“CalqMatch”: GPT-4, vector DB, and quantum computer technology to start demonstration experiment of multi-side matching platform covering everything from clothing, food, and housing to business and private life

Kanda Quantum Co., Ltd.
“CalqMatch”: GPT-4, vector DB, and quantum computer technology to start demonstration experiment of multi-side matching platform covering everything from clothing, food, and housing to business and private life
Bringing together cutting-edge technologies from OpenAI GPT-4, Vector DB Pinecone, and quantum computer Google Dwave

KandaQuantum, whose mission is to “eliminate mismatches” with generative AI and quantum technology, today started a demonstration experiment of the multi-side network platform “CalqMatch” that makes full use of cutting-edge technology. The platform will be powered by AI technology leader OpenAI’s GPT-4 and state-of-the-art vector search engine Pinecone, as well as quantum computers from Google Dwave. This groundbreaking platform aims to streamline matching and enable closer collaboration between business partners and clients across industries. table of contents
Outline of “CalqMatch Platform”
Utilization of GPT-4, vector DB, and quantum annealing technology Comprehensive matching platform including time and place
Purpose of the demonstration experiment
Summary and contact information
Outline of “CalqMatch Platform”
・Conceptual diagram from dialogue with people to making advanced recommendations
[Image 1: https://prtimes.jp/i/82094/104/resize/d82094-104-040682bb8a20cbfd4fae-7.png&s3=82094-104-eda083542089a6a4be355161f7729476-2758×1502.png] Unlike conventional two-side matching platforms, “CalqMatch” utilizes GPT-4, quantum annealing technology, and a cutting-edge vector search engine to realize a multi-side matching platform covering everything from food, clothing, and housing to business and private life. I am aiming for With conventional platforms, matching between consumers and providers was limited to two sides, one-on-one, limiting target fields and needs, limiting usage scenarios. In addition, matching platforms so far have been limited to two: system matching and intermediary matching. CalqMatch overcomes this and provides a matching platform that seamlessly cooperates with generative AI matching to meet a wide range of demands such as clothing, food and housing, business and private. Users can obtain optimal matching results based on their individual needs and conditions, which enables providers to meet more diverse market needs. In addition, Pinecone’s vector search engine enables rapid extraction of highly relevant information, and flexible matching functions that take time and place into account provide a user-friendly experience.・Overview of generative AI matching
[Image 2: https://prtimes.jp/i/82094/104/resize/d82094-104-46bf312bf7292b544c7e-9.png&s3=82094-104-8b23af2935b038f2ebf1333d102b8722-1398×876.png] Horizontal axis and vertical axis in the figure
Horizontal axis: Abundance of internal information: Amount of information linked to the individual, such as the voice tone, complexion, and non-standard information such as the resume of the matching target
Vertical axis: Abundance of external information: Amount of
information for making proposals to matching candidates. For example, it corresponds to database information stored for keyword searches. Matching/recommendation type
Mediation Matching: Matching that involves a person. Compared to system matching, internal information such as facial expressions and tone of voice can be acquired, so matching is possible after understanding the person well, but it is difficult to make proposals based on the vast amount of information in the system, and training of the person in charge is costly.
System matching: Includes matching by search type and AI. Matching is possible after accessing a huge amount of information, but since personal information is acquired from questionnaire-type information and system logs, it is not possible to acquire rich internal information that can be achieved by intermediary matching.

Utilization of GPT-4, vector DB, and quantum annealing technology “CalqMatch” uses OpenAI GPT-4’s overwhelming natural language processing ability, vector DB such as Pinecone, and quantum annealing technology such as Google Dwave, and is used in various scenes such as business and private from the industry related to clothing, food and housing. We aim to match your needs. As a result, we will provide a highly personalized matching service that meets a wide range of needs that conventional matching platforms could not meet.
・Conceptual diagram for generating Gantt charts with tens of thousands of hundreds of millions of lines in real time from a multi-side network and performing highly accurate recommendations and matching in time series
[Image 3: https://prtimes.jp/i/82094/104/resize/d82094-104-a950befd4755a3e1b8a7-7.png&s3=82094-104-eb64e7e3be1664329255206c03366952-2336×1596.png] vector database
Human: Refers to a network that includes not only people but also agents such as AI and robots. Here, SNS networks and networks between men and women are also included as networks between humans.
Projects: Various projects arise as human activities, and the projects also have a complicated network structure. dynamic things.
Product: Corresponds to the goods resulting from the project. For example, rental properties, books, movies, etc. static things. Tensor type DB
It shows the strength of the relationship between networks. Each dimension corresponds to one network.
Gantt chart
The diagram shows who (including people and agents) will be in charge of each task from when to when. The Gantt chart with a huge number of rows changes in real time every time the current information and AI’s future prediction results are updated. Mathematical optimization technology and partial quantum computer (using quantum annealing technology) for high-speed processing

・Utilizing GPT-4 and vector DB Pinecone, sorting out customer requests, extracting issues, and proposing properties.
[Image 4: https://prtimes.jp/i/82094/104/resize/d82094-104-62d1d76e072c8c085e60-3.png&s3=82094-104-ff6613b1268729544df536d06c1a0538-1990×1128.png]
[Image 5: https://prtimes.jp/i/82094/104/resize/d82094-104-b3aadb50fac4e82d0679-4.png&s3=82094-104-7e0533bb498bac6bdb665765f0cf1d99-1380×1260.png] Comprehensive matching platform including time and place
In addition, “CalqMatch” pursues user convenience and enables advanced matching that takes into account time and place. As a result, we will provide the best matching results according to the situation, from lifestyle aspects such as shopping, beauty, and rentals to business and private collaborations.
・Output result to Notion of schedule optimization solver “CalqPM” using quantum technology and natural language processing technology Automatic task matching and Gantt chart generation from employee information and operating time information.
[Image 6: https://prtimes.jp/i/82094/104/resize/d82094-104-934baa9253ac7779e277-0.png&s3=82094-104-feb8706e12c89f9057d9866322f870ec-2446×1146.png] ・An example of a demonstration experiment of a real-time congestion avoidance proposal app that utilizes quantum technology and map information
[Image 7: https://prtimes.jp/i/82094/104/resize/d82094-104-6481bc446461c872b6e8-1.png&s3=82094-104-af8b42122b789664c82d4e6c1f8f3dda-1014×800.png] ・Using GPT-3 technology, mathematical optimization technology and quantum technology, project matching for 40 freelancers in a few minutes.
[Image 8: https://prtimes.jp/i/82094/104/resize/d82094-104-5892e48d8e90cba11114-2.png&s3=82094-104-4f4cb6ac91ff684419d6bdbcf1849d8b-2700×1236.png] https://prtimes.jp/main/html/rd/p/000000004.000082094.html
We have a track record of the following demonstration experiments for optimization in terms of time and place, and these quantum computer technologies will be applied in this demonstration experiment. Purpose of the demonstration experiment and future plans
The purpose of this demonstration experiment is to evaluate the usefulness and reliability of the platform. Specifically, we expect to achieve the following goals:
Improved matching accuracy in each scene
Realization of efficient matching considering time and place Strengthening competitiveness in the marketplace
Future demonstration experiments will be the following, and we will start joint demonstration experiments with multiple companies. Side job/freelance matching solution using LINE and CalqWorks Medical and beauty matching solution using LINE
Love and matchmaking matching solution using LINE and smartphone apps summary
The comprehensive multi-side matching platform “CalqMatch” utilizes GPT-4, vector DB, and quantum annealing technology to provide advanced matching and recommendation services covering everything from clothing, food, and housing to business and private. Through this demonstration experiment, it is expected that the potential of CalqMatch will be clarified and its spread in various fields will be promoted.
For inquiries about demonstration experiments, please contact us from the following.
[About KandaQuantum Inc.]
Company name: KandaQuantum Co., Ltd.
Headquarters: 5F Kojimachi Building, 6-6-2 Kojimachi, Chiyoda-ku, Tokyo, 102-0083
Representative: Daisuke Motoki, President and Representative Director Business description: Established in 2020. In two and a half years, we have supported cutting-edge technologies such as quantum computers, AI, cloud, IoT, etc., from over 20 major domestic companies in each industry to startups that have completed funding of hundreds of millions to billions of yen. With the mission of “creating a foundation for collaborative creation,” we will create new value by utilizing generative AI and quantum technology to realize a society where everyone can be passionate about their lives.
Details about this release:


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