28 datasets found

The dataset used in this paper is a collection of quantum neural network models, including VQA, CV, swap test and phase estimation, RUS, quantum generalization, QBM, QCVNN,...

The dataset used in this paper is a collection of sinusoidal functions with different dimensions.

Generative modeling approach using quantum gates for image and text generation

The synthetic dataset D used in the main text is constructed by generating a set of data points {x(i)} with x(i) ∈ R3. The optimal circuit is Ux = RY(x1) ⊗ RY(x2) ⊗ RY(x3). The...

The dataset used in this paper is a collection of quantum graphs, where each graph represents a photon path to detectors, and edges between vertices indicate correlation between...

Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are...

The dataset used in this paper is a family of resettable stochastic clocks, where the agent is tasked with behaving as a clock with stochastic tick events, that may be reset by...

Design and implementation of a two-qubit quantum comparator circuit (q-cc).

Performing addition on IBM's quantum computers.

Quantum In Graphicon'2013. Hough Transform.

A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a...

The dataset used in the paper is the Asian option dataset, which consists of 32 intervals (5 qubits) and a minimum value of 17. and a maximum value of 300.

The dataset used in the paper is a collection of measurement outcomes from dynamical data, used to learn families of interacting many-body Hamiltonians.

The dataset used in the paper is the Traveling Salesman Problem (TSP) instances, specifically the Burma'7 instance, and six additional instances with 5 to 14 nodes.

The dataset used in the paper is a user study dataset, where participants are shown query-document pairs and asked questions about different relevance dimensions.

The dataset used in the paper is a reinforcement learning-based approach for automated quantum adiabatic algorithm design. The dataset consists of Grover search and 3-SAT problems.

Maximum-likelihood quantum state tomography dataset

The dataset used in this paper is a collection of lattice QCD simulations, specifically the three-point correlation function data of nucleon vector and axial-vector charges.

The dataset used in this paper is a dataset for training quantum convolutional neural networks.

The dataset used in this paper is a quantum convolutional neural network (QCNN) dataset.