Accelerating Multiple Sequence Alignment with Quantum Computing

Hello, I'm Yusuke Kimura from the Quantum Applications CPJ at the Quantum Laboratory.

I have been focusing on genome analysis as one of the application areas for quantum computers. Recently, we implemented part of Multiple Sequence Alignment (MSA)—one of the important techniques in genome analysis—as a quantum circuit, and simulated it efficiently using our decision diagram (DD)-based quantum circuit simulator. The work has been published on arXiv. In this article, I'll introduce the contents of the paper in an easy manner.

Breakthrough quantum technology slashes computational requirements for molecular modelling

In recent years, quantum computers have been garnering attention as a next-generation technology. From healthcare and materials development to the energy sector, their potential applications are virtually limitless. However, significant hurdles still stand in the way of practical implementation. In particular, the issue of qubit errors is severe, and achieving high-accuracy computations requires redundancy to correct these errors. Consequently, realistic computations would require around millions of qubits—a technology that currently remains far in the future.

To address this challenge, we have devised a groundbreaking approach. At Fujitsu Quantum*1, we have developed two core technologies: the “STAR Architecture ver. 3”, jointly developed by Fujitsu and The University of Osaka*2 based on the Fujitsu Small Research Lab*3 and “molecular model optimization technology.” Through these, we have succeeded in breaking through the limitations of conventional quantum computers.*4

These technologies will dramatically accelerate the industrial application of quantum computers in the “Early-FTQC era,” characterized by systems with tens of thousands of qubits.They hold the potential to make significant contributions to solving pressing societal challenges, such as shortening the development time for pharmaceuticals and new materials and improving the energy efficiency of chemical compound manufacturing. In this article, we will provide an easy-to-understand explanation of the details and future prospects of these innovative technologies. While it is a bit lengthy, if you read through to the end, the future of quantum computing will surely become much clearer!

*1:Fujitsu Quantum

*2:Center for Quantum Information and Quantum Biology (QIQB), The University of Osaka

*3:Fujitsu Small Research Lab

*4:Fujitsu and The University of Osaka develop new technologies for chemical material energy calculations on early-FTQC quantum computers [Press Release: March 25, 2026]

The Case for Harness Engineering: Achieving SLM SOTA on SWE-bench Verified with a 27B Model (TTS@8 = 74.8%)

Using Qwen3.5-27B without any fine-tuning, we achieved 74.8% (374/500) on SWE-bench Verified — a benchmark that measures how well a model can fix real OSS issues from GitHub — by generating 8 candidate patches and selecting the best one. This is the highest score*1 among local LLMs with fewer than 229B parameters.

*1:As of April 7, 2026

More Than Just an Answer with Reasoning: Introduction of Grounding Technologies for Multimodal Large Language Models

Hello, we are Fei Li, Jiaqi Ning and Ming Yang, from Generative AI research group of Fujitsu Research & Development Center (FRDC) in China. Today, we would like to introduce our developed grounding technologies for Multimodal Large Language Models (MLLMs).

Fujitsu’s Corporate Benchmarking Proposal: To Unlock the True Value of AI Agent Models ＃3 From Reading to Reasoning: Introducing the Fujitsu Assessing Compliance in Enterprise Dataset for Enterprise Legal Compliance Agents

This article marks the beginning of a TechBlog series entitled 'Fujitsu's Corporate Benchmarking Proposal: To Unlock the True Value of AI Agent Models.' It covers three blogs to the following schedule:

• Part 1: When AI 'Sees' What Isn't There: Introducing a Benchmark for Diagnosing Hallucinations in Multimodal Large Language Models (MLLMs) (Published)
• Part 2: AAAI 2026 AABA4ET Participation Report and Introduction to the Fujitsu RAG Hard Benchmark (Published) Fujitsu RAG Hard Benchmark🔗
• Part 3: From Reading to Reasoning: Introducing the Fujitsu Assessing Compliance in Enterprise Dataset for Enterprise Legal Compliance Agents (This article)

Introducing Fujitsu KG Enhanced RAG (6 sessions) #6 Knowledge Publication - publishing "Usable Knowledge" rather than raw data

Hello. We are Kikuzuki, Narita, Kikuchi, Miyahara from the Artificial Intelligence Laboratory.

To promote the use of generative AI at enterprises, Fujitsu has developed a generative AI framework for enterprises that can flexibly respond to diverse and changing corporate needs and easily comply with the vast amount of data held by a company and laws and regulations. The framework was successfully launched in July 2024 as part of Fujitsu Kozuchi (R&D)'s AI service lineup.

Some of the challenges that enterprise customers face when leveraging specialized generative AI models include:

• Difficulty handling large amounts of data required by the enterprise
• Generated AI cannot meet cost, response speed, and various other requirements
• Requirement to comply with corporate rules and regulations

To address these challenges, the generative AI framework for enterprises consists of the following technologies:

• Fujitsu Knowledge Graph enhanced RAG( *1 )
• Amalgamation Technology
• Generative AI Audit Technology

In this series, we introduce the "Fujitsu Knowledge Graph enhanced RAG" every week. We hope this helps you solve your problems. At the end of the article, we'll also tell you how to try out the technology.

*1: RAG technology: Retrieval Augmented Generation. A technology that extends the capabilities of generative AI by combining it with external data sources.

Toward the Social Implementation of Ocean Digital Twin ~ Demonstration of Seaweed Bed Quantification Technology for Multi‑Species Seaweed Beds ~

Hello, we are Tatsuya Suzuki from the Converging Technologies Research Laboratory and Kazuya Ijuin from the Human Digital Twin Division.

We are engaged in the research and development of the Ocean Digital Twin, which digitally reproduces marine environments to support environmental conservation and industrial applications. As part of this initiative, we are developing seaweed bed quantification technologies that quantitatively evaluate the amount of CO₂ absorbed by seaweed and other marine vegetation and enable their utilization as carbon credits. To date, we have demonstrated the effectiveness of our technology by obtaining certification under J‑Blue Credit, Japan’s domestic blue carbon credit scheme, for seaweed beds consisting of a single seaweed species.
(Reference: Uwaijima Initiative! Collaborative Blue Carbon Project for Eelgrass Restoration by Fisheries Cooperatives, Communities, and Local Government (in Japanese))

Currently, we are advancing these research outcomes toward real‑world business applications and social implementation through Proof of Concept (PoC) activities. In this article, we introduce one such initiative: a demonstration conducted in Yamaguchi Prefecture targeting seaweed beds where multiple seaweed species coexist.

Introduction of Attention Augmented Hallucination Mitigation Technologies for Multimodal Large Language Models

Hello, we are Fei Li, Ziqiang Shi and Jingyi Wang, from Generative AI research group of Fujitsu Research & Development Center (FRDC) in China. Today, we would like to introduce our developed technologies about hallucination mitigation for Multimodal Large Language Models (MLLMs). The related three papers have been accepted by the international conferences of WACV (IEEE/CVF Winter Conference on Applications of Computer Vision) 2026 and ICASSP (IEEE International Conference on Acoustics, Speech, and Signal Processing) 2026.

AAAI-26 Participation and Exhibition #3: Presenting "Hypothesis-Driven Reasoning for LLMs" at the Main Conference

Hello, I'm Yamada from Fujitsu Research's Artificial Intelligence Laboratory. Fujitsu participated in the prestigious international AI conference "The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26)" held in Singapore from January 20 to 27, 2026, presenting multiple papers and hosting a workshop. We will now deliver a series of articles about AAAI-26.

This post is the third article, focusing on techniques that strengthen a large language model’s (LLM’s) ability to solve new tasks by leveraging past experience. The other posts in this series are:

• Part 1: AAAI-26 Participation and Exhibition #1
  • Report on Hosting the Workshop (Published)
• Part 2: AAAI-26 Participation and Exhibition #2
  • Report on the Paper Presentation on Causal AI Technology (Published)
• Part 3: AAAI-26 Participation and Exhibition #3
  • Report on our Paper Presentation on AI Reasoning Technology (This article)

Fujitsu's Corporate Benchmarking Proposal: To Unlock the True Value of AI Agent Models #2 AAAI 2026 AABA4ET Participation Report and Introduction to the Fujitsu RAG Hard Benchmark

This article marks the beginning of a TechBlog series entitled 'Fujitsu's Corporate Benchmarking Proposal: To Unlock the True Value of AI Agent Models.' It covers three blogs to the following schedule:

• Part 1: When AI 'Sees' What Isn't There: Introducing a Benchmark for Diagnosing Hallucinations in Multimodal Large Language Models (MLLMs) (Published)🔗
• Part 2: AAAI 2026 AABA4ET Participation Report and Introduction to the Fujitsu RAG Hard Benchmark (This Article)
• Part 3: Fujitsu Assessing Compliance in Enterprise Dataset (Scheduled for later March)

Hello, We are Siqi Peng and Taku Fukui from the Artificial Intelligence Laboratory.

From January 20 to 27, 2026, we took part in the workshop AABA4ET held in Singapore as part of the international conference AAAI 2026, where we presented a poster.

The blog about the AAAI 2026 workshop is here.

In this article, we will first report on the content and reception of the workshop presentation, and then introduce in concrete terms the benchmark we presented in the poster.

AAAI-26 Participation and Exhibition #2: Presenting “Causal Discovery over Heterogeneous Datasets” and “Ultrafast Nonlinear Causal Discovery” at the Main Conference

Hello, we are Hirofumi Suzuki and Kentaro Kanamori from the Artificial Intelligence Laboratory. Fujitsu participated in the prestigious international AI conference "The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26)" held in Singapore from January 20 to 27, 2026, presenting multiple papers and hosting a workshop. We will now deliver a series of articles about AAAI-26.

In this article, we introduce two of our research papers accepted to the main conference, covering (1) causal discovery over heterogeneous datasets and (2) ultrafast nonlinear causal discovery. In the next post in this series, we will explain another accepted paper, according to the following schedule:

• Part 1: AAAI-26 Participation and Exhibition #1
  • Report on Hosting the Workshop (Published)
• Part 2: AAAI-26 Participation and Exhibition #2
  • Report on the Paper Presentation on Causal AI Technology (This article)
• Part 3: AAAI-26 Participation and Exhibition #3
  • Report on the Paper Presentation on AI Reasoning Technology (Scheduled for March 16)

Fujitsu's Corporate Benchmarking Proposal: To Unlock the True Value of AI Agent Models #1 When AI 'Sees' What Isn't There: Introducing a Benchmark for Diagnosing Hallucinations in Multimodal Large Language Models (MLLMs)

This article marks the beginning of a TechBlog series entitled 'Fujitsu's Corporate Benchmarking Proposal: To Unlock the True Value of AI Agent Models.' It covers three blogs to the following schedule:

• Part 1: When AI 'Sees' What Isn't There: Introducing a Benchmark for Diagnosing Hallucinations in Multimodal Large Language Models (MLLMs) (This Article)
• Part 2: Fujitsu RAG Hard Benchmark (Scheduled for March 13)🔗
• Part 3: Fujitsu Assessing Compliance in Enterprise Dataset (Scheduled for later March)

When AI 'Sees' What Isn't There: Introducing a Benchmark for Diagnosing Hallucinations in Multimodal Large Language Models (MLLMs)

Hello. We are Ziqiang Shi, Liu Liu, Zihao Guo from the Artificial Intelligence Laboratory at Fujitsu Research & Development Center (Beijing). Today, we are pleased to present our research findings on a critical yet often overlooked challenge facing MLLMs: the phenomenon where models overly rely on language-derived knowledge, confidently generating responses that contradict visual information. We have named this phenomenon ECHO (EvidenCe-prior Hallucination Observation). To address this issue, we propose the first dedicated benchmark, the Fujitsu Hallucination Benchmark, along with mitigation strategies that leverage this benchmark.

Efficient Task-Specific Hybrid Attention Model Construction

Hello. We are Xiaojie Xia, Chaoliang Zhong, and Jun Sun from Fujitsu Research and Development Center (FRDC) and Yusuke Oishi from the AI Laboratory in FRJ. We are excited to share our latest research focused on constructing task-specific hybrid models.

Following the Teacher’s Footsteps: Scheduled Checkpoint Distillation for Domain-Specific LLMs

Hello. We are Cheng Feng, Chaoliang Zhong, and Jun Sun from Fujitsu Research and Development Center (FRDC) and Yusuke Oishi from the AI Laboratory in FRJ. We are excited to share our latest research focused on domain-specific Large Language Models (LLM) distillation.