IBM expands Qiskit compiler foundation in v2.6, intensifying competition with Classiq

【Article Title】 IBM expands Qiskit compiler foundation in v2.6, intensifying competition with Classiq 【Article Body】 AI TRANSLATION This article is an AI-generated translation of the original Japanese article. For the authoritative version, please refer to the original. Read the original Japanese article → (https://quantumindex.jp/qiskit-sdk-2-6-c-api-release/) On October 22, 2026, IBM published Qiskit SDK v2.6, an update centered on expanding the C API. Users can now build custom compiler pipelines from compiled languages, and QPY circuit serialization and custom quantum operations can be handled without going through Python. ✍️ Quantum Index Analysis The analysis explains the technical and business implications behind the announcement and highlights evaluation points that numbers and headlines may not reveal. Read our analysis ↓ Summary of the release Qiskit SDK v2.6 adds a new pass manager, QkPassManager, to the C API. Users can create custom compiler paths from compiled languages such as C, building pipelines that transform multiple intermediate representations in stages. Because each pass declares the types of intermediate representations it consumes and produces, pipeline consistency can be validated. The binary format for saving and exchanging quantum circuits, QPY, is also supported via the C API. Circuits can be saved to and restored from files or memory buffers from C, C++, Julia, Fortran, and other languages. The generated data is compatible with the Python-side qiskit.qpy, enabling circuit exchange across languages and execution environments. The default QPY format has been updated to v18. While this reduces file size for circuits with large registers or many classical registers, QPY files produced by v2.6 with the default settings may not be readable by older Qiskit versions. If backward compatibility is required, an older format must be specified when saving. Additionally, using QkCustomOp allows defining custom quantum operations—including hardware-specific gates—via the C API. Work has progressed to run standalone C libraries in environments without Python installed, but v2.6 does not yet achieve complete Python independence. The minimum supported Python remains 3.10, but v2.6 will be the last release to support that version. From v2.7 onward, Python 3.11 or later will be required, and building from source will require Rust 1.89. The C API has not been declared stable, but there were no breaking changes since v2.5; the changes in this release are additive. Key points • Custom compiler paths and a pass manager can be built from the C API, allowing staged transformations across multiple intermediate representations with type checking • QPY is supported by the C API, enabling circuit save/restore and language-to-language exchange without Python • Custom quantum operations can be defined from the C API, allowing hardware-specific gates to be integrated into circuits • QPY v18 becomes the default, reducing saved size but requiring attention for compatibility with older Qiskit versions • Standalone use without Python remains incomplete, and the C API is not declared stable Technical and business implications Technically, the important aspect is that Qiskit’s Rust-based compiler capabilities are now easier to embed into non-Python languages and HPC environments. With support for multi-stage compilation using multiple intermediate representations, cross-language circuit exchange, and hardware-specific operation definitions available on the C API, the software foundation for quantum-centric supercomputing and future fault-tolerant quantum computingfault-tolerant quantum computingFTQC / Fault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important. has been strengthened. From a business perspective, the update broadens choices for programming languages and runtime environments. However, standalone operation without Python is not yet complete, and the announcement does not include adoption case studies or concrete business outcomes. What to watch next The next focus will be whether v2.7 reaches a state where standalone C libraries can be used in environments without Python. Also important will be declarations of C API stability and compatibility policies, availability of language bindings for C++ and Julia, and concrete usage examples in HPC environments. For QPY v18, it will be important to see how much cross-language compatibility and storage reduction for large circuits deliver in real operational settings. ✍️ Quantum Index Analysis Qiskit v2.6 can be seen as an update that further strengthens IBM’s position as the common software foundation for quantum computing. If previous expansions to Fortran, C++, and Julia were about creating an “entry” for external programs to use Qiskit, this release is an “internal” expansion that allows custom intermediate representations and compiler paths to be embedded. IBM appears to be evolving Qiskit from a quantum-circuit development SDK into a compiler foundation that connects diverse languages and hardware. This move will also affect competition with commercial quantum software companies like Classiq. Classiq’s strength is automatic circuit synthesis from high-level algorithm descriptions, and Qiskit’s additions do not immediately replace that capability. However, enabling custom compiler pipelines on Qiskit broadens options for building development environments without relying on commercial platforms like Classiq. We can outline the competition areas between the two as follows. Competition areaIBM QiskitClassiqLow-level quantum circuitquantum circuit量子回路 / Quantum CircuitA representation of the computation procedure executed on a quantum computer, listing in order operations such as qubit initialization, quantum gate operations, and measurements.QI NoteEven for the same algorithm, the number of qubits used, circuit depth, and number of gates vary depending on the implementation. When comparing real-device performance, also check the circuit scale and the conditions after compilation. optimizationCore capabilitySupportedHigh-level automatic circuit synthesisRoom for feature expansionCore technologyCompiler infrastructure extensibilityBeing strengthened via the C APIDeploys proprietary compilerSupport for fault-tolerant quantum computingfault-tolerant quantum computingFTQC / Fault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important.Developing execution and compilation infrastructureProvides a Fault Tolerance EngineBusiness modelOpen-source-centered ecosystemCommercial development platform Classiq also announced a new Qmod compiler architecture in September 2026 and released a Fault Tolerance Engine (https://en.quantumindex.jp/classiq-fault-tolerance-engine/) at the end of that month to help evaluate programs considering error correction, among other advances. However, before the quantum software market fully matures, there is a real possibility that compiler infrastructure will be commoditized through open source. IBM does not necessarily need to monetize Qiskit directly, whereas Classiq must continually demonstrate why customers should pay for its commercial product. This structural asymmetry is significant. That said, the C API is not yet declared stable and Python dependency has not been completely eliminated. Whether IBM’s vision spreads in practice will depend on integration results with third-party hardware and HPC environments. In the quantum software market, competition will increasingly hinge not just on which compiler features are better, but on who sets the development environment standard and which components can be differentiated as commercial products. NETWORKSee the industry network around IBM Quantum → (/qi-network/) Related articles • IBM expands Qiskit to Fortran, C++, and Julia as it prepares C API for quantum–HPC integration (https://en.quantumindex.jp/qiskit-fortran-cpp-julia-c-api/) • IBM opens up error mitigation from a “black box,” unifying QEC via directed execution (https://en.quantumindex.jp/ibm-quantum-directed-execution-qec/) • Q-CTRL’s Fire Opal now supports IBM Nighthawk R2 — runs 60-qubit circuits in 23 seconds (https://en.quantumindex.jp/qctrl-fire-opal-ibm-quantum-nighthawk/) Source Read the source article (https://www.ibm.com/quantum/blog/qiskit-2-6-release-summary) If you found this article useful, please consider sharing it. ──────────────────── 【AI Evaluation Request】 Evaluate the following article critically, not based on the quality of its writing, but on its **original informational value as a specialist publication**. 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On October 22, 2026, IBM published Qiskit SDK v2.6, an update centered on expanding the C API. Users can now build custom compiler pipelines from compiled languages, and QPY circuit serialization and custom quantum operations can be handled without going through Python.

Summary of the release

Qiskit SDK v2.6 adds a new pass manager, QkPassManager, to the C API. Users can create custom compiler paths from compiled languages such as C, building pipelines that transform multiple intermediate representations in stages. Because each pass declares the types of intermediate representations it consumes and produces, pipeline consistency can be validated. The binary format for saving and exchanging quantum circuits, QPY, is also supported via the C API. Circuits can be saved to and restored from files or memory buffers from C, C++, Julia, Fortran, and other languages. The generated data is compatible with the Python-side qiskit.qpy, enabling circuit exchange across languages and execution environments. The default QPY format has been updated to v18. While this reduces file size for circuits with large registers or many classical registers, QPY files produced by v2.6 with the default settings may not be readable by older Qiskit versions. If backward compatibility is required, an older format must be specified when saving. Additionally, using QkCustomOp allows defining custom quantum operations—including hardware-specific gates—via the C API. Work has progressed to run standalone C libraries in environments without Python installed, but v2.6 does not yet achieve complete Python independence. The minimum supported Python remains 3.10, but v2.6 will be the last release to support that version. From v2.7 onward, Python 3.11 or later will be required, and building from source will require Rust 1.89. The C API has not been declared stable, but there were no breaking changes since v2.5; the changes in this release are additive.

Key points

• Custom compiler paths and a pass manager can be built from the C API, allowing staged transformations across multiple intermediate representations with type checking
• QPY is supported by the C API, enabling circuit save/restore and language-to-language exchange without Python
• Custom quantum operations can be defined from the C API, allowing hardware-specific gates to be integrated into circuits
• QPY v18 becomes the default, reducing saved size but requiring attention for compatibility with older Qiskit versions
• Standalone use without Python remains incomplete, and the C API is not declared stable

Technical and business implications

Technically, the important aspect is that Qiskit’s Rust-based compiler capabilities are now easier to embed into non-Python languages and HPC environments. With support for multi-stage compilation using multiple intermediate representations, cross-language circuit exchange, and hardware-specific operation definitions available on the C API, the software foundation for quantum-centric supercomputing and future fault-tolerant quantum computingfault-tolerant quantum computingFTQC / Fault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important. has been strengthened.

From a business perspective, the update broadens choices for programming languages and runtime environments. However, standalone operation without Python is not yet complete, and the announcement does not include adoption case studies or concrete business outcomes.

What to watch next

The next focus will be whether v2.7 reaches a state where standalone C libraries can be used in environments without Python. Also important will be declarations of C API stability and compatibility policies, availability of language bindings for C++ and Julia, and concrete usage examples in HPC environments. For QPY v18, it will be important to see how much cross-language compatibility and storage reduction for large circuits deliver in real operational settings.

✍️ Quantum Index Analysis

Qiskit v2.6 can be seen as an update that further strengthens IBM’s position as the common software foundation for quantum computing. If previous expansions to Fortran, C++, and Julia were about creating an “entry” for external programs to use Qiskit, this release is an “internal” expansion that allows custom intermediate representations and compiler paths to be embedded. IBM appears to be evolving Qiskit from a quantum-circuit development SDK into a compiler foundation that connects diverse languages and hardware.

This move will also affect competition with commercial quantum software companies like Classiq. Classiq’s strength is automatic circuit synthesis from high-level algorithm descriptions, and Qiskit’s additions do not immediately replace that capability. However, enabling custom compiler pipelines on Qiskit broadens options for building development environments without relying on commercial platforms like Classiq.

We can outline the competition areas between the two as follows.

Competition area	IBM Qiskit	Classiq
Low-level quantum circuitquantum circuit量子回路 / Quantum CircuitA representation of the computation procedure executed on a quantum computer, listing in order operations such as qubit initialization, quantum gate operations, and measurements.QI NoteEven for the same algorithm, the number of qubits used, circuit depth, and number of gates vary depending on the implementation. When comparing real-device performance, also check the circuit scale and the conditions after compilation. optimization	Core capability	Supported
High-level automatic circuit synthesis	Room for feature expansion	Core technology
Compiler infrastructure extensibility	Being strengthened via the C API	Deploys proprietary compiler
Support for fault-tolerant quantum computingfault-tolerant quantum computingFTQC / Fault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important.	Developing execution and compilation infrastructure	Provides a Fault Tolerance Engine
Business model	Open-source-centered ecosystem	Commercial development platform

Classiq also announced a new Qmod compiler architecture in September 2026 and released a Fault Tolerance Engine at the end of that month to help evaluate programs considering error correction, among other advances. However, before the quantum software market fully matures, there is a real possibility that compiler infrastructure will be commoditized through open source. IBM does not necessarily need to monetize Qiskit directly, whereas Classiq must continually demonstrate why customers should pay for its commercial product. This structural asymmetry is significant.

That said, the C API is not yet declared stable and Python dependency has not been completely eliminated. Whether IBM’s vision spreads in practice will depend on integration results with third-party hardware and HPC environments. In the quantum software market, competition will increasingly hinge not just on which compiler features are better, but on who sets the development environment standard and which components can be differentiated as commercial products.

NETWORKSee the industry network around IBM Quantum →

Related articles

• IBM expands Qiskit to Fortran, C++, and Julia as it prepares C API for quantum–HPC integration
• IBM opens up error mitigation from a “black box,” unifying QEC via directed execution
• Q-CTRL’s Fire Opal now supports IBM Nighthawk R2 — runs 60-qubit circuits in 23 seconds

Source

Read the source article