microsoft/QuantumArchived
Quantum
Quantum is a quantum development framework that provides a hybrid quantum-classical workflow for coordinating execution between classical host languages and quantum processors. It includes a quantum hardware simulator for modeling state evolution and a library of quantum algorithms for tasks such as integer factorization, database search, and quantum arithmetic.
The project offers specialized tools for quantum hardware characterization and error correction to manage noise and decoherence. It provides resource estimation modeling to calculate the qubit count and gate depth required to execute specific algorithms on physical devices.
The system covers a broad range of capabilities including quantum machine learning, quantum chemistry simulation, and Hamiltonian evolution modeling. It integrates with Python for executing quantum logic and provides utilities for remote job submission and workspace provisioning to deploy programs to cloud-based hardware.
The repository includes a collection of development kit samples and reference implementations for writing and executing quantum programs.
Features
- Hybrid Quantum-Classical Execution - Coordinates computational tasks between classical processors and quantum hardware using a host-language integration layer.
- Quantum Error Correction - Implements error-correcting codes to protect quantum information from noise and decoherence.
- Hybrid Quantum-Classical Computing - Integrates classical programming languages with quantum logic to coordinate workloads across different processors.
- Quantum Algorithm Implementation - Provides libraries for creating and testing quantum procedures for tasks like database searching and integer factorization.
- Quantum Circuit Execution - Enables running quantum circuits on physical hardware or simulators to validate logical operations.
- Quantum Simulators - Provides a hardware simulator for modeling quantum states and gate operations on classical hardware.
- Quantum-Enhanced Training - Implements a framework for building and training sequential models using quantum-enhanced datasets.
- Quantum Machine Learning Algorithms - Provides frameworks for building and training sequential models using quantum-enhanced data processing.
- Arithmetic Operations - Provides capabilities to perform coherent arithmetic operations within a quantum state.
- Quantum Logic Integrations - Allows the execution of quantum logic and computations within Python scripts or notebooks.
- Hardware-Targeted Remote Execution - Provides a mechanism to send computations to remote quantum hardware via terminals or IDEs.
- Remote Compute Job Submission - Provides a mechanism to transmit quantum instruction sets to cloud-based hardware or simulators via CLI.
- Quantum Resource Estimation - Calculates the hardware requirements and physical resources needed to run specific quantum algorithms.
- System Characterization - Provides analytical tools to determine the physical properties of quantum hardware components using classical data.
- Quantum Hardware Characterizations - Analyzes classical data to determine physical properties of quantum components and implements noise management.
- Time-Evolution Modeling - Models the temporal dynamics and state evolution of quantum systems under physical constraints.
- Quantum Chemistry Simulations - Simulates molecular interactions and chemical systems by modeling state evolution and physical constraints.
- Quantum Hardware Characterizations - Analyzes classical output data to determine the operational properties and noise profiles of quantum hardware.
- Resource Estimation Utilities - Calculates the required qubit count and gate depth needed to execute algorithms on physical devices.
- Quantum Resource Estimations - Calculates the required qubit count and gate depth needed to execute specific algorithms on physical devices.
