📊 Datasets — Awesome Quantum Computing

70,000 28×28 grayscale images of handwritten digits (0–9) — the classic image-classification benchmark.

The Max-Cut problem is an optimization problem that involves partitioning a graph's vertices into two sets to maximize the number of edges between the sets, and it is used to evaluate the performance of quantum adiabatic algorithms in detecting multiple solutions.

A drop-in MNIST replacement with 70,000 grayscale images across 10 clothing categories.

The transverse-field Ising model is a quantum many-body system that describes the dynamics of spins under the influence of a transverse magnetic field, and it is used to evaluate the effectiveness of Trotterization methods in digital quantum simulations.

The 'H-2' dataset contains the potential energy surface of the hydrogen molecule (H\(_2\)) across 100 bond lengths and is used to evaluate the performance of the Variational Quantum Eigensolver (VQE) algorithm in computing ground state energies.

The 'LiH' dataset/benchmark contains data related to the lithium hydride molecule and is used to evaluate quantum algorithms for solving the ground state problem in quantum chemistry.

The 'Fermi-Hubbard models' dataset contains data on quantum many-body ground states used to evaluate the effectiveness of an unsupervised machine-learning framework in discovering compressed representations and optimizing energy minimization within latent spaces.

CIFAR-10 is a dataset containing 60,000 32x32 color images in 10 different classes, commonly used to evaluate the performance of machine learning algorithms in image classification tasks.

The Iris dataset contains measurements of iris flowers from three different species and is used to evaluate the performance of machine learning models in classification tasks.

The 'BeH-2' dataset/benchmark contains data related to the BeH2 molecule and is used to evaluate the performance of the unitary coupled cluster algorithm in handling strong correlations and geometrical distortions in quantum computations.

The 'Bell-state' dataset/benchmark contains quantum circuits used for generating Bell states, which are utilized to evaluate the correctness of quantum error detection and entanglement generation methods.

The 'GHZ-state' refers to a specific type of quantum state used to evaluate the performance of quantum circuits, particularly in the context of error detection and entanglement generation, and it involves circuits that generate Greenberger-Horne-Zeilinger states with up to 8,192 qubits.

The 'H-2O' dataset/benchmark contains data related to the molecular ground state problem in quantum chemistry and is used to evaluate the efficiency of quantum algorithms for solving this problem.

The Traveling Salesman Problem is a combinatorial optimization problem that involves finding the shortest possible route that visits a set of cities and returns to the origin city, and it is used to evaluate different optimization formulations and algorithms.

The 'H-4' dataset/benchmark contains data related to the electronic structure of the H4 molecule and is used to evaluate the accuracy of excited-state calculations on quantum computers.

The 'N-2' dataset/benchmark evaluates the accuracy of quantum chemistry simulations, specifically focusing on the potential energy surface of the nitrogen molecule (N₂) using various quantum computational methods.

The Hubbard model is a theoretical framework used to describe interacting particles in a lattice, specifically evaluating fermionic systems in condensed matter physics.

The Ising model is a benchmark used to validate quantum simulation protocols by extracting scaling dimensions of scaling operators from the spectrum of a system of qubits arranged on a polyhedral lattice.

The Traveling Salesperson Problem (TSP) is a combinatorial optimization problem that involves finding the shortest possible route that visits a set of cities and returns to the origin city, and it is used to evaluate heuristic state preparation routines in quantum algorithms.

The Heisenberg model is a Hamiltonian formulation used to evaluate real-time dynamics in lattice field theories, particularly in the context of simulating quantum systems.

The 'IBM Quantum device' refers to superconducting qubits used to evaluate noise characteristics and non-Markovian effects in quantum processors during circuit execution.

IBM superconducting quantum processors are programmable quantum hardware used to evaluate feedback-directed quantum dynamics through measurement-based operations in large-scale simulations of up to 100 qubits.

The Maximum Independent Set (MIS) is a combinatorial optimization problem that involves finding the largest set of vertices in a graph such that no two vertices in the set are adjacent, and it is used to evaluate the performance of quantum-enhanced algorithms in solving complex optimization challenges.

The Quantum Fourier Transform (QFT) is a quantum algorithm used to evaluate the efficiency of qubit reuse and optimization techniques in quantum circuits.

The '[[7, 1, 3]]' dataset/benchmark contains a quantum error-correcting code that is used to evaluate the effectiveness of encoding exponential operations in terms of logical error rates and noise suppression in quantum processors.

Burgers' equation is a fundamental partial differential equation used to model the evolution of velocity fields in fluid dynamics, and it serves as a benchmark for evaluating quantum algorithms in simulating statistical properties of these fields.

Dataset Card for digits dataset Optical recognition of handwritten digits dataset Note - How to load this dataset directly with the datasets library from datasets import load_dataset dataset = load_dataset("sklearn-docs/digits",header=None) Dataset Summary This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits The data set contains images of hand-written… See the full description on the dataset page: https://huggingface.co/datasets/sklearn-docs/digits.

The 'GHZ' dataset/benchmark contains quantum circuits used to evaluate the performance and fidelity of quantum protocols, particularly in the context of distributed quantum computing under noise.

The 'H-3+' dataset/benchmark contains data related to the ground state energy of the H3+ molecule and is used to evaluate the performance of quantum computing methods in estimating molecular ground-state energies.

The 'IBMQ' dataset/benchmark contains a gate-based digital quantum simulator used to evaluate the performance and dynamics of quantum gates implemented through dynamical decoupling protocols.

IBM's quantum hardware consists of quantum processor units (QPUs) that are used to evaluate the performance of distributed quantum computing systems and their ability to execute complex quantum algorithms under noisy conditions.

Dataset Card for PathMedMNIST This is currently the PathMedMNIST part of the MedMNIST dataset in 64x64 resolution. Dataset Source Website: https://medmnist.com/ The entire dataset on HF albertvillanova/medmnist-v2

QASMBench is a benchmarking initiative used to evaluate quantum algorithms in the context of combinatorial optimization problems.

The Sherrington-Kirkpatrick (SK) benchmark is a dataset used to evaluate the performance of optimization algorithms, particularly in the context of solving optimization problems in statistical physics.

The Variational Quantum Eigensolver (VQE) is a quantum algorithm used to evaluate the ground state energy of quantum systems, and it serves as a benchmark for optimizing quantum circuits in the context of qubit reuse and dynamic circuit techniques.

'3-regular graphs' are a type of graph where each vertex has exactly three edges, and they are used to evaluate the performance of quantum algorithms, specifically in the context of the MaxCut optimization problem.

The '[[5, 1, 3]]' dataset/benchmark refers to a quantum error-correcting code that encodes one logical qubit into five physical qubits, providing a distance of three, and is used to evaluate the effectiveness of error correction in suppressing noise during quantum operations.

Bivariate bicycle (BB) codes are a type of two-dimensional topological translationally-invariant quantum code used to evaluate the effectiveness of graph-matching decoders in correcting errors for fault-tolerant quantum computation.

The Calderbank-Shor-Steane codes are a class of quantum error-correcting codes used to evaluate the implementation of weak transversal gates and their efficiency in fault-tolerant quantum computing architectures.

CartPole is a benchmark environment used to evaluate quantum reinforcement learning algorithms, where the goal is to balance a pole on a moving cart.

The Greenberger-Horne-Zeilinger state is a specific type of entangled quantum state involving three qubits, used to evaluate the performance and correctness of quantum subroutines in the context of unit testing.

The 'Hydrogen molecule' dataset/benchmark is used to evaluate quantum algorithms by modeling potential energy curve calculations in quantum chemistry.

The IBM Quantum Platform is a set of quantum computing devices and tools used to evaluate and characterize state-preparation and measurement errors in quantum operations.

The 'IonQ' dataset/benchmark contains circuits constructed from native gates specific to IonQ's quantum processing units and is used to evaluate the performance of simulation techniques for Quantum Machine Learning algorithms.

The 'Maximum Clique' benchmark contains instances of the maximum clique problem, which is used to evaluate algorithms for finding the largest complete subgraph within a given graph.

The 'Maximum Cut' is a benchmark problem in optimization that involves partitioning the vertices of a graph into two sets to maximize the number of edges between the sets, and it is used to evaluate the performance of quantum optimization algorithms.

QAOA (Quantum Approximate Optimization Algorithm) is a benchmark used to evaluate the performance of quantum circuits, specifically in generating samples from both ideal and noisy quantum circuits with up to 476 qubits.

134k small organic molecules with computed quantum-chemical properties, for molecular-property prediction.

Quadratic Unconstrained Binary Optimization (QUBO) is a class of combinatorial optimization problems that is used to evaluate the performance of Variational Quantum Algorithms (VQAs) through metrics such as feasibility, quality, and reproducibility.

The Schwinger model is a theoretical framework used to study chiral dynamics in quantum field theory, specifically focusing on the chiral magnetic effect (CME) and is utilized to evaluate quantum error mitigation methods in noisy quantum simulations.

Transverse-field Ising models (TFIMs) are quantum systems used to evaluate the ground and low-lying excited states, particularly in the context of studying phase transitions, such as ferromagnetic to paramagnetic transitions.

The '1D Hubbard models' dataset contains quantum many-body systems that are used to evaluate the performance of quantum algorithms in preparing ground states and their energies, particularly in the context of strongly correlated electron systems.

The '3D Poisson equation' dataset/benchmark contains large-scale linear systems problems used to evaluate the performance of numerical solvers, particularly in the context of quantum-accelerated methods.

The 'BB-84' dataset/benchmark is used to evaluate quantum communication rates and the quantum capacity thresholds of various quantum channels, particularly in the context of permutation-invariant quantum codes.

The Bose-Hubbard model is a theoretical framework used to describe interacting bosons on a lattice, which is employed to evaluate quantum states and dynamics in many-body quantum systems.

Dataset Card for "breast-cancer" Dataset was taken from the MedSAM project and used in this notebook which fine-tunes Meta's SAM model on the dataset. More Information needed

CIFAR is a dataset used for evaluating image classification tasks, containing a collection of images across multiple classes.

The 'FeMo-cofactor in nitrogenase' is a complex containing eight transition metal centers, used to evaluate the efficiency of quantum algorithms in preparing initial states for simulating strongly correlated molecular systems.

The 'Fermi-Hubbard' dataset/benchmark contains Hamiltonians used to evaluate the performance of quantum optimization algorithms in achieving lower final relative errors to the ground state energy or infidelity.

Dataset Card for [FrozenLake-v1]