1. Fujitsu Research
  2. Quantum Lab

Quantum Lab

Transforming society in the digital age with the world's most advanced computing

Our mission is to push the boundaries of cutting-edge computing technologies,
surpassing the limitations of Moore's Law and Von Neumann bottlenecks.

Quantum computing is a dynamic field of research and development that, when fully realized, will undoubtedly revolutionize the world of computing and society at large. Addressing some of the world’s most pressing challenges will necessitate scientific and technological innovations, where computing will play a crucial role. The quantum computing paradigm introduces a novel approach to processing information through quantum particles, offering the potential to alleviate the computational bottlenecks faced by several complex societal problems.

At Fujitsu Research of America, we delve into the capabilities and constraints of quantum computing, focusing on algorithmic development and applications. Our exploration spans various societal domains, considering both near-term and long-term perspectives. Actively researching the benefits of quantum algorithms for fundamental computational problems, we address specific issues and offer quantum solutions to enhance the resolution of our customers' challenges. Identifying quantum computing advantages across different computational models, applications, and industrial verticals is a crucial step in exploring the future usability of our research.

Researchers in the Quantum Lab

Quantum algorithms,
quantum optimal control,
quantum many-body physics

Hayato Ushijima

Quantum optimization,
quantum machine learning,
quantum algorithms,
quantum error mitigation

Quantum algorithms,
Deep learning and Optimization algorithms

Quantum cryptography,
quantum algorithms,
quantum machine learning theory

Quantum neural networks,
quantum linear algebra,
complex networks

Quantum error-correction,
quantum algorithms,
quantum machine learning

Quantum machine learning,
quantum annealing,
quantum algorithms

Quantum information, complexity theory,
optimization, machine learning, differential privacy

Quantum error mitigation,
quantum circuit compilation

Quantum optimization,
quantum machine learning,
quantum algorithms

Discover more about computing, one of our 5 key technologies

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Publications

2024
2023
  • Another Round of Breaking and Making Quantum Money: How Not to Build It from Lattices, and More. Eurocrypt, 2023. Contributed talk at Annual Conference on Quantum Information Processing(QIP), 2023. Jiahui Liu, Hart Montgomery, Mark Zhandry.
  • Collusion-Resistant Copy Protection for Watermarkable Functionalities. Theory of Cryptography Conference (TCC), 2022; Contributed talk at Annual Conference on Quantum Information Processing(QIP), 2023. Jiahui Liu, Qipeng Liu, Luowen Qian, Mark Zhandry.
  • Pauli String Partitioning Algorithm with the Ising Model for Simultaneous Measurements Tomochika Kurita, Mikio Morita, Hirotaka Oshima, Shintaro Sato The Journal of Physical Chemistry A 127, 1068-1080 (2023).
  • Synergetic quantum error mitigation by randomized compiling and zero-noise extrapolation for the variational quantum eigensolver Tomochika Kurita, Hammam Qassim, Masatoshi Ishii, Hirokata Oshima, Shintaro Sato, Joseph Emerson Quantum 7, 1184 (2023).
  • Rohit Sarma Sarkar, and Bibhas Adhikari. Scalable quantum circuits for n-qubit unitary matrices, In Proceedings of the IEEE International Conference on Quantum Computing and Engineering, (IEEE QCE23), 2023.
  • Rima Hazra, Mayank Singh, Pawan Goyal, Bibhas Adhikari, and Animesh Mukherjee. Modeling interdisciplinary interactions among Physics, Mathematics & Computer Science. To appear in the Journal of Physics: Complexity, 2023.
  • Amrita Mandal, Rohit Sarma Sarkar, and Bibhas Adhikari. Localization of two-dimensional quantum walks defined by generalized Grover coins. Journal of Physics A: Mathematical and Theoretical, 56(2), p.025303, 2023.
  • Bibhas Adhikari, Amrik Singh, and Sandeep Kumar Yadav. Corona product of signed graphs and its application to modeling signed networks, Discrete Mathematics, Algorithms and Applications, 15(1), 2250062, 2023.
  • Alexey Galda, Eesh Gupta, Jose Falla, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, and Ilya Safro. Similarity-Based Parameter Transferability in the Quantum Approximate Optimization Algorithm, Frontiers of Quantum Science and Technology, 2023.
  • Dylan Herman, Cody Googin, Xiaoyuan Liu, Yue Sun, Alexey Galda, Ilya Safro, Marco Pistoia, and Yuri Alexeev. Quantum computing for finance, Nature Reviews Physics: 1-16, 2023.
  • Krish Matta, Xiaoyuan Liu, and Ilya Safro. Decomposition Based Refinement for the Network Interdiction Problem, In Proceedings of the IEEE High Performance Extreme Computing Conference (HPEC), 2023.
  • Anthony Angone, Xiaoyuan Liu, Ruslan Shaydulin, and Ilya Safro. Hybrid Quantum Classical Multilevel Approach for Maximum Cuts on Graphs, In Proceedings of the IEEE High Performance Extreme Computing Conference (HPEC), 2023.
  • Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, and Ilya Safro. Learning To Optimize Quantum Neural Networks Without Gradients, In Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE), 2023.
  • Monika Henzinger, Jalaj Upadhyay, and Sarvagya Upadhyay. Almost tight error bounds on differentially private continual counting. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 5009-5039, 2023.
2022
  • Beating the Classical Impossibility of Position Verification. Innovations in Theoretical Computer Science(ITCS), 2022; Contributed talk at Annual Conference on Quantum Information Processing(QIP), 2022. Jiahui Liu, Qipeng Liu, Luowen Qian.
  • Hidden Cosets and Applications to Unclonable Cryptography. CRYPTO,2021; Contributed talk at Annual Conference on Quantum Information Processing(QIP), 2022 and QCrypt, 2021. Andrea Coladangelo, Jiahui Liu, Qipeng Liu, Mark Zhandry.
  • Amrita Mandal, Rohit Sarma Sarkar, Shantanav Chakraborty, and Bibhas Adhikari. Limit theorems and localization of three-state quantum walks on a line defined by generalized Grover coins, Physical Review A, 106, 042405, 2022.
  • Amrita Mandal, and Bibhas Adhikari. A characterization of orthogonal permutative matrices of order 4, Linear Algebra and its Applications, 654, pp. 102-124, 2022.
  • Pradumn Kumar Pandey, Bibhas Adhikari, Mainak Mazumdar, and Niloy Ganguly. Modeling signed networks as 2-layer growing networks, IEEE Trans. on Knowledge and Data Engineering, 34(7) pp. 3377-3390, 2022.
  • Tinku Ganai, and Bibhas Adhikari. Eigenvalue embedding problem for quadratic regular matrix polynomials with symmetry structures, Linear Algebra and its Applications, 640, pp. 118-148, 2022.
  • Avinash Vadali, Rutuja Kshirsagar, Prasanth Shyamsundar, and Gabriel N. Perdue. Quantum circuit fidelity estimation using machine learning. To appear in Quantum Machine Intelligence, 2022.
  • Allison Beemer, Rutuja Kshirsagar, and Gretchen L. Matthews. Graph-based codes for hierarchical recovery. In Proceedings of the IEEE International Symposium on Information Theory (ISIT), 2022.
  • Hannes Leipold, Federico M. Spedalieri, and Eleanor Rieffel. Tailored Quantum Alternating Operator Ansätzes for Circuit Fault Diagnostics. Algorithms 15.10: 356, 2022.
  • Hannes, Leipold and Federico M. Spedalieri. Quantum annealing with special drivers for circuit fault diagnostics. Scientific Reports 12.1: 11691, 2022.
  • Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin, Ilya Safro, Yuri Alexeev, and Lukasz Cincio. Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers, IEEE Transactions on Quantum Engineering 3:1-20, 2022.
  • Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Avradip Mandal, Sarvagya Upadhyay, Ilya Safro, and Arnab Roy. Leveraging Special-Purpose Hardware for Local Search Heuristics. Computational Optimization and Applications 82.1: 1-29, 2022.
  • Xiaoyuan Liu, Ilya Tyagin, Hayato Ushijima-Mwesigwa, Indradeep Ghosh, and Ilya Safro. Towards Practical Explainability with Cluster Descriptors. In Workshop on Optimization Based Techniques for Emerging Data Mining Problems Workshop (OEDM) at IEEE International Conference on Data Mining (ICDM), 2022.
  • Xiaoyuan Liu, Ruslan Shaydulin, and Ilya Safro. Quantum Approximate Optimization Algorithm with Sparsified Phase Operator. In Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE), 2022.
  • Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Indradeep Ghosh, and Ilya Safro. Partitioning Dense Graphs with Hardware Accelerators. In Proceedings of the International Conference on Computational Science (ICCS), 2022.
  • Hanjing, Xu, Hayato Ushijima-Mwesigwa, and Indradeep Ghosh. Scaling Vehicle Routing Problem Solvers with QUBO-based Specialized Hardware. In Proceedings of the IEEE/ACM 7th Symposium on Edge Computing (SEC), pp. 381-386, 2022.
2021
  • New Approaches to Quantum Copy-Protection. CRYPTO, 2021 and contributed talk at TQC(Conference on the Quantum Computation, Communication and Cryptography), 2021. Scott Aaronson, Jiahui Liu, Qipeng Liu, Mark Zhandry, Ruizhe Zhang.
  • Adaptive Security via Deletion in Attribute-Based Encryption: Solutions from Search Assumptions in Bilinear Groups. ASIACRYPT 2021. Rishab Goyal, Jiahui Liu, Brent Waters.
  • Secure Multi-party Computation in the Bounded Storage Model. IMA Conference on Cryptography and Coding 2021. Jiahui Liu, Satyanarayana Vusirikala.
  • デジタルアニーラを用いた量子状態測定の高速化の取り組み. 栗田知周, 森田幹雄, 大島弘敬, 佐藤信太郎. 研究報告量子ソフトウェア (QS), 2021-3, 15, 1-7 (2021).
  • 低回路深さ同時測定型 VQE の計算コスト検証. 森田幹雄, 高橋憲彦, 栗田知周, 大島弘敬, 佐藤信太郎. 研究報告量子ソフトウェア (QS) 2021-2, 11, 1-7 (2021).
  • Tinku Ganai, and Bibhas Adhikari. Preserving spectral properties of structured matrices under structured perturbations, Linear Algebra and its Applications, 629, pp. 168-191, 2021.
  • Bibhas Adhikari, Biswa Nath Datta, Tinku Ganai, and Michael Karow. Updating structured matrix pencils with no spillover effect on unmeasured spectral data and deflating pair, Linear Algebra and its Applications, 624, pp. 318-348, 2021.
  • Karim Eldefrawy, Nicholas Genise, Rutuja Kshirsagar, Moti Yung. On Regenerating Codes and Proactive Secret Sharing: Relationships and Implications. In Proceedings of the International Symposium on Stabilizing, Safety and Security of Distributed Systems, 2021.
  • Austin Allen, Keller Blackwell, Olivia Fiol, Rutuja Kshirsagar, Bethany Matsick, Gretchen L. Matthews, and Zoe Nelson. Twisted Hermitian Codes. Mathematics, 9, 40, 2021.
  • Hannes Leipold and Federico M. Spedalieri. Constructing driver Hamiltonians for optimization problems with linear constraints. Quantum Science and Technology 7.1: 015013, 2021.
  • Zirou Qiu, Ruslan Shaydulin, Xiaoyuan Liu, Yuri Alexeev, Christopher S. Henry, and Ilya Safro. ELRUNA: Elimination Rule-Based Network Alignment. Journal of Experimental Algorithmics (JEA) 26: 1-32, 2021.
  • Alexey Galda, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, and Ilya Safro. Transferability of optimal QAOA parameters between random graphs. In Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2021.
  • Osman Asif Malik, Hayato Ushijima-Mwesigwa, Arnab Roy, Avradip Mandal, and Indradeep Ghosh. Binary matrix factorization on special purpose hardware. PloS one 16, no. 12: e0261250, 2021.
  • Akshat A. Jha, Eliana L. Stoyanoff, Guga Khundzakishvili, Paul Kairys, Hayato Ushijima-Mwesigwa, and Arnab Banerjee. Digital annealing route to complex magnetic phase discovery. In Proceedings of the International Conference on Rebooting Computing (ICRC), pp. 119-123, 2021.
  • Hayato Ushijima-Mwesigwa, J.D. Hyman, A. Hagberg, Ilya Safro, S. Karra, C.W. Gable, M.R. Sweeney and G. Srinivasan. Multilevel graph partitioning for three-dimensional discrete fracture network flow simulations. Mathematical Geosciences, 53(8), pp.1699-1724, 2021.
  • Jalaj Upadhyay and Sarvagya Upadhyay. A framework for private matrix analysis in sliding window model. In Proceedings of the International Conference on Machine Learning (ICML), pages 10465-10475, 2021.
  • Jalaj Upadhyay, Sarvagya Upadhyay, and Raman Arora. Differentially private analysis on graph streams. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), pages 1171-1179, 2021.
  • Hayato Ushijima-Mwesigwa, M.D. Zadid Khan, Mashrur A. Chowdhury, and Ilya Safro. Optimal placement of wireless charging lanes in road networks. Journal of Industrial and Management Optimization 17, no. 3: 1315-1341, 2021.
  • Eldan Cohen, Hayato Ushijima-Mwesigwa, Avradip Mandal, and Arnab Roy. Unified clustering and outlier detection on specialized hardware. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3770-3774, 2021.
  • Pouya Rezazadeh Kalehbasti, Hayato Ushijima-Mwesigwa, Avradip Mandal, and Indradeep Ghosh. Ising-based louvain method: clustering large graphs with specialized hardware. In Advances in Intelligent Data Analysis XIX: 19th International Symposium on Intelligent Data Analysis (IDA), pp. 350-361, 2021.
2020
  • Siddhant Singh, Bibhas Adhikari, Supriyo Dutta, and David Zueco. Perfect state transfer on hypercubes and its implementation using superconducting qubits, Physical Review A, 102, 062609, 2020.
  • Rohit Sarma Sarkar, Amrita Mandal, and Bibhas Adhikari. Periodicity of lively quantum walks on cycles with generalized Grover coin, Linear Algebra and its Applications, 604, pp. 399-424, 2020. 3. Supriyo Dutta, and Bibhas Adhikari. Construction of cospectral graphs, Journal of Algebraic Combinatorics, 52, pp. 215–235, 2020.
  • Supriyo Dutta, and Bibhas Adhikari. Construction of cospectral graphs, Journal of Algebraic Combinatorics, 52, pp. 215–235, 2020.
  • Pradumn Kumar Pandey, B. Adhikari, and Sandip Chakraborty. A diffusion protocol for detection of link failure and utilization of resources in multi-agent systems, IEEE Trans. on Network Science and Engineering, 7(3), pp. 1493-1507, 2020.
  • Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Avradip Mandal, Sarvagya Upadhyay, Ilya Safro, and Arnab Roy. On Modeling Local Search with Special-Purpose Combinatorial Optimization Hardware. SIAM Workshop on Combinatorial Scientific Computing (CSC), 2020.
  • Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, and Hayato Ushijima-Mwesigwa. Compressed quadratization of higher order binary optimization problems. In Proceedings of the 17th ACM International Conference on Computing Frontiers, pp. 126-131. 2020.
  • Eldan Cohen, Avradip Mandal, Hayato Ushijima-Mwesigwa, and Arnab Roy. Ising-based consensus clustering on specialized hardware. In Advances in Intelligent Data Analysis XVIII: 18th International Symposium on Intelligent Data Analysis (IDA), pp. 106-118, 2020.
  • Christian F.A. Negre, Hayato Ushijima-Mwesigwa, and Susan M. Mniszewski. Detecting multiple communities using quantum annealing on the D-Wave system. PloS one 15, no. 2: e0227538, 2020.

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