantum Circuits Boost QML Adversarial Robustness
Random quantum circuits (RQCs) generate pseudo-noise akin to adversarial perturbations, training QML models to resist attacks. On CIFAR-10, successful attack rate drops from 89.8% to 68.45%; on CINIC-10, from 94.23% to 78.68%. This leverages quantum properties like superposition and entanglement for…
Expert Perspectives on the Trajectory and Challenges of Quantum Machine Learning
Experts in quantum machine learning (QML) discuss the field's current state, emphasizing the need for critical evaluation beyond "speedups." Key areas of focus include the potential for QML algorithms to process quantum data directly, the importance of rigorous theoretical foundations, and the neces…
Quantum Computers Excel at Spectral Methods Fundamental to Machine Learning
Spectral methods, which manipulate the Fourier spectrum of ML models for learning and regularization, align naturally with quantum computing capabilities like the Quantum Fourier Transform. Representing generative models as quantum states enables efficient spectrum manipulation unavailable classical…
Quantum Fourier Transform Enables Exact Probabilistic Modeling of Permutations
Quantum computers exploit super-exponential speedup in the Quantum Fourier Transform (QFT) over the symmetric group to encode exact non-Abelian harmonic analysis models for permutation-structured data. These models, intractable classically, capture permutation correlations via group Fourier spectra,…
Quantum Kernel Methods Leverage Hilbert Space Equivalence to Boost Classical ML Classifiers
Kernel methods in ML define similarity measures between data points via positive semi-definite functions, equivalent to inner products in a high-dimensional Hilbert feature space, enabling nonlinear classification through the kernel trick in algorithms like SVMs and Gaussian processes. Quantum theor…
Hybrid Quantum Transfer Learning Augments Pre-Trained Classical Networks with Quantum Circuits for Efficient NISQ Processing
The paper extends transfer learning to hybrid classical-quantum neural networks by pre-training classical networks on high-dimensional data like images and augmenting them with final variational quantum circuits. This leverages classical preprocessing to embed informative features into quantum proce…
Universal Continuous-Variable Quantum Neural Networks via Layered Gaussian and Non-Gaussian Gates
The paper presents variational quantum circuits in the continuous-variable (CV) architecture as universal neural networks, using layered Gaussian gates for affine transformations and non-Gaussian gates for nonlinear activations. These CV-QNNs encode highly nonlinear transformations unitarily, suppor…
Low-Depth Variational Quantum Classifiers with Poly-Log Parameters for Noisy Quantum Hardware
Proposes a variational quantum classifier encoding input features into quantum state amplitudes, processed by a shallow circuit of parameterized single- and two-qubit gates followed by single-qubit measurement. The architecture scales learnable parameters poly-logarithmically with input dimension, e…
Quantum Kernel Methods Mirror Feature Maps to Hilbert Spaces
Quantum computing parallels kernel methods by enabling efficient computation in high-dimensional Hilbert spaces via nonlinear feature maps that encode classical data into quantum states. Two quantum ML approaches emerge: (1) quantum estimation of intractable kernel inner products for classical algor…
Quantum Ensembles Enable Exponentially Large Untrained Classifier Aggregates via Parallel Evaluation
Quantum ensembles of quantum classifiers form by preparing a superposition state that encodes multiple classifiers, allowing parallel evaluation on a quantum computer followed by a single-qubit measurement for the collective decision. This approach supports exponentially large ensembles without indi…
Quantum Linear Regression Enables Direct Prediction via Single Qubit Measurement
The algorithm performs least-squares linear regression on quantum computers, prioritizing prediction of outputs for new inputs over parameter readout. It handles non-sparse data via low-rank approximations, reducing condition number dependence. Runtime is logarithmic in input dimension when data is …
Quantum Algorithm Leverages Hamming Distance for Enhanced Pattern Classification
This paper introduces a quantum machine learning approach to pattern classification by adapting Trugenberger's quantum Hamming distance measurement method. The algorithm aims to exploit quantum computing's known advantages over classical methods for specific tasks within machine learning. It demonst…
Quantum Perceptron via Phase Estimation Mimics Classical Step Activation with Linear Resources
The paper proposes a quantum perceptron model that simulates the step-activation function of classical perceptrons using the quantum phase estimation algorithm. It processes inputs of size n with O(n) resource requirements, enabling efficient scaling. This foundational unit supports development of t…
Systematic Overview of Quantum Machine Learning Approaches
Quantum machine learning explores enhancing classical ML algorithms using quantum computing, from efficient execution of costly subroutines to reformulating stochastic methods in quantum terms. The field addresses tasks like image/speech recognition and optimization relevant to IT. This paper provid…
No Existing Quantum Neural Network Fully Merges Neural Nonlinearity with Quantum Unitary Dynamics
Quantum Neural Networks (QNNs) aim to fuse neural computing's nonlinear, dissipative dynamics with quantum computing's linear, unitary evolution, but current proposals fall short. The paper systematically reviews QNN research, defines key requirements, and finds no model fully leverages both quantum…
Quantum Walks Model Associative Memory in Qubit-Based Neural Networks
Proposes quantum neural networks (QNNs) using qubits instead of binary neurons to leverage quantum computing. Models QNN dynamics via stochastic quantum walks on global firing state graphs, replicating classical associative memory. Biased discrete Hadamard walks from biological neuron updates fail u…