Qiskit ™
Table Of Contents
Table Of Contents

Development Strategy


We are going to look out 12 months to establish a set of goals we want to work towards. When planning, we typically look at potential work from the perspective of the elements.

Qiskit Terra

In 2018 we worked on formalizing the backends and user flow in Qiskit Terra. The basic idea is as follows: the user designs a quantum circuit and then, through a set of transpiler passes, rewrites the circuit to run on different backends with different optimizations. We also introduced the concept of a provider, whose role is to supply backends for the user to run quantum circuits on. The provider API we have defined at version one supplies a set of schemas to verify that the provider and its backends are Terra-compatible.

In 2019, we have many extensions planned. These include:

  • Add passes to the transpiler. The goal here is to be more efficient in circuit depth as well as adding passes that find approximate circuits and resource estimations.
  • Introduce a circuit foundry and circuit API. This has the goal of making sure that a user can easily build complex circuits from operations. Some of these include adding controls and power to operations, and inserting unitary matrices directly.
  • Provide an API for OpenPulse. Now that OpenPulse is defined, and the IBM Q provider can accept it, we plan to build out the pulse features. These will include a scheduler and tools for building experiments out of pulses. Also included will be tools for mapping between experiments with gates (QASM) to experiments with pulses.

Qiskit Aer

The first release of Qiskit Aer was made avaialble at the end of 2018. It included C++ implementations of QASM, statevector, and unitary simulators. These are the core to Qiskit Aer, and replace the simulators that existed in Terra. The QASM simulator includes a customizable general (Kraus) noise model, and all simulators are include CPU parallelization through the OpenMP library.

In 2019, Aer will be extended in many ways:

  • Optimize simulators. We are going to start profiling the simulators and work on making them faster. This will include automatic settings for backend configuration and OpenMP parallelization configuration based on the input Qobj and available hardware.
  • Develop additional simulator backends. We will include several approximate simulator backends that are more efficient for specific subclasses of circuits, such as the T-gate simulator, which works on Clifford and T gates (with low T-depth), and a stabilizer simulator, which works just on Clifford gates.
  • Add noise approximation tools. We plan to add noise approximation tools to mapping general (Kraus) noise models to approximate noise model that may be implemented on an approximate backends (for example only mixed Clifford and reset errors in the noise model).

Qiskit Ignis

This year, we are going to release the first version of Qiskit Ignis. The goal of Ignis is to be a set of tools for characterization of errors, improving gates, and enhancing computation in the presence of noise. Examples of these tools include optimal control, dynamical decoupling, and error mitigation.

In 2019, Ignis will include tools for:

  • quantum state/process tomography
  • randomized benchmarking over different groups
  • optimal control (e.g., pulse shaping)
  • dynamical decoupling
  • circuit randomization
  • error mitigation (to improve results for quantum chemistry experiments)

Qiskit Aqua

Aqua is an open-source library of quantum algorithms and applications, introduced in June 2018. As a library of quantum algorithms, Aqua comes with a rich set of quantum algorithms of general applicability—such as VQE, QAOA, Grover’s Search, Amplitude Estimation and Phase Estimation—and domain-specific algorithms-such as the Support Vector Machine (SVM) Quantum Kernel and Variational algorithms, suitable for supervised learning. In addition, Aqua includes algorithm-supporting components, such as optimizers, variational forms, oracles, Quantum Fourier Transforms, feature maps, multiclass classification extension algorithms, uncertainty problems, and random distributions. As a framework for quantum applications, Aqua provides support for Chemistry (released separately as the Qiskit Chemistry component), as well as Artificial Intelligence (AI), Optimization and Finance. Aqua is extensible across multiple domains, and has been designed and structured as a framework that allows researchers to contribute their own implementations of new algorithms and algorithm-supporting components.

Over the course of 2019, we are planning to enrich Aqua as follows:

  • We will include several new quantum algorithms, such as Deutsch-Jozsa, Simon’s, Bernstein-Vazirani, and Harrow, Hassidim, and Lloyd (HHL).
  • We will improve the performance of quantum algorithms on top of both simulators and real hardware.
  • We will provide better support for execution on real quantum hardware.
  • We will increase the set of problems supported by the AI, Optimization and Finance applications of Aqua.

Qiskit Chemistry

Qiskit Chemistry is the first end-to-end software stack that enables experimenting with chemistry problems on Noisy Intermediate-Scale Quantum (NISQ) computers. It translates high-level chemistry problem specifications into into inputs for Aqua algorithms, which are then executed on top of IBM quantum hardware of simulators. Qiskit Chemistry is an Aqua application. As such, it was designed to be modular and extensible, and to allow users with different levels of experience to execute chemistry experiments and contribute to the quantum computing chemistry software stack. Qiskit Chemistry continues to be the most advanced quantum chemistry application available, with support for the computation of a molecule’s ground state energy and dipole moment, and with the inclusion of numerous chemistry-specific algorithmic components.

In 2019, we are planning to enrich Qiskit Chemistry as follows:

  • Improved scalability to support the simulation of larger molecules and/or the use of more sophisticated basis sets
  • Enhanced support for the execution of chemistry experiments on real hardware
  • Support for new chemistry problems, such as the computation of a molecule’s excited states


These are examples of just some of the work we will be focusing on in the next 12 months. We will continuously adapt the plan based on feedback. Please follow along and let us know what you think!

Component Status

Qiskit is developing so fast that is it is hard to keep all different parts of the API supported for various versions. We do our best and we use the rule that for one minor version update, for example 0.6 to 0.7, we will keep the API working with a deprecated warning. Please don’t ignore these warnings. Sometimes there are cases in which this can’t be done and for these in the release history we will outline these in great details.

This being said as we work towards Qiskit 1.0 there are some modules that have become stable and the table below is our attempt to label them


Name status Note
assembler stable completed in version 0.9
circuit unstable the goal is stable version in 0.11
converters unstable the goal is stable version in 0.11
compiler stable completed in version 0.9
dagcircuit remove will be part of circuits
extensions remove will be part of circuits
providers stable completed in version 0.7
pulse unstable the goal is stable in version 0.11
qasm unstable passer location to be determined
qobj unstable the goal is stable version in 0.11
quantum_info unstable the goal is stable version in 0.11
result stable completed in version 0.7
schemas stable completed in version 0.7
tools unstable various elements to be removed
tests unstable the goal is stable version in 0.11
transpiler stable completed in version 0.9
validation stable completed in version 0.7
visualization stable completed in version 0.9

Basic Aer Provider

This is stable the addition here a name change of the folder to basicaer in version 0.8

Aer Provider





The Qiskit project is made up of several elements each performing different functionality. Each is independently useful and can be used on their own, but for convenience we provide this repository and meta-package to provide a single entrypoint to install all the elements at once. This is to simplify the install process and provide a unified interface to end users. However, because each Qiskit element has it’s own releases and versions some care is needed when dealing with versions between the different repositories. This document outlines the guidelines for dealing with versions and releases of both Qiskit elements and the meta-package.

For the rest of this guide the standard Semantic Versioning nomenclature will be used of: Major.Minor.Patch to refer to the different components of a version number. For example, if the version number was 0.7.1, then the major version is 0, the minor version 7, and the patch version 1.

Meta-package Version

The Qiskit meta-package version is an independent value that is determined by the releases of each of the elements being tracked. Each time we push a release to a tracked component (or add an element) the meta-package requirements, and version will need to be updated and a new release published. The timing should be coordinated with the release of elements to ensure that the meta-package releases track with element releases.

Adding New Elements

When a new Qiskit element is being added to the meta-package requirements, we need to increase the Minor version of the meta-package.

For example, if the meta-package is tracking 2 elements qiskit-aer and qiskit-terra and it’s version is 0.7.4. Then we release a new element qiskit-ignis that we intend to also have included in the meta-package. When we add the new element to the meta-package we increase the version to 0.8.0.

Patch Version Increases

When any Qiskit element that is being already tracked by the meta-package releases a patch version to fix bugs in a release we need also bump the requirement in the setup.py and then increase the patch version of the meta-package.

For example, if the meta-package is tracking 3 elements qiskit-terra==0.8.1, qiskit-aer==0.2.1, and qiskit-ignis==0.1.4 with the current version 0.9.6. When qiskit-terra release a new patch version to fix a bug 0.8.2 the meta-package will also need to increase it’s patch version and release, becoming 0.9.7.

Additionally, there are occasionally packaging or other bugs in the meta-package itself that need to be fixed by pushing new releases. When those are encountered we should increase the patch version to differentiate it from the broken release. Do not delete the broken or any old releases from pypi in any situation, instead just increase the patch version and upload a new release.

Minor Version Increases

Besides adding a new element to the meta-package the minor version of the meta-package should also be increased anytime a minor version is increased in a tracked element.

For example, if the meta-package is tracking 2 elements qiskit-terra==0.7.0 and qiskit-aer==0.1.1 and the current version is 0.7.5. When the qiskit-aer element releases 0.2.0 then we need to increase the meta-package version to be 0.8.0 to correspond to the new release.

Major Version Increases

The major version is different from the other version number components. Unlike the other version number components, which are updated in lock step with each tracked element, the major version is only increased when all tracked versions are bumped (at least before 1.0.0). Right now all the elements still have a major version number component of 0 and until each tracked element in the meta-repository is marked as stable by bumping the major version to be >=1 then the meta-package version should not increase the major version.

The behavior of the major version number component tracking after when all the elements are at >=1.0.0 has not been decided yet.

Qiskit Element Requirement Tracking

While not strictly related to the meta-package and Qiskit versioning how we track the element versions in the meta-package’s requirements list is important. Each element listed in the setup.py should be pinned to a single version. This means that each version of Qiskit should only install a single version for each tracked element. For example, the requirements list at any given point should look something like:

requirements = [

This is to aid in debugging, but also make tracking the versions across multiple elements more transparent.

It is also worth pointing out that the order we install the elements is critically important too. pip does not have a real dependency solver which means the installation order matters. So if there are overlapping requirements versions between elements or dependencies between elements we need to ensure that the order in the requirements list installs everything as expected. If the order needs to be change for some install time incompatibility it should be noted clearly.