- Explore projects and hardware providers that leverage the open-source + Explore projects and hardware providers that use the open-source Qiskit toolkit or are built on top of it.
@@ -103,7 +103,7 @@- Use these projects to compile and optimize your quantum circuits. + Compile and optimize your quantum circuits
- Explore packages that help apply quantum technology to real-world use - cases + Packages that help apply quantum technology to real-world use cases
NQI C2QA project to simulate hybrid boson-qubit systems within Qiskit.
+Qiskit Algorithms is a library of quantum algorithms based on Qiskit, suitable to run on near-term quantum devices with short-depth circuits.
Qiskit Algorithms is a library of quantum algorithms based on Qiskit, suitable to run on near-term quantum devices with short-depth circuits.
- -NQI C2QA project to simulate hybrid boson-qubit systems within Qiskit.
+ +Qiskit Nature PySCF is a third-party integration plugin of Qiskit Nature and PySCF.
+A library for supervised machine learning based on quantum computing and Riemannian geometry. The project is built on top of the Qiskit and pyRiemann projects and focuses on the classification of time series data.
A library for supervised machine learning based on quantum computing and Riemannian geometry. The project is built on top of the Qiskit and pyRiemann projects and focuses on the classification of time series data.
+Qiskit Nature PySCF is a third-party integration plugin of Qiskit Nature and PySCF.
The SSVQE algorithm (https://arxiv.org/abs/1810.09434) is a generalization of VQE to find low-lying eigenstates of a Hermitian operator. This specific implementation of SSVQE carries out one optimization procedure using weights.
+The SSVQE algorithm (arXiv:1810.09434) is a generalization of VQE to find low-lying eigenstates of a Hermitian operator. This specific implementation of SSVQE carries out one optimization procedure using weights.
The C3 package is intended to close the loop between open-loop control optimization, control pulse calibration, and model-matching based on calibration data.
+This platform allows you to execute quantum algorithms using the cQASM language.
This platform allows you to execute quantum algorithms using the cQASM language.
+The C3 package is intended to close the loop between open-loop control optimization, control pulse calibration, and model-matching based on calibration data.
Kaleidoscope
+Qlasskit is a Python library that allows quantum developers to write classical algorithms in pure Python and translate them into quantum circuits.
- Spinoza is a quantum state simulator (implemented in Rust) that is one of the fastest open-source simulators. Spinoza is implemented using a functional approach. Additionally, Spinoza has a QuantumCircuit object-oriented interface, which partially matches Qiskit's interface. Spinoza is capable of running in a myriad of computing environments (e.g., small workstations), and on various architectures.... At this juncture, Spinoza only utilizes a single thread; however, it is designed to be easily extended into a parallel version, as well as a distributed version. The paper associated with Spinoza is available at arXiv:2303.01493. -
- +Kaleidoscope
A Python package that uses a backend written in Julia to implement high performance features for standard Qiskit.
+ ++ Spinoza is a quantum state simulator (implemented in Rust) that is one of the fastest open-source simulators. Spinoza is implemented using a functional approach. Additionally, Spinoza has a QuantumCircuit object-oriented interface, which partially matches Qiskit's interface. Spinoza is capable of running in a myriad of computing environments (e.g., small workstations), and on various architectures.... At this juncture, Spinoza only utilizes a single thread; however, it is designed to be easily extended into a parallel version, as well as a distributed version. The paper associated with Spinoza is available at arXiv:2303.01493. +
+What would happen if you combine Tetris with a Quantum computer? The winning entry of the Quantum Design Jam from IBM and Parsons in October 2021 explores just that!
+A Python package that uses a backend written in Julia to implement high performance features for standard Qiskit.
an extension to Pytket (a python module for interfacing with CQC tket) that allows Pytket circuits to be run on IBM backends and simulators, as well as conversion to and from Qiskit representations.
+What would happen if you combine Tetris with a Quantum computer? The winning entry of the Quantum Design Jam from IBM and Parsons in October 2021 explores just that!
A lightweight framework to enable configurable memory consumption when simulating large quantum circuits.
+An extension to Pytket (a python module for interfacing with CQC tket) that allows Pytket circuits to be run on IBM backends and simulators, as well as conversion to and from Qiskit representations.
- Distributed quantum computing is a concept that proposes to connect multiple quantum computers in a network to leverage a collection of more, but physically separated, qubits. In order to perform distributed quantum computing, it is necessary to add the addition of classical communication and entanglement distribution so that the control information from one qubit can be applied to another that is... located on another quantum computer. For more details on distributed quantum computing, see the Medium blog post "Distributed Quantum Computing: A path to large scale quantum computing". In this project, we aim to validate distributed quantum algorithms using Qiskit. Because Qiskit does not yet come with networking features, we embed a "virtual network topology" into large circuits to mimic distributed quantum computing. The idea is to take a monolithic quantum circuit developed in the Qiskit language and distribute the circuit according to an artificially segmented version of a quantum processor. The inputs to the library are a quantum algorithm written monolithically (i.e., in a single circuit) and a topology parameter that represents the artificial segmentation of the single quantum processor. The algorithm takes these two inputs and remaps the Qiskit circuit to the specified segmentation, adding all necessary steps to perform an equivalent distributed quantum circuit. Our algorithm for achieving this is based on the work "Distributed Quantum Computing and Network Control for Accelerated VQE" (doi: 10.1109/TQE.2021.3057908). The algorithm output is another Qiskit circuit with the equivalent measurement statistics but with all of the additional logic needed to perform a distributed version. -
- +A lightweight framework to enable configurable memory consumption when simulating large quantum circuits.
+ Distributed quantum computing is a concept that proposes to connect multiple quantum computers in a network to leverage a collection of more, but physically separated, qubits. In order to perform distributed quantum computing, it is necessary to add the addition of classical communication and entanglement distribution so that the control information from one qubit can be applied to another that is... located on another quantum computer. For more details on distributed quantum computing, see the Medium blog post "Distributed Quantum Computing: A path to large scale quantum computing". In this project, we aim to validate distributed quantum algorithms using Qiskit. Because Qiskit does not yet come with networking features, we embed a "virtual network topology" into large circuits to mimic distributed quantum computing. The idea is to take a monolithic quantum circuit developed in the Qiskit language and distribute the circuit according to an artificially segmented version of a quantum processor. The inputs to the library are a quantum algorithm written monolithically (i.e., in a single circuit) and a topology parameter that represents the artificial segmentation of the single quantum processor. The algorithm takes these two inputs and remaps the Qiskit circuit to the specified segmentation, adding all necessary steps to perform an equivalent distributed quantum circuit. Our algorithm for achieving this is based on the work "Distributed Quantum Computing and Network Control for Accelerated VQE" (doi: 10.1109/TQE.2021.3057908). The algorithm output is another Qiskit circuit with the equivalent measurement statistics but with all of the additional logic needed to perform a distributed version. +
+ + +Write quantum programs as Python functions, rather than separate circuit objects. Create higher-level quantum data types, and return measurement results as bool-like objects.
+ +