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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.”
Quantum Technology: the Next Game Changer?
The 6th KAIST Global Strategy Institute Forum explores how quantum technology has evolved into a new growth engine for the future The participants of the 6th KAIST Global Strategy Institute (GSI) Forum on April 20 agreed that the emerging technology of quantum computing will be a game changer of the future. As KAIST President Kwang Hyung Lee said in his opening remarks, the future is quantum and that future is rapidly approaching. Keynote speakers and panelists presented their insights on the disruptive innovations we are already experiencing. The three keynote speakers included Dr. Jerry M. Chow, IBM fellow and director of quantum infrastructure, Professor John Preskill from Caltech, and Professor Jungsang Kim from Duke University. They discussed the academic impact and industrial applications of quantum technology, and its prospects for the future. Dr. Chow leads IBM Quantum’s hardware system development efforts, focusing on research and system deployment. Professor Preskill is one of the leading quantum information science and quantum computation scholars. He coined the term “quantum supremacy.” Professor Kim is the co-founder and CTO of IonQ Inc., which develops general-purpose trapped ion quantum computers and software to generate, optimize, and execute quantum circuits. Two leading quantum scholars from KAIST, Professor June-Koo Kevin Rhee and Professor Youngik Sohn, and Professor Andreas Heinrich from the IBS Center for Quantum Nanoscience also participated in the forum as panelists. Professor Rhee is the founder of the nation’s first quantum computing software company and leads the AI Quantum Computing IT Research Center at KAIST. During the panel session, Professor Rhee said that although it is undeniable the quantum computing will be a game changer, there are several challenges. For instance, the first actual quantum computer is NISQ (Noisy intermediate-scale quantum era), which is still incomplete. However, it is expected to outperform a supercomputer. Until then, it is important to advance the accuracy of quantum computation in order to offer super computation speeds. Professor Sohn, who worked at PsiQuantum, detailed how quantum computers will affect our society. He said that PsiQuantum is developing silicon photonics that will control photons. We can’t begin to imagine how silicon photonics will transform our society. Things will change slowly but the scale would be massive. The keynote speakers presented how quantum cryptography communications and its sensing technology will serve as disruptive innovations. Dr. Chow stressed that to realize the potential growth and innovation, additional R&D is needed. More research groups and scholars should be nurtured. Only then will the rich R&D resources be able to create breakthroughs in quantum-related industries. Lastly, the commercialization of quantum computing must be advanced, which will enable the provision of diverse services. In addition, more technological and industrial infrastructure must be built to better accommodate quantum computing. Professor Preskill believes that quantum computing will benefit humanity. He cited two basic reasons for his optimistic prediction: quantum complexity and quantum error corrections. He explained why quantum computing is so powerful: quantum computer can easily solve the problems classically considered difficult, such as factorization. In addition, quantum computer has the potential to efficiently simulate all of the physical processes taking place in nature. Despite such dramatic advantages, why does it seem so difficult? Professor Preskill believes this is because we want qubits (quantum bits or ‘qubits’ are the basic unit of quantum information) to interact very strongly with each other. Because computations can fail, we don’t want qubits to interact with the environment unless we can control and predict them. As for quantum computing in the NISQ era, he said that NISQ will be an exciting tool for exploring physics. Professor Preskill does not believe that NISQ will change the world alone, rather it is a step forward toward more powerful quantum technologies in the future. He added that a potentially transformable, scalable quantum computer could still be decades away. Professor Preskill said that a large number of qubits, higher accuracy, and better quality will require a significant investment. He said if we expand with better ideas, we can make a better system. In the longer term, quantum technology will bring significant benefits to the technological sectors and society in the fields of materials, physics, chemistry, and energy production. Professor Kim from Duke University presented on the practical applications of quantum computing, especially in the startup environment. He said that although there is no right answer for the early applications of quantum computing, developing new approaches to solve difficult problems and raising the accessibility of the technology will be important for ensuring the growth of technology-based startups.
Professor June-Koo Rhee’s Team Wins the QHack Open Hackathon Science Challenge
The research team consisting of three master students Ju-Young Ryu, Jeung-rak Lee, and Eyel Elala in Professor June-Koo Rhee’s group from the KAIST IRTC of Quantum Computing for AI has won the first place at the QHack 2022 Open Hackathon Science Challenge. The QHack 2022 Open Hackathon is one of the world’s prestigious quantum software hackathon events held by US Xanadu, in which 250 people from 100 countries participate. Major sponsors such as IBM Quantum, AWS, CERN QTI, and Google Quantum AI proposed challenging problems, and a winning team is selected judged on team projects in each of the 13 challenges. The KAIST team supervised by Professor Rhee received the First Place prize on the Science Challenge which was organized by the CERN QTI of the European Communities. The team will be awarded an opportunity to tour CERN’s research lab in Europe for one week along with an online internship. The students on the team presented a method for “Leaning Based Error Mitigation for VQE,” in which they implemented an LBEM protocol to lower the error in quantum computing, and leveraged the protocol in the VQU algorithm which is used to calculate the ground state energy of a given molecule. Their research successfully demonstrated the ability to effectively mitigate the error in IBM Quantum hardware and the virtual error model. In conjunction, Professor June-Koo (Kevin) Rhee founded a quantum computing venture start-up, Qunova Computing(https://qunovacomputing.com), with technology tranfer from the KAIST ITRC of Quantum Computing for AI. Qunova Computing is one of the frontier of the quantum software industry in Korea.
Quantum Emitters: Beyond Crystal Clear to Single-Photon Pure
‘Nanoscale Focus Pinspot’ can quench only the background noise without changing the optical properties of the quantum emitter and the built-in photonic structure Photons, fundamental particles of light, are carrying these words to your eyes via the light from your computer screen or phone. Photons play a key role in the next-generation quantum information technology, such as quantum computing and communications. A quantum emitter, capable of producing a single, pure photon, is the crux of such technology but has many issues that have yet to be solved, according to KAIST researchers. A research team under Professor Yong-Hoon Cho has developed a technique that can isolate the desired quality emitter by reducing the noise surrounding the target with what they have dubbed a ‘nanoscale focus pinspot.’ They published their results on June 24 in ACS Nano. “The nanoscale focus pinspot is a structurally nondestructive technique under an extremely low dose ion beam and is generally applicable for various platforms to improve their single-photon purity while retaining the integrated photonic structures,” said lead author Yong-Hoon Cho from the Department of Physics at KAIST. To produce single photons from solid state materials, the researchers used wide-bandgap semiconductor quantum dots — fabricated nanoparticles with specialized potential properties, such as the ability to directly inject current into a small chip and to operate at room temperature for practical applications. By making a quantum dot in a photonic structure that propagates light, and then irradiating it with helium ions, researchers theorized that they could develop a quantum emitter that could reduce the unwanted noisy background and produce a single, pure photon on demand. Professor Cho explained, “Despite its high resolution and versatility, a focused ion beam typically suppresses the optical properties around the bombarded area due to the accelerated ion beam’s high momentum. We focused on the fact that, if the focused ion beam is well controlled, only the background noise can be selectively quenched with high spatial resolution without destroying the structure.” In other words, the researchers focused the ion beam on a mere pin prick, effectively cutting off the interactions around the quantum dot and removing the physical properties that could negatively interact with and degrade the photon purity emitted from the quantum dot. “It is the first developed technique that can quench the background noise without changing the optical properties of the quantum emitter and the built-in photonic structure,” Professor Cho asserted. Professor Cho compared it to stimulated emission depletion microscopy, a technique used to decrease the light around the area of focus, but leaving the focal point illuminated. The result is increased resolution of the desired visual target. “By adjusting the focused ion beam-irradiated region, we can select the target emitter with nanoscale resolution by quenching the surrounding emitter,” Professor Cho said. “This nanoscale selective-quenching technique can be applied to various material and structural platforms and further extended for applications such as optical memory and high-resolution micro displays.” Korea’s National Research Foundation and the Samsung Science and Technology Foundation supported this work. -PublicationMinho Choi, Seongmoon Jun, and Yong-Hoon Cho et al. ACS Nano‘Nanoscale Focus Pinspot for High-Purity Quantum Emitters via Focused-Ion-Beam-Induced Luminescence Quenching,’(https://pubs.acs.org/doi/10.1021/acsnano.1c00587) -ProfileProfessor Yong-Hoon ChoQuantum & Nanobio Photonics Laboratoryhttp://qnp.kaist.ac.kr/ Department of PhysicsKAIST
KAIST Joins IBM Q Network to Accelerate Quantum Computing Research and Foster Quantum Industry
KAIST has joined the IBM Q Network, a community of Fortune 500 companies, academic institutions, startups, and research labs working with IBM to advance quantum computing for business and science. As the IBM Q Network’s first academic partner in Korea, KAIST will use IBM's advanced quantum computing systems to carry out research projects that advance quantum information science and explore early applications. KAIST will also utilize IBM Quantum resources for talent training and education in preparation for building a quantum workforce for the quantum computing era that will bring huge changes to science and business. By joining the network, KAIST will take a leading role in fostering the ecosystem of quantum computing in Korea, which is expected to be a necessary enabler to realize the Fourth Industrial Revolution. Professor June-Koo Rhee who also serves as Director of the KAIST Information Technology Research Center (ITRC) of Quantum Computing for AI has led the agreement on KAIST’s joining the IBM Q Network. Director Rhee described quantum computing as "a new technology that can calculate mathematical challenges at very high speed and low power” and also as “one that will change the future.” Director Rhee said, “Korea started investment in quantum computing relatively late, and thus requires to take bold steps with innovative R&D strategies to pave the roadmap for the next technological leap in the field”. With KAIST joining the IBM Q Network, “Korea will be better equipped to establish a quantum industry, an important foundation for securing national competitiveness,” he added. The KAIST ITRC of Quantum Computing for AI has been using the publicly available IBM Quantum Experience delivered over the IBM Cloud for research, development and training of quantum algorithms such as quantum artificial intelligence, quantum chemical calculation, and quantum computing education. KAIST will have access to the most advanced IBM Quantum systems to explore practical research and experiments such as diagnosis of diseases based on quantum artificial intelligence, quantum computational chemistry, and quantum machine learning technology. In addition, knowledge exchanges and sharing with overseas universities and companies under the IBM Q Network will help KAIST strengthen the global presence of Korean technology in quantum computing. About IBM Quantum IBM Quantum is an industry-first initiative to build quantum systems for business and science applications. For more information about IBM's quantum computing efforts, please visit www.ibm.com/ibmq. For more information about the IBM Q Network, as well as a full list of all partners, members, and hubs, visit https://www.research.ibm.com/ibm-q/network/ ©Thumbnail Image: IBM. (END)
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 firstname.lastname@example.org Professor, School of Electrical Engineering Director, ITRC of Quantum Computing for AIKAIST Daniel Kyungdeock Parkkpark10@kaist.ac.krResearch Assistant ProfessorSchool of Electrical EngineeringKAIST
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