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Released on 2026-03-30.

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Enhancements

  • sample function: Added a new top-level sample function for executing a quantum program and retrieving results as a DataFrame directly, without managing a job object. Supports single and batch execution — pass a list of parameter dictionaries to parameters to run multiple parameter sets and receive a list of DataFrames. Also supports run_via_classiq=True to run using Classiq’s provider credentials against your allocated budget. See the SDK reference for details.
  • observe function: Added a new public observe function that computes the expectation value of a Hermitian observable with respect to a quantum program’s output state. Supports exact statevector calculation or shot-based estimation, batch execution, and run_via_classiq. See the SDK reference for details.
  • get_backend_details function: Added a get_backend_details function that returns a DataFrame of all supported quantum backends, including provider, backend name, type (hardware or simulator), qubit count, availability, pending jobs, and queue time.
  • calculate_state_vector function: Added a new public calculate_state_vector function that returns the full state vector of a quantum program as a DataFrame. Supports batch execution by passing a list of parameter dictionaries. Available on Classiq simulators (e.g. classiq/simulator).
  • minimize function: Added a new public minimize function for variational optimization of a cost function over the parameter values of a quantum program. Supports Hamiltonian and classical cost functions, run_via_classiq, and improved input validation. See the SDK reference for details.
  • OpenQASM in sample and ExecutionSession: You can pass OpenQASM 2.0 or 3.0 source as a string to sample() (first argument) or to ExecutionSession instead of a synthesized QuantumProgram. Results use the same histogram DataFrame shape (bitstring, counts, etc.). The parameters argument is not supported for OpenQASM strings (use a QuantumProgram for Qmod main parameters, or bind parameters inside the QASM circuit). See the Execution section of the user guide.
  • Improve error messages related to qfunc arguments.
  • Add emulate on AzureBackendPreferences to enable IonQ hardware noise simulation on Azure Quantum when using an IonQ QPU target (ionq.qpu.*); ignored for other Azure targets.
  • Add a QSVM application with a QSVM class that provides train, test, and predict methods for easy implementation of Quantum Support Vector Machine training and data classification. The relevant notebooks in the classiq-library will be updated accordingly.