Fixstars Amplify SDK v1.7 speeds formulation up to 30× and cuts memory use up to 80%

【Article Title】 Fixstars Amplify SDK v1.7 speeds formulation up to 30× and cuts memory use up to 80% 【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/fixstars-amplify-sdk-1-7-release/) Fixstars Amplify has released version 1.7 of its unified optimization development environment, Fixstars Amplify SDK. By overhauling the internal architecture, the company says it has reduced runtime memory consumption by up to 80% and sped up formulation by as much as 30× versus the previous version. ✍️ Quantum Index Analysis The following explains the technical and business significance behind the announcement and highlights evaluation points that aren’t obvious from the numbers or headlines alone. Read our independent analysis ↓ Announcement summary Version 1.7 implements a fundamental redesign of the software’s internal architecture. In comparisons using the traveling salesman problem, a representative benchmark for logistics and routing, the company reports performance advantages over other major optimization tools. However, the names of the comparator tools and the measurement conditions were not disclosed in the announcement. Fixstars says these improvements will make it easier to develop applications that recompute large, complex optimization tasks in response to changing conditions. The reduction in memory usage also opens possibilities for deployment not only on high-performance servers but in resource-constrained on-premises environments and on edge devices. Key points • Released Fixstars Amplify SDK version 1.7 • Claims up to 80% reduction in runtime memory consumption • Claims formulation up to 30× faster than the previous version • Reported superior performance to other major optimization tools in traveling salesman problem comparisons • Envisions large-scale computation on on-premises systems and edge devices Technical and business implications Faster formulation can reduce the wait time incurred each time an optimization problem must be reassembled when input conditions change. Reduced memory usage also supports deployment in environments with limited computing resources. From a business perspective, this update could expand applicability where real-time responsiveness or operational constraints have been barriers to adoption—such as logistics and routing. That said, actual effects will vary with problem size, structure, and execution environment, so evaluation including the undisclosed comparison conditions is necessary. What to watch next Going forward, key questions include whether the conditions under which the company measured up to 30× speedup and up to 80% memory reduction—such as problem sizes, execution environments, and comparator tools—will be published. It will also be important to verify whether similar improvements appear for practical optimization problems beyond the traveling salesman problem, and whether concrete deployment examples on on-premises systems or edge devices are demonstrated. The specifics of integration with quantum hardware and the actual reductions in recomputation time and resource usage observed by user organizations are also important observational points. ✍️ Quantum Index Analysis This announcement concerns speed and memory reductions in the SDK-side preprocessing responsible for formulating optimization problems and generating data to pass to solvers. It does not increase the computation speed of the solvers themselves—whether cloud gate-model quantum computers, quantum annealers, or quantum-inspiredquantum-inspiredQuantum-Inspired Technology / Quantum-Inspired Computing / Quantum-Inspired Technology / Quantum-Inspired ComputingA computing technology that, inspired by concepts and mathematical methods from quantum computing, primarily runs on classical computers. It is widely used especially in the field of combinatorial optimization.QI NoteEven though "quantum" appears in the name, it does not necessarily use quantum hardware. In announcements or publications, verify the actual computing platform and whether an advantage over classical methods has been demonstrated. In particular in the optimization field, there are solvers that already demonstrate practically useful, high-level performance. solvers. However, for large-scale problems this preprocessing can be nontrivial. As bit counts and constraints grow, formulation and request-data generation require more time and memory, so if the claimed “up to 30× faster” and “up to 80% reduction” are reproducible under real-world conditions, they could lower application-wide latency and resource requirements, including the time until a solver begins computation. Fixstars Amplify had over 1,000 registered organizations and more than 100 million cumulative solves of Amplify AE as of December 2025 (https://amplify.fixstars.com/ja/news/1128/). In May 2026, the SDK added support for “Amplify Quantum,” enabling access to gate-model quantum computers such as IBM Quantum, IonQ, Rigetti, and IQM from the SDK (https://en.quantumindex.jp/fixstars-amplify-adds-ionq-simulator/). In addition to previously supported D-Wave quantum annealers and quantum-inspired machines from Toshiba and Fujitsu, Fixstars has continued expanding supported backends, including OQTOPUS Cloud and AIST’s “System F” . (https://en.quantumindex.jp/fixstars-amplify-sdk-system-f-support/) In short, the Fixstars Amplify SDK is broadening its scope to handle heterogeneous compute resources—from quantum annealers and quantum-inspired solvers to gate-model quantum computers—through a common development environment. Alongside the steady addition of accessible quantum computers and solvers from the SDK, the team is also investing in performance improvements to the SDK itself, which users interact with directly. As usage reaches a certain scale, Fixstars appears to be entering a stage of addressing formulation and data-generation overheads that become apparent in production. The reported performance results were obtained on a traveling salesman problem of roughly 100,000 bits (317 cities); verification is needed to determine whether similar gains appear for different problem structures and scales. Looking ahead, an important indicator of whether Amplify will become a common optimization platform is not just the number of supported solvers but the extent to which users adopt and switch among multiple compute backends from the same SDK. NETWORKSee the industry network surrounding Fixstars Amplify → (/qi-network/) Related articles • Fixstars Amplify SDK adds support for Fujitsu’s superconducting quantum computer “System F” (https://en.quantumindex.jp/fixstars-amplify-sdk-system-f-support/) • Fixstars Amplify adds IonQ quantum simulator as a standard machine (https://en.quantumindex.jp/fixstars-amplify-adds-ionq-simulator/) • Fixstars Amplify releases GPU annealing solver Amplify AE 1.3 (https://en.quantumindex.jp/fixstars-amplify-annealing-engine-1-3/) Source Read the original announcement (https://amplify.fixstars.com/ja/news/1843/) 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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Fixstars Amplify has released version 1.7 of its unified optimization development environment, Fixstars Amplify SDK. By overhauling the internal architecture, the company says it has reduced runtime memory consumption by up to 80% and sped up formulation by as much as 30× versus the previous version.

Announcement summary

Version 1.7 implements a fundamental redesign of the software’s internal architecture. In comparisons using the traveling salesman problem, a representative benchmark for logistics and routing, the company reports performance advantages over other major optimization tools. However, the names of the comparator tools and the measurement conditions were not disclosed in the announcement. Fixstars says these improvements will make it easier to develop applications that recompute large, complex optimization tasks in response to changing conditions. The reduction in memory usage also opens possibilities for deployment not only on high-performance servers but in resource-constrained on-premises environments and on edge devices.

Key points

• Released Fixstars Amplify SDK version 1.7
• Claims up to 80% reduction in runtime memory consumption
• Claims formulation up to 30× faster than the previous version
• Reported superior performance to other major optimization tools in traveling salesman problem comparisons
• Envisions large-scale computation on on-premises systems and edge devices

Technical and business implications

Faster formulation can reduce the wait time incurred each time an optimization problem must be reassembled when input conditions change. Reduced memory usage also supports deployment in environments with limited computing resources. From a business perspective, this update could expand applicability where real-time responsiveness or operational constraints have been barriers to adoption—such as logistics and routing. That said, actual effects will vary with problem size, structure, and execution environment, so evaluation including the undisclosed comparison conditions is necessary.

What to watch next

Going forward, key questions include whether the conditions under which the company measured up to 30× speedup and up to 80% memory reduction—such as problem sizes, execution environments, and comparator tools—will be published. It will also be important to verify whether similar improvements appear for practical optimization problems beyond the traveling salesman problem, and whether concrete deployment examples on on-premises systems or edge devices are demonstrated. The specifics of integration with quantum hardware and the actual reductions in recomputation time and resource usage observed by user organizations are also important observational points.

✍️ Quantum Index Analysis

This announcement concerns speed and memory reductions in the SDK-side preprocessing responsible for formulating optimization problems and generating data to pass to solvers. It does not increase the computation speed of the solvers themselves—whether cloud gate-model quantum computers, quantum annealers, or quantum-inspiredquantum-inspiredQuantum-Inspired Technology / Quantum-Inspired Computing / Quantum-Inspired Technology / Quantum-Inspired ComputingA computing technology that, inspired by concepts and mathematical methods from quantum computing, primarily runs on classical computers. It is widely used especially in the field of combinatorial optimization.QI NoteEven though "quantum" appears in the name, it does not necessarily use quantum hardware. In announcements or publications, verify the actual computing platform and whether an advantage over classical methods has been demonstrated. In particular in the optimization field, there are solvers that already demonstrate practically useful, high-level performance. solvers.

However, for large-scale problems this preprocessing can be nontrivial. As bit counts and constraints grow, formulation and request-data generation require more time and memory, so if the claimed “up to 30× faster” and “up to 80% reduction” are reproducible under real-world conditions, they could lower application-wide latency and resource requirements, including the time until a solver begins computation.

Fixstars Amplify had over 1,000 registered organizations and more than 100 million cumulative solves of Amplify AE as of December 2025. In May 2026, the SDK added support for “Amplify Quantum,” enabling access to gate-model quantum computers such as IBM Quantum, IonQ, Rigetti, and IQM from the SDK. In addition to previously supported D-Wave quantum annealers and quantum-inspired machines from Toshiba and Fujitsu, Fixstars has continued expanding supported backends, including OQTOPUS Cloud and AIST’s “System F” .

In short, the Fixstars Amplify SDK is broadening its scope to handle heterogeneous compute resources—from quantum annealers and quantum-inspired solvers to gate-model quantum computers—through a common development environment. Alongside the steady addition of accessible quantum computers and solvers from the SDK, the team is also investing in performance improvements to the SDK itself, which users interact with directly. As usage reaches a certain scale, Fixstars appears to be entering a stage of addressing formulation and data-generation overheads that become apparent in production.

The reported performance results were obtained on a traveling salesman problem of roughly 100,000 bits (317 cities); verification is needed to determine whether similar gains appear for different problem structures and scales. Looking ahead, an important indicator of whether Amplify will become a common optimization platform is not just the number of supported solvers but the extent to which users adopt and switch among multiple compute backends from the same SDK.

NETWORKSee the industry network surrounding Fixstars Amplify →

Related articles

• Fixstars Amplify SDK adds support for Fujitsu’s superconducting quantum computer “System F”
• Fixstars Amplify adds IonQ quantum simulator as a standard machine
• Fixstars Amplify releases GPU annealing solver Amplify AE 1.3

Source

Read the original announcement