Introduction

Welcome to the documentation for Qiskit and IBM Quantum. Find how-to guides, specific use-case tutorials, and API references for your quantum research and development. Use Qiskit and its related packages and tools to build, optimize, and execute workflows on the IBM® fleet of quantum computers.

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Get started

• Quickstart

  Build a quantum circuit in under two minutes - no sign-in or API key required.

  Start building

• Tutorials

  Try end-to-end examples that apply Qiskit to common quantum computing use cases.

  Browse tutorials

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Tools by task

Map problem to circuits

• AQC-Tensor - A Qiskit addon for building time evolution circuits
• Qiskit circuit library - The Qiskit SDK standard library of gates and circuit instructions
• Optimization mapper - A Qiskit addon for mapping optimization problems to circuits and operators
• Quantum optimization best practices - A collection of guidelines to run quantum optimization workloads
• Device benchmarking

Prepare and optimize workloads

• Transpiler - Translate circuit instructions to execute on quantum hardware, with options for efficient layout and routing
• Dynamic circuits - Perform certain tasks, such as long-range entanglement and state preparation, at constant depth
• Dynamical decoupling - Reduce decoherence errors on idle qubits
• Operator backpropagation - Reduce circuit depth by trimming operations at the cost of increased measurements

Debug

• Noisy estimator analyzer tool - Gauge the expected performance of quantum workloads
• Local testing mode - Simulate smaller or Cliffordized circuits to assess performance

Execute on hardware

• Estimator and Sampler primitives - Handles hardware execution and returns samples or expectation values, with some built-in error suppression/mitigation support
• Execution modes - Efficiently schedule workload execution
• Executor primitive - Generate and execute circuit variants based on the input samplex directive

Manage noise

• Samplomatic - Fine-tune error mitigation in specific circuit regions
• Noise learner - Learn and return the sparse Pauli-Lindblad noise model, which can be used for error mitigation methods like PEA, PEC, and PNA
• Pauli twirling - Convert coherent noise to better characterized stochastic noise
• Matrix-free measurement mitigation - Reduce measurement errors
• Probabilistic error cancellation - Mitigate errors by statistically canceling noise, resulting in an unbiased expectation value at the expense of greater overhead
• Zero-noise extrapolation - Mitigate errors by amplifying noise and extrapolating corrected expectation values
• Twirled readout error extinction - Use twirling to reduce measurement error
• Propagated noise absorption - Characterize and propagate the effects of noise into an observable
• Shaded lightcones - Reduce the overhead of PEC error mitigation

Post-process results

• Sample-based quantum diagonalization - Post-process samples to refine results in simulation workloads
• Multi-product formulas - Refine expectation values in workloads simulating time evolution
• Measurement post-selection - Refine samples by post-selecting known "bad" bitstrings

Qiskit Functions

• Algorithmiq Tensor-Network Error Mitigation Function
• Q-CTRL Performance Management Function
• QEDMA Qiskit Function QESEM
• Q-CTRL Optimization Solver

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Support

• Error code registry

  Look up error codes and suggested solutions.

  Find an error code

• Support page

  Find answers to frequently-asked questions, learn how to report bugs, discover communities to join, and more.

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