JIJ releases quantum programming language “Qamomile” v0.14.0 — supports resource estimation and conversion to multiple SDKs

【Article Title】 JIJ releases quantum programming language “Qamomile” v0.14.0 — supports resource estimation and conversion to multiple SDKs 【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/jij-qamomile-v0-14-0-quantum-programming-language/) JIJ announced on July 27, 2026 that it has open-sourced the Python-based quantum programming language “Qamomile” v0.14.0. In addition to enabling type-safe program descriptions, it can algebraically estimate the number of qubits and gates and convert and execute the same program across multiple quantum SDKs and intermediate representations. Announcement overview Qamomile began as a library for quantum optimization, and with this update it has been expanded into a general-purpose programming platform capable of handling a wide range of quantum algorithms. The system can algebraically compute the number of qubits and quantum gates required from a quantum program, allowing one to estimate how resources scale with problem size. Conversion targets include CUDA-Q and Qiskit, and it also supports QURI Parts, HUGR, qBraid, and Quration. The design lets the same code be used for resource estimation, simulation, and execution on real hardware. A type system has been introduced to detect disallowed operations in quantum programming early and to suggest fixes. Using Python if and while statements, users can also write conditional branching and control constructs based on measurement results from quantum circuits. Furthermore, quantum algorithms such as QSVT (quantum singular value transformation) are provided as built-in functions. Key points • Qamomile v0.14.0 has been made available as an open-source library from July 27, 2026 • From the same quantum program, the number of qubits and gates can be estimated and the program can be converted to multiple SDKs and intermediate representations • Supports conversion to CUDA-Q, Qiskit, QURI Parts, HUGR, qBraid, and Quration • Adopts a type system and Python-native control structures, enabling early detection of misuse and conditional branching based on measurement results • Provides quantum algorithms such as QSVT as built-in functions Technical and business significance Technically, it is significant that estimation and execution circuits do not have to be managed separately; instead, a common program can drive the workflow from resource evaluation to execution. Conversion to multiple SDKs reduces dependence on any single development environment and can serve as a bridge between development using current quantum hardware and evaluations that assume fault-tolerant quantum computing. However, this announcement does not specify adopting companies, scale of deployment, performance comparisons with other development platforms, or the specific names of supported quantum hardware. What to watch next Going forward, it will be important to see how consistently programs converted to each SDK run on real devices and simulators. Publication of concrete examples of resource estimation, comparisons with other approaches, and the range of supported hardware will be useful for assessment. In addition, if concrete cases of algorithm development or corporate adoption are shown, it will be easier to evaluate how much development overhead can be reduced in practice. ✍️ Quantum Index Analysis What is notable about this update is less the individual new features and more the change in Qamomile’s positioning. Qamomile, which started as a quantum optimization library, has expanded its scope in v0.14.0 into a general-purpose quantum programming platform, enabling resource estimation, a type system, and conversion to multiple SDKs and intermediate representations from a single Python codebase. That said, this direction is not novel: the competitive space for quantum software development platforms is already forming. Platforms such as Classiq already offer high-level DSLs, resource estimation, and multi-backend support, and this announcement alone does not reveal a technical advantage that only Qamomile can deliver. Nor does the announcement demonstrate new superiority in core technologies that determine a development platform’s competitiveness, such as quantum circuit synthesis or compiler technologies. Therefore, this release should be viewed not as evidence of improved quantum computational performance but as a declaration that Qamomile has evolved from a quantum optimization library into a general-purpose quantum software development platform. Going forward, the divide in evaluations will hinge on how much practical development experience, compiler technology, backend support, and corporate adoption the project can accumulate. Related articles • Ranking of the capabilities and prospects of 29 quantum companies [Summer 2026 edition] (https://en.quantumindex.jp/%e9%87%8f%e5%ad%90%e4%bc%81%e6%a5%ad29%e7%a4%be%e3%81%ae%e5%ae%9f%e5%8a%9b%e3%81%a8%e5%b0%86%e6%9d%a5%e6%80%a7%e3%83%a9%e3%83%b3%e3%82%ad%e3%83%b3%e3%82%b0%e3%80%902026%e5%b9%b4%e5%a4%8f%e7%89%88/) • Fixstars Amplify adds IonQ quantum simulator to its standard machines (https://en.quantumindex.jp/fixstars-amplify-adds-ionq-simulator/) • Strangeworks announces enterprise optimization system “Aura” (https://en.quantumindex.jp/strangeworks-aura-enterprise-optimization/) Source Read the original announcement (https://www.j-ij.com/ja/news/20260803) 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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JIJ announced on July 27, 2026 that it has open-sourced the Python-based quantum programming language “Qamomile” v0.14.0. In addition to enabling type-safe program descriptions, it can algebraically estimate the number of qubits and gates and convert and execute the same program across multiple quantum SDKs and intermediate representations.

Announcement overview

Qamomile began as a library for quantum optimization, and with this update it has been expanded into a general-purpose programming platform capable of handling a wide range of quantum algorithms. The system can algebraically compute the number of qubits and quantum gates required from a quantum program, allowing one to estimate how resources scale with problem size. Conversion targets include CUDA-Q and Qiskit, and it also supports QURI Parts, HUGR, qBraid, and Quration. The design lets the same code be used for resource estimation, simulation, and execution on real hardware. A type system has been introduced to detect disallowed operations in quantum programming early and to suggest fixes. Using Python if and while statements, users can also write conditional branching and control constructs based on measurement results from quantum circuits. Furthermore, quantum algorithms such as QSVT (quantum singular value transformation) are provided as built-in functions.

Key points

• Qamomile v0.14.0 has been made available as an open-source library from July 27, 2026
• From the same quantum program, the number of qubits and gates can be estimated and the program can be converted to multiple SDKs and intermediate representations
• Supports conversion to CUDA-Q, Qiskit, QURI Parts, HUGR, qBraid, and Quration
• Adopts a type system and Python-native control structures, enabling early detection of misuse and conditional branching based on measurement results
• Provides quantum algorithms such as QSVT as built-in functions

Technical and business significance

Technically, it is significant that estimation and execution circuits do not have to be managed separately; instead, a common program can drive the workflow from resource evaluation to execution. Conversion to multiple SDKs reduces dependence on any single development environment and can serve as a bridge between development using current quantum hardware and evaluations that assume fault-tolerant quantum computing. However, this announcement does not specify adopting companies, scale of deployment, performance comparisons with other development platforms, or the specific names of supported quantum hardware.

What to watch next

Going forward, it will be important to see how consistently programs converted to each SDK run on real devices and simulators. Publication of concrete examples of resource estimation, comparisons with other approaches, and the range of supported hardware will be useful for assessment. In addition, if concrete cases of algorithm development or corporate adoption are shown, it will be easier to evaluate how much development overhead can be reduced in practice.

✍️ Quantum Index Analysis

What is notable about this update is less the individual new features and more the change in Qamomile’s positioning. Qamomile, which started as a quantum optimization library, has expanded its scope in v0.14.0 into a general-purpose quantum programming platform, enabling resource estimation, a type system, and conversion to multiple SDKs and intermediate representations from a single Python codebase.

That said, this direction is not novel: the competitive space for quantum software development platforms is already forming. Platforms such as Classiq already offer high-level DSLs, resource estimation, and multi-backend support, and this announcement alone does not reveal a technical advantage that only Qamomile can deliver. Nor does the announcement demonstrate new superiority in core technologies that determine a development platform’s competitiveness, such as quantum circuit synthesis or compiler technologies.

Therefore, this release should be viewed not as evidence of improved quantum computational performance but as a declaration that Qamomile has evolved from a quantum optimization library into a general-purpose quantum software development platform. Going forward, the divide in evaluations will hinge on how much practical development experience, compiler technology, backend support, and corporate adoption the project can accumulate.

Related articles

• Ranking of the capabilities and prospects of 29 quantum companies [Summer 2026 edition]
• Fixstars Amplify adds IonQ quantum simulator to its standard machines
• Strangeworks announces enterprise optimization system “Aura”

Source

Read the original announcement