Amazon Braket Python SDK

The Amazon Braket Python SDK is an open source library to design and build quantum circuits, submit them to Amazon Braket devices as quantum tasks, and monitor their execution.

This documentation provides information about the Amazon Braket Python SDK library. The project homepage is in Github https://github.com/amazon-braket/amazon-braket-sdk-python. The project includes SDK source, installation instructions, and other information.

Getting Started

• Getting Started with the Amazon Braket Python SDK
  • Getting started using an Amazon Braket notebook
  • Getting started in your environment

Examples

Explore Amazon Braket examples.

• Examples
  • Getting started
    • Getting started
    • Running quantum circuits on simulators
    • Running quantum circuits on QPU devices
    • Superdense coding
    • Deep Dive into the anatomy of quantum circuits
  • Amazon Braket features
    • Getting Started with OpenQASM on Braket
    • Getting notifications when a quantum task completes
    • Adjoint Gradient Result Type
    • Verbatim Compilation
    • Using the Barrier Statement on Amazon Braket
    • Advanced OpenQASM programs using the Local Simulator
    • Using the experimental local simulator
    • Using the tensor network simulator TN1
    • TN1 and Hayden-Preskill circuits
    • Simulating noise on Amazon Braket
    • Error Mitigation on IonQ
    • Noise Models on Amazon Braket
    • Noise Models on Rigetti’s device
    • IQM Garnet Native Gates
    • IonQ Native Gates
    • Allocating Qubits on QPU Devices
    • Getting Devices and Checking Device Properties
    • Getting started with Amazon Braket program sets
    • Using the local emulator
    • Expectation value calculations with Amazon Braket program sets
    • Introduction to Amazon Braket spending limits
  • Advanced circuits and algorithms
    • Grover
    • Quantum Amplitude Amplification
    • Quantum Fourier Transform
    • Quantum Phase Estimation
    • Randomness Generation
    • Simon’s Algorithm
  • Hybrid quantum algorithms
    • Quantum Approximate Optimization Algorithm
    • VQE Chemistry
    • VQE Transverse Ising
  • Analog Hamiltonian simulation
    • Noisy quantum dynamics
    • Getting Started with Analog Hamiltonian Simulation
    • Getting Started with Aquila
    • Ordered Phases in Rydberg Systems
    • Parallel Tasks on Aquila
    • Maximum Independent Sets
    • Running on Local Simulator
    • Simulation with PennyLane
    • Simulating lattice gauge theory with Rydberg atoms
    • Maximum weight independent set
  • Amazon Braket Hybrid Jobs
    • Getting started with Amazon Braket Hybrid Jobs
    • Quantum machine learning in Amazon Braket Hybrid Jobs
    • QAOA with Amazon Braket Hybrid Jobs and PennyLane
    • Bring your own containers to Braket Hybrid Jobs
    • Embedded simulators in Braket Hybrid Jobs
    • Parallelize training for Quantum Machine Learning
    • QN-SPSA optimizer using an Embedded Simulator
    • Running Jupyter notebooks as a Hybrid Job
    • Creating Hybrid Job Scripts
  • Pulse control
    • Bringup Experiments
    • Native Gate Calibrations
    • Bell pair with pulses on Rigetti
    • Build single qubit gates
  • Quantum machine learning and optimization with PennyLane
    • Combining PennyLane with Amazon Braket
    • Computing gradients in parallel with PennyLane-Braket
    • Graph optimization with QAOA
    • Hydrogen geometry with VQE
    • Simulation of Noisy Circuits with PennyLane-Braket
    • Tracking Resource Usage
    • Adjoint Gradient Computation
  • Using Qiskit with Amazon Braket
    • Getting started with Qiskit on Amazon Braket
    • Compiling Braket circuits with the Qiskit-Braket provider
  • Experimental capabilities
    • Dynamic Circuits on Qiskit with Amazon Braket
    • Getting Started with Dynamic Circuits on IQM
    • Dynamic Circuit Constructions
    • Handling Noise with Dynamic Circuits
    • Open Quantum Systems with Dynamic Circuits
    • Dynamic Circuits with OpenQASM 3.0 on Amazon Braket
  • Using NVIDIA CUDA-Q with Amazon Braket
    • Getting started with CUDA-Q on Amazon Braket
    • Using the CUDA-Q Applications Hub
    • Using the CUDA-Q Academic library
    • CUDA-Q with Braket Hybrid Jobs
    • Simulating quantum programs on GPUs
    • Parallel simulations on multiple GPUs
    • Distributed state vector simulations on multiple GPUs
  • Error Mitigation
    • Getting started with Mitiq on Braket
    • Readout mitigation with mitiq
    • Zero noise extrapolation with mitiq
    • Twirling with program sets
    • Error mitigation workflow with mitiq
    • Advanced QPU error mitigation workflow

Python SDK APIs

The Amazon Braket Python SDK APIs:

• API Reference
  • Analog Hamiltonian Simulation
  • Annealing
  • Aws
  • Circuits
  • Devices
  • Emulation
  • Error Mitigation
  • Experimental Capabilities
  • Jobs
  • Parametric
  • Program Sets
  • Pulse
  • Quantum Information
  • Registers
  • Tasks
  • Timings
  • Tracking
  • IpythonUtils