Classiq 1.4.0

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Enhancements

• Updated the Classiq Platform home page.

Classiq 1.3.0

Released on 2026-02-22.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Enhancements

• The function poly_inversion now supports error_type ("relative" default or "uniform") to choose between minimizing relative error
  |xp(x)-1| or absolute uniform error |p(x)-1/x| over x in [1/kappa,1].
  The same error_type option is also available in poly_inversion_degree and poly_inversion_error.
• Add the foreach
  statement to Qmod. Foreach iterates efficiently through the elements of a
  classical array.
• Support QBits in addition to QNums in lookup_table.

API Changes

1. Renamed sample to cmain_sample in legacy cscope. This does not affect any usage of ExecutionSession.

Classiq 1.2.0

Released on 2026-02-15.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Enhancements

• Introduce CUDA-Q Integration, enabling translation of synthesized Qmod programs into Python CUDA-Q kernels, to leverage CUDA-Q’s high-performance simulation and hybrid quantum–classical workflows.

Bug Fixes

• Fix compilation of symbolic values in concatenations (for example,
  [q[i], q[j]] where i and j are of type CInt).

API Changes

• Rename parameter run_through_classiq of BackendPreferences to run_via_classiq. run_through_classiq is deprecated and will no longer be supported starting on 2026-03-09 at the earliest.

Classiq 1.1.0

Released on 2026-02-08.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Bug Fixes

• IDE - Phase legend mismatches actual phase
• Fix compilation of the I (identity) gate.
• Fix evaluation of constants in main function parameter types.
• Remove previously deprecated "count" column from execution result dataframe. Use "counts" column instead.

Enhancements

• IDE - Adjustments to the table shown in "State Vector" jobs
• IDE - Add table in "Measurement Results" jobs
• Noise models are now available for IonQ Simulators.

Classiq 1.0.1

Released on 2026-02-03.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Enhancements

• Reduce CX count and circuit depth for multi-controlled Pauli rotations (RX, RY, RZ).

Classiq 1.0.0

Released on 2026-02-02

Classiq 1.0

Classiq 1.0 consolidates capabilities introduced in recent releases into a stable, production-ready baseline.

This release brings major gains in Qmod expressiveness, stricter correctness guarantees around uncomputation, broader algorithm coverage, and new developer tooling. It introduces Classiq Studio, the Classiq AI code assistant, model-based visualization, and meaningful performance and usability improvements across the platform. Stability and reliability were strengthened, simulators were scaled, and integrations with new quantum hardware were formalized.

Highlights

Language and compiler features

• Automatic uncomputation with enforced uncomputation rules, ensuring circuits are correct by construction.
• Seamless integration of Python classical logic within Qmod functions, enabling the use of Python's expressive power, third-party libraries, and debugging tools.
• Initial support for runtime classical control flow, including local variables, mid-circuit measurements, and runtime if statements.
• Enhanced assignment semantics, including array slicing, non-scalar assignments, and in-place quantum subscript updates.
• Automatic conversion from QASM to Qmod.

Algorithmic components

Key algorithmic building blocks, along with their full Qmod implementations, are available as part of the Classiq 1.0 open-library:

• QSVT/GQSP functions, encapsulating the overall algorithm and its components. Polynomial approximations and phase factors for the protocols can be computed using the QSP utility functions.
• Utility functions for linear combination of unitaries, supporting block encoding constructions.
• A comprehensive set of quantum modular arithmetic functions (add, multiply, inverse, negate).
• Structured state preparation functions, such as sparse, dicke state, linear.

Execution

• Run-via-Classiq support across multiple quantum simulator and hardware providers.
  • One-click execution from a unified platform.
  • Execution cost limiting and tracking.
• Streamlined onboarding of new quantum backends through a new integration protocol.
• State-vector filtering logic for scalable state-vector simulation.
• Stable, GPU-based simulators, including configurations optimized for hybrid (QML-focused) workflows.
• Deployment of simulators on Cineca’s HPC infrastructure.
• Improved post-processing using pandas DataFrames.

Studio

The Classiq Studio continues to evolve as a dedicated environment for quantum development. It includes built-in visualization, debugging tools, and AI assistance for model generation, optimization, and execution. Workspace loading, environment management, version control, and memory and CPU usage were improved. Your work is automatically saved, dependencies are managed, and you can run all required tasks from a single environment.

AI assistant tool

Classiq 1.0 includes two paths to AI-assisted quantum development:

• The Classiq AI code assistant, integrated directly into Classiq Studio, provides in-context help for quantum program creation without additional setup.
• An initial prompt for local workflows, enabling local AI agents to use Classiq context when generating quantum code.

Visualization

Classiq provides model-based visualization of quantum programs, representing synthesized programs in terms that reflect both their high-level algorithmic intent and their low-level implementation, bridging abstraction layers intuitively.

• Quantum variable lifetimes, quantum expressions, and controlled operations are presented graphically, supporting reasoning about correctness, resource usage, and functionality.
• Hierarchical interactive navigation enables easy exploration across abstraction layers, mapping gate/qubit-level implementation decisions, including those of the synthesis engine, back to higher-level model elements.
• Visualization of synthesized programs is available via the Python SDK, IDE, and Studio, and can be shared for debugging, reviews, and collaboration.

General improvements

• Added profile and organization settings to improve user and admin control.
• Documentation improvements:
  • New Classiq Tutorial.
  • New Support page and FAQs.
  • Improved documentation search ranking.
• IDE usability improvements:
  • Improved appearance of result histograms and bar plots in the platform result page.
  • Improved native Qmod library usability.
  • Enhanced the formatting of labels for slice operations in QP Visualization.
• Classiq Studio:
  • Added light-mode support for the Quantum Program (QP) visualizer. Useful for publications and articles.
• C12 carbon-based QPU emulators are now available - see the Cloud Providers section in the user guide.

Classiq 0.105.0

Released on 2026-01-19.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Bug Fixes

• Fixed error message for a measurement under unitary context.

Classiq 0.103.0

Released on 2026-01-05.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Bug Fixes

• IDE - Fix expand / collapse of backend details in the HW Catalogue
• In the dataframe, change the count field to counts so as not to override the built-in count method on pandas dataframes.
  The current count field is deprecated and will be removed in the next release.
• Fix multi-value Boolean operations in Native Qmod (e.g., a or b or c).
• Fix synthesis error when controlling X with >=13 control qubits.
• Raise indicative errors when overriding internal functions (for example, when re-defining prepare_state).

Enhancements

• Support QArray[QBit] assignment statements, for example, qarr |= [1, 0, 0, 1].
• Improve implementation of in-place quantum subscript assignments (e.g., x ^= subscript([1, 2, 3, 4], y)).
• IDE - Execution in the IDE now supports running through Classiq account for Amazon Braket, Microsoft Azure Quantum, and IonQ backends. Users can enable this option using the "Run through Classiq" switch, which eliminates the need to provide their own credentials for these backends. This feature also includes spending tracking and budget management capabilities.
• Support assignments of non-scalar variables, for example, qarr1 |= qarr2.
  Both variables must have the same type.
• Improve error message when assigning a symbolic value to a generative parameter.

Deprecation

• IDE - Removed support for max depth and max gate count synthesis constraints in the model

Classiq 0.102.0

Released on 2025-12-21.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Interface Changes

• Violations of uncomputation rules are now flagged as errors instead of warnings.
• The function qubit_op_to_pauli_terms in the chemistry module is
  deprecated due to incorrect order of qubits in its result. Please use
  qubit_op_to_qmod instead.

Bug Fixes

• Resolved an issue with labeling CX gate inputs in QP visualization, so that the correct qubit labels are now displayed.
• Fixed qfuncs being expanded declaratively (symbolically) when passed as arguments to other qfuncs.
• Improved the error message for type errors with function arrays.
• Fixed error message for nan values.
• Fix mapping from output registers to measured qubits on parametrized repeat circuits with HW aware synthesis.
• Fix phase applied with an execution parameter under multiple controls.
• Fix unscheduled_suzuki_trotter and qdrift implementations.
• Fix SDK function get_execution_actions
  • Flattened the cost field. The result holds now cost, currency_code
  • Add session_id in the result
• Fix compilation of unused variables defined in nested blocks.

Studio

• Classiq Studio now includes built-in AI integration, allowing users to generate, optimize, and execute quantum models directly within the Studio. This capability leverages Classiq's quantum resources and requires no external API tokens.

Classiq 0.101.0

Released on 2025-12-07.

Upgrade Instructions

• Python SDK
• The IDE upgrades automatically.

Enhancements

• Add quantum modular arithmetic functions to the open library, enabling modular quantum operations:
  modular addition, multiplication, squaring, negation, and inversion: modular_add_inplace,
  modular_double_inplace, modular_negate_inplace, modular_multiply, modular_square,
  modular_multiply_constant, modular_multiply_constant_inplace, modular_to_montgomery_inplace,
  modular_montgomery_to_standard_inplace, modular_inverse_inplace, kaliski_iteration, and
  modular_rsub_inplace.
• Change precision of GPU simulators to single-precision to make better use of hardware.
• Add functions get_execution_actions and get_execution_actions_async return a pandas DataFrame of execution actions. Filter by id, session_id, status, name, provider, backend, program_id, cost range (total_cost_min/max), and time ranges (start_time_min/max, end_time_min/max). All filters are combined with AND logic.
• Add functions get_synthesis_actions and get_synthesis_actions_async return a pandas DataFrame of synthesis actions. Filter by id, status, backend, program_id, backend_name, optimization_parameter, random_seed, max_width, max_gate_count, cost range (total_cost_min/max), and time ranges (start_time_min/max, end_time_min/max). All filters are combined with AND logic.

Bug Fixes

• Fix uncomputation of function calls with input or output concatenations.
• Fix within-apply bug caused by variable declarations nested in the "within"
  block.