Quantum ML promises faster data analysis but faces trainability challenges.
problem Challenges in training quantum machine learning models.
method Review of current methods and applications of quantum neural networks and quantum deep learning.
result Opportunities for quantum advantage in quantum machine learning.
Quantum Gaussian processes enable scalable quantum learning.
problem Lack of simple, interpretable, scalable learning frameworks for quantum data.
method Bayesian framework using Gaussian processes with quantum kernels.
result Provable and scalable quantum Gaussian processes for quantum learning.
Quantum machine learning models can approximate any continuous function.
problem Theoretical understanding of quantum feature maps in machine learning.
method Proving universal approximation property of quantum machine learning models in quantum-enhanced feature spaces.
result Quantum machine learning models are universal approximators of continuous functions.
Quantum Earth Mover's distance improves stability and efficiency in quantum learning.
problem Quantum learning's loss landscapes often lead to poor local minima and gradients.
method Introduced the quantum Earth Mover's (EM) distance and proposed a quantum Wasserstein generative adversarial network (qWGAN).
result The quantum EM distance makes quantum learning more stable and efficient.
Q-CurL optimizes quantum learning with a curriculum design.
problem Efficiently training quantum models with limited resources.
method Quantum curriculum learning framework.
result Q-CurL enhances training convergence and generalization.
Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.
problem Quantum machine learning's loss minimization through cross entropy is affected by measurement outcomes.
method Defined quantum cross entropy, proved its lower bounds, and investigated its relation to quantum fidelity and likelihood.
result Quantum cross entropy is lower-bounded by negative log-likelihood when derived from quantum data, but measurement outcomes can cause loss.
QGAA learns latent quantum states, reducing errors in quantum data generation.
problem Learning latent representations for quantum data generation.
method Quantum Generative Adversarial Autoencoder (QGAA) combining QAE and QGAN.
result Average errors in energies for H2 and LiH are 0.02 Ha and 0.06 Ha respectively, demonstrating QGAA's potential.
Quantum machine learning uses superposition to create a large ensemble of classifiers.
problem Improving machine learning efficiency on quantum computers.
method Using superposition to create an exponentially large ensemble of classifiers, trained with an optimization-free learning algorithm.
result Adding an optimization step improves the performance of quantum ensembles of classifiers.
Classical clients can verify quantum learning tasks efficiently.
problem Making quantum learning accessible to classical clients.
method Developed a framework for classical verification of quantum learning.
result Quantum learning tasks can be efficiently verified by classical verifiers.
Quantum machine learning boosts drug discovery efficiency.
problem Enhancing drug discovery through quantum computing.
method Quantum neural networks on gate-based quantum computers.
result Significant advancements in molecular property prediction and generation.
This paper reviews quantum machine learning from NISQ to fault tolerance.
problem The challenges and opportunities in quantum machine learning.
method Comprehensive review of quantum machine learning concepts.
result Coverage of NISQ and fault-tolerant quantum computing approaches.
Quantum variational circuits improve reinforcement learning efficiency.
problem Improving reinforcement learning algorithms using quantum computing.
method Investigation of quantum variational circuits for DQN and Double DQN, encoding classical data for quantum circuits.
result Quantum variational circuits can solve reinforcement learning tasks with a smaller parameter space.
Quantum neural tangent kernels help understand variational quantum circuits in machine learning.
problem Designing and predicting performance of variational quantum circuits.
method Using quantum neural tangent kernels and dynamical equations for loss functions.
result Analytical solutions for training dynamics in variational quantum circuits.
Quantum machine learning tackles large datasets with randomized measurements.
problem Efficiently process large, high-dimensional datasets on quantum computers.
method Randomized measurements to scale linearly with dataset size and quadratic for post-processing.
result Substantial speed-up for noisy quantum computers, enabling image classification.
Quantum learning complexity reviewed using information theory.
problem Learning properties of quantum systems or processing data via quantum computing.
method Information-theoretic techniques focusing on data, copy, and model complexity.
result Copy complexity due to irreversible quantum measurements limits information extraction.
Survey of QML applications on near-term quantum devices.
problem Achieving quantum advantage on real-world applications.
method Analysis of supervised and unsupervised techniques, including encoding, ansatz structure, error mitigation, and gradient methods.
result Current QML implementations on quantum hardware face limitations but show potential for real-world applications.
Quantum machine learning boosts financial forecasting accuracy.
problem Churn prediction and credit risk assessment in finance.
method Used quantum and classical Determinantal Point Processes for churn prediction, and quantum neural networks for credit risk assessment.
result Significant improvement in precision for churn prediction (6% increase). Quantum models match classical performance with fewer parameters.
Quantum algorithms speed up reinforcement learning policies in large state-action spaces.
problem Limitations of quantum access in training reinforcement learning policies.
method Designing quantum algorithms to train reinforcement learning policies.
result Quantum algorithms offer full quadratic speed-ups in sample complexity for well-behaved policies.
Quantum systems learn like machine learning models, influenced by dissipation.
problem Understanding how quantum systems learn and evolve.
method Hydrodynamical formulation of quantum mechanics, gradient descent model, empirical demonstration.
result Quantum systems follow a disrupted gradient descent model, influenced by dissipation.
We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety…
This work introduces a new quantum kernel, quantum tangent kernel, for improved performance.
problem Improving quantum machine learning performance beyond conventional methods.
method Developed a deep parameterized quantum circuit and used first-order expansion for training.
result The quantum tangent kernel outperforms conventional quantum kernel methods for ansatz-generated datasets.
Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves …
A new hybrid framework reduces quantum runtime and noise effects.
problem Challenges in deploying deep QFMs on real quantum hardware.
method Iterative Quantum Feature Maps (IQFMs) combining shallow QFMs and classical augmentation weights.
result Numerical experiments show IQFMs outperforming quantum convolutional neural networks.
Quantum algorithms improve perceptron learning efficiency.
problem Improving quantum algorithms for perceptron learning.
method Revisiting and correcting a flawed quantum version space perceptron algorithm, proposing quantum-enhanced cutting-plane algorithms.
result Improved complexity bounds for quantum perceptron learning.
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. How…
Photonic quantum reinforcement learning for control problems.
problem Solving continuous control problems with noisy quantum computers.
method Proximal policy optimization for photonic variational quantum agents.
result Photonic policy learning achieves comparable performance to classical neural networks.
VQC-MLPNet combines quantum and classical elements for scalable quantum machine learning.
problem Challenges in expressivity, trainability, and noise resilience of VQCs.
method Hybrid architecture with a VQC generating weights for a classical MLP during training.
result Improved expressivity, trainability, and robustness compared to standalone quantum or hybrid approaches.
Quantum models are rephrased as kernel methods, improving performance.
problem Improving quantum machine learning models by encoding data into quantum states.
method Rephrasing quantum models as kernel methods and using support vector machines.
result Kernel-based training finds better quantum models than variational circuit training.
Quantum datasets improve QML performance.
problem Benchmarking QML on classical datasets is uncertain.
method Introduced NTangled dataset of quantum states with varying entanglement.
result QML models trained on NTangled dataset outperform classical models.
Unified framework combines trace-induced quantum kernels for improved machine learning models.
problem Improving performance of quantum machine learning models using trace-induced kernels.
method Developed a unified framework combining various trace-induced quantum kernels, including global fidelity and local projected kernels, as Lego kernels.
result Local projected kernels can achieve comparable performance to global fidelity kernels with fewer quantum resources.
Quantum machine learning improves hedging in finance.
problem Improving hedging strategies in financial markets.
method Developed quantum reinforcement learning methods using policy-search and distributional actor-critic algorithms.
result Quantum models reduce parameter count and achieve comparable performance to classical methods.
Adaptive Quantum Conformal Prediction improves reliability of quantum machine learning predictions.
problem Quantum machine learning lacks robust uncertainty quantification methods.
method Adaptive Conformal Inference applied to quantum conformal prediction to maintain validity over time.
result AQCP achieves target coverage levels and is more stable than standard quantum conformal prediction.
A quantum reinforcement learning algorithm reduces sample complexity.
problem Quantum reinforcement learning under model-free settings with quantum oracle access.
method Quantum Natural Policy Gradient (QNPG) algorithm replacing random sampling with deterministic gradient estimation.
result QNPG achieves a sample complexity of ildeO(ε−1.5) for queries to the quantum oracle, significantly improving classical lower bound. Quantum correlations enhance generative models, providing a new resource for machine learning.
problem Capturing complex probability distributions in unsupervised learning.
method Theoretical and numerical analysis of quantum correlations in generative models.
result Quantum nonlocality and contextuality provide an expressivity advantage over classical models.
Quantum states can be learned efficiently using gentle measurements.
problem Efficiently learning quantum states with minimal measurements.
method Introducing α-LGM measurements and proving strong quantum DPI.
result The number of states needed for accurate learning is of order 1/(ε^2 α^2).
Enhances quantum circuit synthesis using deep learning and geometric methods.
problem Optimizing quantum circuits for time efficiency.
method Combining deep learning with geometric control techniques.
result Improved time-optimal control in quantum circuit synthesis.
Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural net…
Quantum machine learning aims to solve learning problems more efficiently.
problem Solving learning problems more efficiently using quantum processors.
method Leveraging quantum processors for optimization, supervised, unsupervised, reinforcement learning, and generative modeling.
result Quantum approaches may offer real benefits under certain conditions.
QBC uses quantum computers to speed up Bayesian computation.
problem Exponential speed-up in Bayesian computation.
method Quantum von Neumann measurement for simulating ML algorithms.
result Quantum versions of regression, Gaussian processes, and SGD.
Researchers develop a framework for quantum machine learning models.
problem Comparing quantum machine learning models and their resource requirements.
method Constructive framework of linear quantum models using quantum information theory.
result Linear quantum models require exponentially more qubits than data re-uploading models for certain learning tasks.
New method uses quantum computing to process classical data efficiently.
problem Inefficient quantum machine learning due to data loading and trainability issues.
method Linear Hamiltonian-based machine learning with ground state problems for k-local Hamiltonians.
result Demonstrated the effectiveness and scalability of the method on up to 50 qubits.
New trends explore quantum machine learning to speed up computations and analyze data.
problem Speeding up machine learning computations and analyzing large quantum data.
method Interplay between quantum physics and machine learning, including new algorithms and hardware.
result Breakthroughs in quantum machine learning can provide advantages over classical methods.
This study improves quantum classifiers by optimizing data preprocessing.
problem Quantum Machine Learning advantages are not yet clearly demonstrated.
method Used Linear Discriminant Analysis (LDA) for data preprocessing.
result Variational Quantum Algorithm (VQA) outperforms classical classifiers.
MPE framework proves universal approximation for quantum data distribution.
problem Challenges in generating quantum data from underlying distributions.
method Many-body Projected Ensemble (MPE) framework for quantum state design.
result MPE can approximate any quantum distribution within 1-Wasserstein distance error.
The paper uses quaternions to model quantum learning on devices.
problem Designing adaption and optimization techniques for quantum learning machines.
method Division algebra of quaternions to model computation and measurement on qubits, developing a training framework.
result Established quantum information processing units similar to neurons in classical approaches.
Quantum models learn unitary actions on entangled states from product states.
problem Generalization to out-of-distribution data in quantum machine learning.
method Proved out-of-distribution generalization for learning unitary actions.
result Learned unitary actions on entangled states from product states.
Quantum machine learning model for binary classification.
problem Efficiency in high-dimensional binary classification tasks.
method Quantum-classical hybrid algorithm and quantum computer for inference.
result Quantum discriminator achieves 99% accuracy on Iris dataset.
Quantum CNNs improve on multi-channel data processing.
problem Lack of efficient processing for multi-channel data in QCNNs.
method Developed hardware-adaptable quantum circuit ansatzes for convolutional kernels.
result Quantum CNNs outperform existing QCNNs on multi-channel data classification tasks.