June-Koo+Kevin+Rhee
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KAIST & LG U+ Team Up for Quantum Computing Solution for Ultra-Space 6G Satellite Networking
KAIST quantum computer scientists have optimized ultra-space 6G Low-Earth Orbit (LEO) satellite networking, finding the shortest path to transfer data from a city to another place via multi-satellite hops.
The research team led by Professor June-Koo Kevin Rhee and Professor Dongsu Han in partnership with LG U+ verified the possibility of ultra-performance and precision communication with satellite networks using D-Wave, the first commercialized quantum computer.
Satellite network optimization has remained challenging since the network needs to be reconfigured whenever satellites approach other satellites within the connection range in a three-dimensional space. Moreover, LEO satellites orbiting at 200~2000 km above the Earth change their positions dynamically, whereas Geo-Stationary Orbit (GSO) satellites do not change their positions. Thus, LEO satellite network optimization needs to be solved in real time.
The research groups formulated the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and managed to solve the problem, incorporating the connectivity and link distance limits as the constraints.
The proposed optimization algorithm is reported to be much more efficient in terms of hop counts and path length than previously reported studies using classical solutions. These results verify that a satellite network can provide ultra-performance (over 1Gbps user-perceived speed), and ultra-precision (less than 5ms end-to-end latency) network services, which are comparable to terrestrial communication.
Once QUBO is applied, “ultra-space networking” is expected to be realized with 6G. Researchers said that an ultra-space network provides communication services for an object moving at up to 10 km altitude with an extreme speed (~ 1000 km/h). Optimized LEO satellite networks can provide 6G communication services to currently unavailable areas such as air flights and deserts.
Professor Rhee, who is also the CEO of Qunova Computing, noted, “Collaboration with LG U+ was meaningful as we were able to find an industrial application for a quantum computer. We look forward to more quantum application research on real problems such as in communications, drug and material discovery, logistics, and fintech industries.”
2022.06.17 View 6325 -
Quantum Classifiers with Tailored Quantum Kernel
Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.
The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.
Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.
More recently, the kernel method has been introduced in quantum machine learning with great success. The ability of quantum computers to efficiently access and manipulate data in the quantum feature space can open opportunities for quantum techniques to enhance various existing machine learning methods.
The idea of the classification algorithm with a nonlinear kernel is that given a quantum test state, the protocol calculates the weighted power sum of the fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements (see Figure 1). This requires only a small number of quantum data operations regardless of the size of data. The novelty of this approach lies in the fact that labeled training data can be densely packed into a quantum state and then compared to the test data.
The KAIST team, in collaboration with researchers from the University of KwaZulu-Natal (UKZN) in South Africa and Data Cybernetics in Germany, has further advanced the rapidly evolving field of quantum machine learning by introducing quantum classifiers with tailored quantum kernels.This study was reported at npj Quantum Information in May.
The input data is either represented by classical data via a quantum feature map or intrinsic quantum data, and the classification is based on the kernel function that measures the closeness of the test data to training data.
Dr. Daniel Park at KAIST, one of the lead authors of this research, said that the quantum kernel can be tailored systematically to an arbitrary power sum, which makes it an excellent candidate for real-world applications.
Professor Rhee said that quantum forking, a technique that was invented by the team previously, makes it possible to start the protocol from scratch, even when all the labeled training data and the test data are independently encoded in separate qubits.
Professor Francesco Petruccione from UKZN explained, “The state fidelity of two quantum states includes the imaginary parts of the probability amplitudes, which enables use of the full quantum feature space.”
To demonstrate the usefulness of the classification protocol, Carsten Blank from Data Cybernetics implemented the classifier and compared classical simulations using the five-qubit IBM quantum computer that is freely available to public users via cloud service. “This is a promising sign that the field is progressing,” Blank noted.
Link to download the full-text paper:
https://www.nature.com/articles/s41534-020-0272-6
-Profile
Professor June-Koo Kevin Rhee
rhee.jk@kaist.ac.kr
Professor, School of Electrical Engineering
Director, ITRC of Quantum Computing for AIKAIST
Daniel Kyungdeock Parkkpark10@kaist.ac.krResearch Assistant ProfessorSchool of Electrical EngineeringKAIST
2020.07.07 View 10378