Fixstars Amplify releases Amplify‑BBOpt v1.3, speeds up black‑box optimization computation

【Article Title】 Fixstars Amplify releases Amplify‑BBOpt v1.3, speeds up black‑box optimization computation 【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-bbopt-v1-3-release/) Fixstars Amplify has released v1.3 of the black‑box optimization library Amplify‑BBOpt, which leverages the Fixstars Amplify SDK. The update renews the internal implementation of the Factorization Machine and advances execution speed and surrogate‑model search performance. ✍️ Quantum Index Analysis We explain the technical and business significance behind the announcement and highlight evaluation points that aren’t obvious from the numbers or headlines alone. Read our independent analysis ↓ Summary of the announcement Amplify‑BBOpt is a black‑box optimization library that makes use of the Fixstars Amplify SDK. The v1.3 release refreshes the internal implementation of the Factorization Machine, achieving a lighter library and faster execution. The update also accelerates the process that searches for the surrogate model’s minimum, shortening search times. The announcement does not include specific performance improvement rates, measurement conditions, impacts on supported compute platforms, or user case studies. Key points • Released Amplify‑BBOpt v1.3, leveraging the Fixstars Amplify SDK • Refreshed internal implementation of the Factorization Machine • Slimmed the library and improved execution speed • Accelerated surrogate‑model minimum search and reduced search time • Performance improvement rates, measurement conditions, and use cases are not disclosed Technical and business significance In black‑box optimization, surrogate models are used to guide search when the objective function cannot be analyzed directly. The improvements to internal implementations and minimum‑search processing could reduce computational load and waiting times associated with optimization. However, assessing the technical and business impact requires comparison data against previous versions and performance on real‑world problems. What to watch next Going forward, attention will focus on whether the release provides concrete comparisons of execution speed, search time, and library size versus prior versions. It will also be important to see effects across problem scales and execution environments, and the extent to which the speedups preserve optimization result quality. Publication of use cases or deployment records would make practical utility easier to judge. ✍️ Quantum Index Analysis This update focuses on improving the efficiency of computation that composes black‑box optimization rather than adding new optimization algorithms. Amplify‑BBOpt approximates the objective function using Factorization Machines (FM) and kernel models, then optimizes that surrogate model with an Ising machine to identify the next candidate to evaluate. In v1.3, the standard FM implementation was changed and the conversion process from surrogate models to QUBO polynomials was accelerated for both FMQA and Kernel‑QA. These changes reduce computational overhead associated with the iterative processes of black‑box optimization rather than improving the raw solver performance of an Ising machine itself. Parallel to these technical developments, application research in industrial fields is progressing. In 2024, Mazda and Fixstars Amplify presented “Simultaneous body structure design optimization for multiple vehicle models using FMQA” at the Society of Automotive Engineers of Japan Autumn Meeting. In August 2026, researchers from Mazda and Fixstars Amplify jointly presented verification of the effectiveness of quantum‑inspired methods for multi‑objective, multi‑constraint black‑box optimization problems at the Japan Society of Mechanical Engineers’ 36th Design Engineering and Systems Division Conference. This suggests they are validating application to more complex design‑optimization problems with multiple objectives and constraints. However, there is no information explicitly linking those research results to v1.3. It is important to distinguish between objective‑function evaluation time and the computation time of the optimization algorithm itself in black‑box optimization. For example, if a single simulation takes hours, speeding up internal processing will have limited effect on total runtime. Conversely, when evaluation times are relatively short and many iterations are required, these improvements are more likely to be impactful. Fixstars Amplify’s SDK v1.7 in September 2026 also achieved faster formulation and memory reductions, and v1.3 should be seen as another layer of performance improvement distinct from the solver itself. Specific speedup rates and measurement conditions have not been published. The key question is how much the overall optimization computation time and number of objective‑function evaluations can be reduced on real problems while maintaining solution quality. NETWORKSee the industry network around Fixstars Amplify → (/qi-network/) Related articles • Fixstars Amplify SDK v1.7, speeding up formulation up to 30× and reducing memory use up to 80% (https://en.quantumindex.jp/fixstars-amplify-sdk-1-7-release/) • Fixstars Amplify releases GPU annealing solver “Amplify AE” 1.3 (https://en.quantumindex.jp/fixstars-amplify-annealing-engine-1-3/) • Q‑CTRL expands Fire Opal’s quantum‑optimization features with constrained QAOA and error‑mitigation (https://en.quantumindex.jp/qctrl-fire-opal-constrained-qaoa-estimator-twirling/) Source Read the original announcement (https://amplify.fixstars.com/ja/news/1932/) 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 v1.3 of the black‑box optimization library Amplify‑BBOpt, which leverages the Fixstars Amplify SDK. The update renews the internal implementation of the Factorization Machine and advances execution speed and surrogate‑model search performance.

Summary of the announcement

Amplify‑BBOpt is a black‑box optimization library that makes use of the Fixstars Amplify SDK. The v1.3 release refreshes the internal implementation of the Factorization Machine, achieving a lighter library and faster execution. The update also accelerates the process that searches for the surrogate model’s minimum, shortening search times. The announcement does not include specific performance improvement rates, measurement conditions, impacts on supported compute platforms, or user case studies.

Key points

• Released Amplify‑BBOpt v1.3, leveraging the Fixstars Amplify SDK
• Refreshed internal implementation of the Factorization Machine
• Slimmed the library and improved execution speed
• Accelerated surrogate‑model minimum search and reduced search time
• Performance improvement rates, measurement conditions, and use cases are not disclosed

Technical and business significance

In black‑box optimization, surrogate models are used to guide search when the objective function cannot be analyzed directly. The improvements to internal implementations and minimum‑search processing could reduce computational load and waiting times associated with optimization. However, assessing the technical and business impact requires comparison data against previous versions and performance on real‑world problems.

What to watch next

Going forward, attention will focus on whether the release provides concrete comparisons of execution speed, search time, and library size versus prior versions. It will also be important to see effects across problem scales and execution environments, and the extent to which the speedups preserve optimization result quality. Publication of use cases or deployment records would make practical utility easier to judge.

✍️ Quantum Index Analysis

This update focuses on improving the efficiency of computation that composes black‑box optimization rather than adding new optimization algorithms.

Amplify‑BBOpt approximates the objective function using Factorization Machines (FM) and kernel models, then optimizes that surrogate model with an Ising machine to identify the next candidate to evaluate. In v1.3, the standard FM implementation was changed and the conversion process from surrogate models to QUBO polynomials was accelerated for both FMQA and Kernel‑QA. These changes reduce computational overhead associated with the iterative processes of black‑box optimization rather than improving the raw solver performance of an Ising machine itself.

Parallel to these technical developments, application research in industrial fields is progressing. In 2024, Mazda and Fixstars Amplify presented “Simultaneous body structure design optimization for multiple vehicle models using FMQA” at the Society of Automotive Engineers of Japan Autumn Meeting. In August 2026, researchers from Mazda and Fixstars Amplify jointly presented verification of the effectiveness of quantum‑inspired methods for multi‑objective, multi‑constraint black‑box optimization problems at the Japan Society of Mechanical Engineers’ 36th Design Engineering and Systems Division Conference. This suggests they are validating application to more complex design‑optimization problems with multiple objectives and constraints. However, there is no information explicitly linking those research results to v1.3.

It is important to distinguish between objective‑function evaluation time and the computation time of the optimization algorithm itself in black‑box optimization. For example, if a single simulation takes hours, speeding up internal processing will have limited effect on total runtime. Conversely, when evaluation times are relatively short and many iterations are required, these improvements are more likely to be impactful.

Fixstars Amplify’s SDK v1.7 in September 2026 also achieved faster formulation and memory reductions, and v1.3 should be seen as another layer of performance improvement distinct from the solver itself. Specific speedup rates and measurement conditions have not been published. The key question is how much the overall optimization computation time and number of objective‑function evaluations can be reduced on real problems while maintaining solution quality.

NETWORKSee the industry network around Fixstars Amplify →

Related articles

• Fixstars Amplify SDK v1.7, speeding up formulation up to 30× and reducing memory use up to 80%
• Fixstars Amplify releases GPU annealing solver “Amplify AE” 1.3
• Q‑CTRL expands Fire Opal’s quantum‑optimization features with constrained QAOA and error‑mitigation

Source

Read the original announcement