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Upgrade Instructions

Python Environment

  1. Classiq is now supported on Google Colab.

Interface Changes

  1. Change the default authentication URL from to The Classiq IDE and Python SDK update automatically.
  2. Register names of arithmetic operations are no longer customizable. See Arithmetic and sub-pages for the up-to-date interfaces.
  3. The default URL for the backend is changed from to The Classiq IDE and Python SDK update automatically.
  4. Remove the is_uniform_start flag from StatePreparation. See StatePreparation.
  5. Remove "dirty" arithmetic uncomputation options. See Arithmetic Expressions.
  6. Synthesis and execution flow has been updated to work on textual inputs.

    • A get_model method as been added to Model in order to obtain the textual serialized underlying model.
    • Model synthesis is now done by serialized_quantum_program = synthesize(serialized_model) instead of model.synthesize(). See synthesis.
    • Execution of the quantum algorithms is now done by execute(serialized_quantum_program). See execution
      • The execution flow is now incorporated as part of the model. In order to add it to the Model instance use the sample method.
    • Setting additional preferences and constraints for the synthesis and execution engines is done by the dedicated functions (that work on the pre synthesis serialized model):
      • set_preferences(serialized_model, preferences) for setting the synthesis preferences.
      • set_constraints(serialized_model, constraints) for setting the synthesis constraints.
      • set_execution_preferences(serialized_model, execution_preferences) for setting the execution preferences.
  7. Application suites have been changed to work with the new flow by adding specific model constructor function for each application.

    • construct_chemistry_model function creates a model for solving ground state problems with VQE.
    • construct_combinatorial_optimization_model function creates a model for solving combinatorial optimization problems with QAOA.
    • construct_grover_model function creates a model for using the "Grover Search" algorithm.
    • construct_qsvm_model function creates a model for running the QSVM algorithm.
    • construct_finance_model function creates a model for solving finance problems.


  1. Multiple Execution: Users can select up to five hardware backends for simultaneous execution.
  2. Generated model in the backend for VQE, finance and Grover: A “generate model” button has been added which generates the model based on the form values in the IDE.
  3. Modifications to the home screen: fast execution page, registration page.
  4. Modification to application nav bar: Added “learn” section with links to tutorial and Python SDK installation guide.
  5. The results page now supports multiple results per run.
  6. The execution page was simplified. There are now much fewer execution options on the page, and they are instead handled automatically via model options.
  7. The Examples menu changed location and its interface and will be extended soon.
  8. Hardware providers can now be selected from a dropdown list on the synthesis page.
  9. Output formats selected from the dropdown in the Synthesis page can now be exported via the Circuit page.

Bug fixes

  1. Correctly assign qubits for sliced inputs in the main function.


  1. Introduce new amplitude loading functions. See PiecewiseLinearRotationAmplitudeLoading and PiecewiseLinearAmplitudeLoading.


  1. Add introduction tutorials. See tutorials.
  2. Deprecate light mode.
  3. Add auto completion when searching. Use the right arrow key to accept the suggestion.
  4. Highlight the searched keywords when entering a page using the search widget.