Toppan Develops Quantum Computing Method Towards Error Correction Technologies For Quantum Software


Two research papers selected for the Posters program at the IEEE QCE22 international conference.

Tokyo – Toppan, a global leader in communication, security, packaging, decorative materials and electronic solutions, is advancing quantum software research as part of its efforts to develop technologies next-generation computing. Two papers in which Toppan researchers are involved have been selected for the IEEE1 International Conference on Quantum Computing and Engineering (QCE)2 poster program, with the research to be exhibited September 19-22.

These articles cover research in optical quantum computing3 and quantum machine learning. The first article on optical quantum computing looks at a new computational method developed through a collaboration between Toppan and blueqat Inc. (blueqat). This method should lead to the application of quantum error correction technologies4, contributing to the realization of high-speed optical quantum computers in the future.

The second article discusses a type of quantum machine learning that Toppan is working on. It is expected that the use of a new method for evaluating learning model building processes will make learning trends visible and enable the application of machine learning to practical challenges, such as as improving the accuracy and efficiency of inspection processes in factories. By developing these technologies, Toppan aims to boost the industrial application of quantum computers and contribute to a safe and secure digital society in the future.

The QCE was first held in 2020 and is one of the largest annual international conferences on quantum computing and engineering for the quantum industry. Papers are presented and a poster program is also organized in order to help the development of quantum computing and its related industries. QCE22 marks the event’s third year and will be held at the Omni Interlocken Hotel in Broomfield, Colorado, USA, from Sunday, September 18 through Friday, September 23.

About research papers

(1) Optimization of non-Gaussian state generation using tensor networks and automatic differentiation
Authors: Ryutaro Nagai (blueqat Inc.), Takao Tomono (Toppan Inc.)

(2) The characteristic of Quantum Kernel in the initial learning process
Authors: Takao Tomono and Satoko Natsubori (Toppan Inc.)

Research background

In recent years, expectations for quantum computers have increased as a next-generation computing platform with high computing power and high security. There are currently a variety of research and hardware developments underway that aim to bring more capable quantum computers to life, exploiting techniques that use superconductors, trapped ions, silicon, photons, and more. The optical quantum computing method is particularly important because it not only enables computation, but can also be used in communications involving the sending and receiving of quantum data.

In response, Toppan is advancing quantum software research in collaboration with blueqat, a leader in software development for quantum computers. Selection for presentation in the QCE22 poster program comes in recognition of results from the development of a novel optical quantum computation method that may lead to the establishment of error-correcting technologies in efforts focused on increasing the speed of quantum computing.

It is believed that we will soon have practical quantum machine learning using classical-quantum hybrid computing,5 and the development of quantum software that ensures that quantum computers perform calculations accurately and appropriately is planned. To meet these challenges, Toppan has discovered a new evaluation method, the proposal of which will be presented during the QCE22 poster program.

Toppan aims to contribute to building a safe and secure digital society through its Digital Transformation (DX) business. In anticipation of the quantum computing era, Toppan is committed to researching not only the high-speed computing functions of optical quantum computers, but also optical quantum communication, which is essential for quantum data transmission. Additionally, Toppan is working on the development of quantum machine learning technologies that can provide solutions to social problems.

Overview of research articles

(1) Optimization of non-Gaussian state generation using tensor networks and automatic differentiation
?Proposal for the optimization of the generation of non-Gaussian states
Previous research has proposed a framework to generate an arbitrary non-Gaussian state from a parametric circuit, but there were some problems in optimizing the parameters efficiently. In this paper, the authors propose a method for parameter optimization through tensor networks incorporating automatic differentiation to generate a desired non-Gaussian state. This method generated Schrödinger’s cat states (states of superposition of photons) with a fidelity comparable to that of previous studies. The method uses simulation expressed using a tensor network, which should shorten computation times and increase efficiency.

Toppan will continue to collaborate with blueqat to achieve the goal of using this computational method to solve specific problems, such as those related to error correction and quantum teleportation.

(2) The characteristic of Quantum Kernel in the initial learning process
? Propose a method to evaluate learning models
Here, the authors propose a method for evaluating quantum machine learning and classical classical machine learning. By using a new plotting method on the space of receiver operating characteristics, it is now possible to distinguish the construction methods of two learning models. The authors confirmed the effectiveness of the method by comparing it to real equipment using heart disease data and other datasets. Toppan aims to analyze the differences between the two and exploit these characteristics in building learning models for quantum machine learning.

Future plans

Toppan will contribute to the more widespread use of quantum computers and the formation of a safe and secure society through basic research on computational methods, algorithms and other elements of quantum computing, and the development of quantum machine learning and other technologies.

1. IEEE: Institute of Electrical and Electronics Engineers
The IEEE is the world’s largest academic organization dedicated to electrical and computer engineering research, headquartered in the United States of America, with more than 423,000 members in more than 160 countries.


3. Optical quantum computing
A quantum computing method that uses photons.

4. Error Correction Technologies
Error correction technologies predict and replace data gaps that occur during transmission. They help ensure the reliability of memory devices as well as digital communication and signal processing.

5. Classical-quantum hybrid computing
Hybrid classical-quantum computing takes advantage of the strengths of quantum computing and classical computing. Since quantum computers and classical computers have strengths in different computational fields, this method uses classical computers to extract the computational field of quantum computers and perform quantum computation.

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