JIJ releases Qamomile v0.14.0 with resource estimation and multi‑SDK conversion

【Article Title】 JIJ releases Qamomile v0.14.0 with resource estimation and multi‑SDK conversion 【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/) On July 27, 2026, JIJ published v0.14.0 of Qamomile, a quantum programming language that can be written in Python, as open source. In addition to type-safe program authoring, the release enables algebraic estimation of qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important. counts and gate counts, and supports conversion and execution to multiple quantum SDKs and intermediate representations from the same program. 発表概要 Qamomile began as a library for quantum optimization and, with this update, has been expanded into a general-purpose programming foundation capable of handling a wide range of quantum algorithms. The language can algebraically compute the number of qubits and gates required by a quantum program, allowing users to estimate how resource needs grow with problem size. Target backends include CUDA-Q and Qiskit, and the release also supports QURI Parts, HUGR, qBraid, and Quration. The design lets the same code be used for resource estimation, simulation, and execution on devices. A type system has been introduced to detect invalid operations in quantum programming early and to suggest fixes. Developers can write conditional branching and control based on 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. measurement outcomes using Python’s if and while statements. Built-in functions for quantum algorithms such as QSVT (quantum singular value transformation) are also available. 重要ポイント • Qamomile v0.14.0 has been available as an open-source library since July 27, 2026. • The same quantum program can be used to estimate qubit and gate counts and to convert to multiple SDKs and intermediate representations. • Conversion targets include CUDA-Q, Qiskit, QURI Parts, HUGR, qBraid, and Quration. • The release adopts a type system and Python-native control constructs to enable early detection of misuse and to support conditional branching based on measurement outcomes. • Quantum algorithms such as QSVT are provided as built-in functions. 技術・事業上の意味 Technically, the significance lies in not having to manage separate circuits for estimation and execution: a single program can drive the workflow from resource evaluation to execution. Conversion to multiple SDKs reduces dependence on a specific development environment and could bridge development on current quantum hardware with evaluations that assume fault-tolerant quantum computationfault-tolerant quantum computationFTQC / 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.. The announcement does not specify adopting companies, scale of use, performance comparisons with other platforms, or the specific quantum devices supported. 今後の注目点 Going forward, it will be important to see how consistently programs converted to each SDK run on actual devices and simulators. The publication of concrete examples of resource estimation, comparisons with other methods, and the range of supported devices will also be important evaluation criteria. In addition, real-world algorithm development and corporate use cases would make it easier to judge how much the update reduces development burden. ✍️ Quantum Index Analysis The notable aspect of this update is less the individual features than the change in Qamomile’s positioning. Originally an optimization library, Qamomile has expanded in v0.14.0 into a general-purpose quantum programming foundation, enabling resource estimation, a type system, and conversion to multiple SDKs and intermediate representations from a single Python codebase. However, this direction itself is not novel: a competitive space for quantum software development platforms is already taking shape. Platforms such as Classiq already provide high-level DSLs, resource estimation, and multi-backend support, and this announcement alone does not reveal a technical advantage unique to Qamomile. The release also does not claim new superiority in core technologies that determine platform competitiveness, such as 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. synthesis or compiler technology. Therefore, this release should be seen not as evidence of improved quantum compute performance but as an update declaring Qamomile’s evolution from a quantum optimization library to a general-purpose quantum software development platform. Going forward, the differentiation will depend on whether Qamomile can build sufficient development experience, compiler technologies, backend support, and enterprise adoption to be chosen in production environments. 関連記事 • Ranking of 29 quantum companies by capability and future potential (Summer 2026) (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 as a standard machine (https://en.quantumindex.jp/fixstars-amplify-adds-ionq-simulator/) • Strangeworks announces enterprise optimization system “Aura” (https://en.quantumindex.jp/strangeworks-aura-enterprise-optimization/) 出典 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**. Do not give a high rating to summaries, paraphrases, explanations of technical terms, or generic observations such as "this will be worth watching" or "the timeline remains uncertain" if they could easily be produced by a general-purpose generative AI, even if they are well written. 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On July 27, 2026, JIJ published v0.14.0 of Qamomile, a quantum programming language that can be written in Python, as open source. In addition to type-safe program authoring, the release enables algebraic estimation of qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important. counts and gate counts, and supports conversion and execution to multiple quantum SDKs and intermediate representations from the same program.

発表概要

Qamomile began as a library for quantum optimization and, with this update, has been expanded into a general-purpose programming foundation capable of handling a wide range of quantum algorithms. The language can algebraically compute the number of qubits and gates required by a quantum program, allowing users to estimate how resource needs grow with problem size. Target backends include CUDA-Q and Qiskit, and the release also supports QURI Parts, HUGR, qBraid, and Quration. The design lets the same code be used for resource estimation, simulation, and execution on devices.

A type system has been introduced to detect invalid operations in quantum programming early and to suggest fixes. Developers can write conditional branching and control based on 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. measurement outcomes using Python’s if and while statements. Built-in functions for quantum algorithms such as QSVT (quantum singular value transformation) are also available.

重要ポイント

• Qamomile v0.14.0 has been available as an open-source library since July 27, 2026.
• The same quantum program can be used to estimate qubit and gate counts and to convert to multiple SDKs and intermediate representations.
• Conversion targets include CUDA-Q, Qiskit, QURI Parts, HUGR, qBraid, and Quration.
• The release adopts a type system and Python-native control constructs to enable early detection of misuse and to support conditional branching based on measurement outcomes.
• Quantum algorithms such as QSVT are provided as built-in functions.

技術・事業上の意味

Technically, the significance lies in not having to manage separate circuits for estimation and execution: a single program can drive the workflow from resource evaluation to execution. Conversion to multiple SDKs reduces dependence on a specific development environment and could bridge development on current quantum hardware with evaluations that assume fault-tolerant quantum computationfault-tolerant quantum computationFTQC / 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.. The announcement does not specify adopting companies, scale of use, performance comparisons with other platforms, or the specific quantum devices supported.

今後の注目点

Going forward, it will be important to see how consistently programs converted to each SDK run on actual devices and simulators. The publication of concrete examples of resource estimation, comparisons with other methods, and the range of supported devices will also be important evaluation criteria. In addition, real-world algorithm development and corporate use cases would make it easier to judge how much the update reduces development burden.

✍️ Quantum Index Analysis

The notable aspect of this update is less the individual features than the change in Qamomile’s positioning. Originally an optimization library, Qamomile has expanded in v0.14.0 into a general-purpose quantum programming foundation, enabling resource estimation, a type system, and conversion to multiple SDKs and intermediate representations from a single Python codebase.

However, this direction itself is not novel: a competitive space for quantum software development platforms is already taking shape. Platforms such as Classiq already provide high-level DSLs, resource estimation, and multi-backend support, and this announcement alone does not reveal a technical advantage unique to Qamomile. The release also does not claim new superiority in core technologies that determine platform competitiveness, such as 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. synthesis or compiler technology.

Therefore, this release should be seen not as evidence of improved quantum compute performance but as an update declaring Qamomile’s evolution from a quantum optimization library to a general-purpose quantum software development platform. Going forward, the differentiation will depend on whether Qamomile can build sufficient development experience, compiler technologies, backend support, and enterprise adoption to be chosen in production environments.

関連記事

• Ranking of 29 quantum companies by capability and future potential (Summer 2026)
• Fixstars Amplify adds IonQ quantum simulator as a standard machine
• Strangeworks announces enterprise optimization system “Aura”

出典

Read the original announcement