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Found 12 papers, 2 papers with code
Generative Learning of Continuous Data by Tensor Networks
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning.
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
In this study, we build over a proposed framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs).
Do Quantum Circuit Born Machines Generalize?
To the best of our knowledge, this is the first work in the literature that presents the QCBM's generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM's ability to generalize to high-quality, desired novel samples.
Generalization Metrics for Practical Quantum Advantage in Generative Models
Using the sample-based approach proposed here, any generative model, from state-of-the-art classical generative models such as GANs to quantum models such as Quantum Circuit Born Machines, can be evaluated on the same ground on a concrete well-defined framework.
GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models
The first uses data points previously evaluated by any quantum or classical optimizer, and we show how TN-GEO improves the performance of the classical solver as a standalone strategy in hard-to-solve instances.
Combinatorial Optimization
Portfolio Optimization
+1
Quantum Physics
Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer
Generating high-quality data (e. g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning.
Quantum Physics
Robust Implementation of Generative Modeling with Parametrized Quantum Circuits
Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today.
Quantum Physics
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications.
Quantum Physics
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques.
Quantum Physics Emerging Technologies
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
Bayesian Network Structure Learning Using Quantum Annealing
The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.
A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems
Diagnosing the minimal set of faults capable of explaining a set of given observations, e. g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques.
Quantum Physics
