A First Step Toward Faster Quantum Networks Using Light! Successful Coherent Coupling Between SnV Centers in Diamond and Optical Cavities

I am Kenichi Kawaguchi, a Senior Project Director in the Modular Quantum Computing Core Project. In this article, I will explain the latest results in diamond spin quantum technology achieved by a research team at Delft University of Technology and QuTech through a joint research project with Fujitsu [1]. This work was published in June 2026 in Physical Review X, a leading academic journal in the quantum field [2], and a press release is available on the QuTech official website [3].

Attended ISC High Performance 2026 #1 ~Focusing on Quantum Computer x HPC related sessions

Introduction

Hello, this is Shun GOKITA from the Quantum Laboratory at Fujitsu.

From June 22 to 26, 2026, I attended ISC High Performance 2026 (hereafter, ISC 2026) held in Hamburg, Germany. In this article, I will share highlights from the conference, focusing on platform-related sessions concerning the integration of quantum computers into HPC environments.

Quantum Generative Adversarial Autoencoders

Hello, I am Naipunnya Raj from the Quantum team at Fujitsu Research India Private Limited (FRIPL).

Quantum Autoencoders (QAE) are excellent at compressing quantum states, but what if we could also make them generate entirely new quantum states? In our recent work, we propose the Quantum Generative Adversarial Autoencoder (QGAA), a model that does exactly this by training a Quantum Generative Adversarial Network (QGAN) on the compressed latent space of a trained QAE. We validated this on entangled state generation and molecular ground state energy profiling for and , achieving energy errors as low as . In this post, we walk through the core idea, the key results, and what this means for practical quantum machine learning.

Improving Hybrid Quantum Models through Efficient Model Selection: A pathway for scalable quantum machine learning

Hello, I am Rahul Bhowmick from the Quantum team at Fujitsu Research India Private Limited (FRIPL).

With advancements in both quantum hardware and software, quantum computing is steadily moving from theory toward practical applications. Fields such as chemistry, materials science, optimization, and machine learning are all expected to benefit from this progress. At FRIPL, we are actively contributing to this effort through both theory and algorithmic research. While fault-tolerant quantum computers are still some years away, there is growing interest in identifying useful methods for today’s hybrid quantum-classical setting.

In this article, I will discuss our recent work on improving the development of hybrid quantum machine learning models by making model selection more efficient. Instead of training every possible candidate architecture which can run into 100s of variants, our approach aims to identify promising models in advance using lightweight evaluation metrics.

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.