Search Results for author:
Found 13 papers, 10 papers with code
Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
Thus, Q-Seg emerges as a viable alternative for real-world applications using available quantum hardware, particularly in scenarios where the lack of labeled data and computational runtime are critical.
Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars
In this work, we propose Nav-Q, the first quantum-supported DRL algorithm for CFN of self-driving cars, that leverages quantum computation for improving the training performance without the requirement for onboard quantum hardware.
QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing
The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions.
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy
Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning.
MAQA: A Quantum Framework for Supervised Learning
In its general formulation, MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions, such as ensemble algorithms and neural networks.
Quantum Splines for Non-Linear Approximations
Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance.
Enabling Non-Linear Quantum Operations through Variational Quantum Splines
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms.
GCS-Q: Quantum Graph Coalition Structure Generation
The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard.
BILP-Q: Quantum Coalition Structure Generation
Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI.
Quantum Algorithm for Ensemble Learning
The idea of ensemble learning is to build a prediction model by combining the strengths of a collection of simpler base models.
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use.
Ranked #1 on
Supervised Image Retrieval
on CIFAR-10
Quantum Ensemble for Classification
We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models.
A Variational Algorithm for Quantum Neural Networks
Quantum Computing leverages the laws of quantum mechanics to build computers endowed with tremendous computing power.
