RIKEN Quantum Seminar

Data assimilation with the full covariance on a fault-tolerant quantum computer

October 21 (Wed) 15:00 - 16:30, 2026

Takemasa Miyoshi (Team Principal, Data Assimilation Research Team, RIKEN Center for Computational Science (R-CCS))

Data assimilation (DA) is the core of numerical weather prediction, estimating an accurate state of the weather model by combining forecast and observations. DA provides a general approach and is being applied to various problems beyond weather. The state estimation is a linear solve where the background error covariance B is essential. Since B is an N × N matrix with N ~ 10^8 to 10^9 for the weather model, the full B has never been used. For decades the community has approximated B by parameterized covariance models or by low-rank ensembles with localization and thereby discards genuine structures that we know exist from large-ensemble experiments. Fault-tolerant quantum computing (FTQC) could remove this constraint, since quantum linear system solvers scale polylogarithmically in N. However, the DA equations cannot be applied naively: the observation-space form squares the block-encoding normalization factor for observations with wide footprints, and the state-space form contains B^{-1}, whose condition number ~10^8. This talk presents a combined state-observation formulation that avoids both difficulties: it contains no B^{-1}, and its condition number is governed by the observation error covariance R rather than by B. Numerical experiments with the Lorenz-96 model reproduce the exact solution with the full B and outperform localized ensemble Kalman filters. The formulation assumes that B and the observation operator are provided as block-encoded oracles, and how to construct such oracles without ever storing the matrices is the open problem. A first concrete target is an end-to-end demonstration with Lorenz-96 on a quantum simulator with only seven data qubits, which I would like to discuss with the iTHEMS community.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Tensor networks for QCD in the strong-coupling expansion

April 2 (Thu) 15:30 - 17:00, 2026

Tilo Wettig (Professor, Universität Regensburg, Germany)

We present the order-separated Grassmann higher-order tensor renormalization group (OS-GHOTRG) method for QCD with staggered quarks in the strong-coupling expansion. Themethod allows us to determine the expansion coefficients of the partition function, from which we can obtain the strong-coupling expansions of thermodynamical observables. We use the method in two dimensions to compute the free energy, the particle-number density, and the chiral condensate as a function of the chemical potential up to third order in the inverse coupling 𝛽. Although the expansion itself is only a good approximation to the full theory at small 𝛽, we show that in the vicinity of the phase transition the range of applicability can be greatly extended by fits to judiciously chosen transition functions. These fits also yield a valuable expansion of the critical chemical potential in 𝛽. https://www.uni-regensburg.de/physics/hep/people/professors/wettig/index.html

Venue: Seminar Room #359, Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Scalable Simulation of Quantum Many-Body Dynamics with Or-Represented Quantum Algebra

February 4 (Wed) 14:30 - 16:00, 2026

Lukas Broers (Postdoctoral Researcher, Computational Materials Science Research Team, RIKEN Center for Computational Science (R-CCS))

High-performance numerical methods are essential for advancing quantum many-body physics, as well as for enabling the integration of supercomputers with emerging quantum computing platforms. We have developed a scalable and general-purpose numerical framework for quantum simulations based on or-represented quantum algebra (ORQA). This framework applies to arbitrary spin-systems and naturally integrates with quantum circuit simulation in the Heisenberg picture, particularly relevant to recent large-scale experiments on superconducting qubit processors [Kim et al., Nature 618, 500 (2023)]. As a benchmark, we simulate the kicked Ising model on a 127-qubit heavy-hexagon lattice, successfully tracking the time-evolution of local magnetization using up to one trillion Pauli strings. Our simulations exhibit strong scaling up to 2^17 parallel processes with near-linear communication overhead. Further, we show that our framework is naturally extended to a broader range of quantum systems, superseding the capabilities of recently established Pauli propagation methods. We present possible future directions on how to utilize our algorithm.

Venue: via Zoom / Seminar Room #359

Event Official Language: English

From Wavefunction Sparsity to Quantum Filter-Assisted Subspace Diagonalization

February 4 (Wed) 13:00 - 14:30, 2026

Han Xu (Postdoctoral Researcher, Computational Materials Science Research Team, RIKEN Center for Computational Science (R-CCS))

Subspace diagonalization techniques based on quantum sampling, such as quantum selected configuration interaction (QSCI) and sample-based quantum diagonalization (SQD), are a class of quantum-centric algorithms for approximating ground-state energies of many-body systems. One of the foundational bottlenecks for SQD is due to the lack of compactness of the ground-state wavefunctions. In this talk, we will introduce a filter-assisted SQD protocol that enhances the wavefunction sparsity through a quantum-circuit transformation of the Hamiltonian. Using the Gini coefficient as a robust sparsity measure, we clarify how sparsity determines the resource requirements of SQD. To construct the quantum filter, we develop a tensor-network-based automatic circuit-encoding algorithm that encodes the target matrix product states with controllable fidelity. We benchmark the method on the quantum Ising model under the transverse and longitudinal fields, using both numerical simulations and experiments on IBM quantum hardware. Our results show that the filter-assisted protocol reduces energy-estimation errors by orders of magnitude and substantially lowers the overhead of measurement compared with standard SQD, which highlight the potential of filter-assisted protocol in quantum-centric computing for strongly correlated materials.

Venue: via Zoom / Seminar Room #359

Event Official Language: English

RIKEN Quantum hands-on seminar on IBM and QEDMA software for quantum computing beginners

December 15 (Mon) 9:30 - 12:20, 2025

This seminar will be conducted in a hybrid format, both in-person and via Zoom. Since it includes hands-on sessions, we kindly ask you to consider attending in person whenever possible to ensure more effective learning. The overview of the seminar is as follows: Program: 9:30 - 10:50 Yuri Kobayashi (IBM Quantum) "Introduction to IBM Quantum and Qiskit" 10:50 - 11:00 Break 11:00 - 12:20 Ori Alberton (QEDMA) "Introduction to QESEM error mitigation software: Theory,  use-case demonstrations and usage tutorial" Abstract of Yuri Kobayashi's tutorial: This talk introduces HPC researchers to the IBM Quantum platform and the Qiskit SDK, providing a practical orientation to quantum programming without assuming prior quantum-computing knowledge. Attendees will learn how to construct and execute quantum circuits using Qiskit, explore available simulators, and real quantum backends and submit jobs to IBM’s cloud-based quantum processors. The session will also showcase how to map your problem to quantum circuits through specific use-case applications. Abstract of Ori Alberton's tutorial: This talk introduces QESEM, Qedma’s characterization-based software tool for reliable, high-accuracy quasi-probabilistic error mitigation. We will begin by explaining why error mitigation methods are expected to be the first to unlock quantum advantage, and why they will continue to play a central role even as error correction becomes feasible. We will highlight some of the key innovations underlying QESEM’s operation and present recent results on IBM Heron r2 devices demonstrating its capabilities in the largest utility-scale unbiased error mitigation experiment to date. In addition different use-cases and demonstrations ran by QESEM users will be presented. Finally, we will provide guidance on how to start using QESEM to obtain error-mitigated results in experiments on IBM quantum systems.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Computational Problems in Post-Quantum Cryptography

July 7 (Mon) 15:30 - 17:00, 2025

Tsuyoshi Takagi (Professor, Department of Mathematical Informatics, The University of Tokyo)

The security of current public-key cryptosystems relies on the hardness of factoring large integers or solving discrete logarithm problems. However, these computational problems can be solved in polynomial time using a quantum computer. This vulnerability has prompted research into post-quantum cryptography (PQC) using alternative mathematical problems that are secure in the era of quantum computers. In this talk, we give an overview of recent developments in the research on PQC. We explain a standardization project of PQC conducted by the National Institute of Standards and Technology (NIST). We then introduce an efficient digital signature, QR-UOV, based on the hardness of solving a system of multivariate quadratic polynomial equations over a finite field (the MQ problem). We also introduce a computational challenge problem, Fukuoka MQ Challenge, which aims at evaluating the hardness of the MQ problem with practical parameters.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Extracting particle mass on quantum computers: state preparation and measurement

June 3 (Tue) 11:00 - 12:30, 2025

Xiaoyang Wang (Postdoctoral Researcher, Quantum Mathematical Science Team, Division of Applied Mathematical Science, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

In this seminar, I will introduce the procedure of extracting particle mass from the ab initio calculation using quantum computers, including two essential steps: state preparation and measurement. For the measurement process, in our recent work "Computing n-time correlation functions without ancilla qubits" [arXiv:2504.12975], we developed a measurement method for correlation functions without ancilla qubits, circumventing longstanding hardware constraints of limited qubit connectivity and short-range control operations. We demonstrate our method using IBM quantum hardware and successfully reproduce the noiseless results of the Schwinger model hadron mass within a relative error of 0.18%, even in the presence of realistic hardware limitations and noise. For the state preparation process, another work "Performance guarantees of light-cone variational quantum algorithms for the maximum cut problem" [arXiv:2504.12896] focused on the accuracy of the state preparation using variational quantum algorithms (VQAs). We propose a light-cone VQA with provable performance guarantees, whose single round has higher accuracy than the 3-round standard VQA for the maximum cut problem. We experimentally validated the single-round light-cone VQA using IBM quantum hardware with solution accuracy that exceeds the known classical hardness threshold in both a 72-qubit demonstration and a 148-qubit demonstration.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

RIKEN Quantum hands-on workshop on QURI SDK for creating and executing quantum algorithms on various quantum computers and simulators

March 13 (Thu) 15:00 - 17:30, 2025

This workshop will be a hands-on session on QURI SDK, following the RIKEN Quantum seminar by Andreas Thomasen (QunaSys) on January 27. Even if you did not attend the previous seminar, please join us if you would like to learn how to use QURI SDK.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Quantum-Centric Supercomputing Software

February 26 (Wed) 13:00 - 14:00, 2025

Hanhee Paik (Head of IBM Quantum Japan, IBM Quantum)

A quantum-centric supercomputer represents the next generation of computing, combining a quantum computer with a classical supercomputer. It leverages error mitigation and error correction techniques to deliver results within practical timeframes. When fully developed, this system relies on advanced middleware to seamlessly integrate quantum circuits with classical computing resources. In this presentation, we will introduce IBM Quantum’s middleware for quantum-centric supercomputers, highlighting collaborative projects with our research partners.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Architectures and algorithms for early FTQC

January 27 (Mon) 16:00 - 17:15, 2025

Andreas Thomasen (R&D Engineer, QunaSys Inc.)

The NISQ era of quantum computing is characterized by quantum devices that have low error rates, but no error correction and typically on the order of 100 qubits, whereas the era of FTQC requires devices with full error correction facilitated by hundreds of thousands to millions of qubits for every logical qubit. Due to the distinct requirements and operating characteristics of these devices, algorithms and applications supported by NISQ and FTQC respectively are highly distinct as well. However, this leaves a large gap, both in terms of devices, algorithms and applications which exist in the intermediate regime where partial error correction is possible and the devices support on the order of tens of thousands of qubits. In this seminar we will give a brief introduction to quantum computing for a non-specialist audience. We will then describe device architectures and algorithms that are specifically designed to fill this gap during the so-called early FTQC era. We will present the space-time efficient analogue rotation (STAR) architecture together with some algorithms that are well supported by it, namely quantum selected configuration interaction (QSCI) and statistical phase estimation (SPE). This seminar serves as theoretical background for our QURI SDK hands-on session at a later date. The algorithms introduced are directly available as OSS as described in the link below.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Simulating Parton Fragmentation on Quantum Computers

December 11 (Wed) 13:30 - 15:00, 2024

Tianyin Li (Ph.D. Student, Institute of Quantum Matter, South China Normal University, China)

Parton fragmentation functions (FFs) are indispensable for understanding processes of hadron production ubiquitously existing in high-energy collisions, but their first principle determination has never been realized due to the insurmountable difficulties in encoding their operator definition using traditional lattice methodology. We propose a framework that makes a first step for evaluating FFs utilizing quantum computing methodology. The key element is to construct a semi-inclusive hadron operator for filtering out hadrons of desired types in a collection of particles encoded in the quantum state. We illustrate the framework by elaborating on the Nambu-Jona-Lasinio model with numeral simulations. Remarkably, We show that the semi-inclusive hadron operator can be constructed efficiently with a variational quantum algorithm. Moreover, we develop error mitigation techniques tailed for accurately calculating the FFs in the presence of quantum noises. Our work opens a new avenue for investigating QCD hadronization on near-term quantum computers.

Venue: Hybrid Format (3F #359 and Zoom), Seminar Room #359

Event Official Language: English

Quantum Computing in Omics Medicine

May 10 (Fri) 16:00 - 17:15, 2024

Tatsuhiko Tsunoda (Professor, Department of Biological Sciences, Graduate School of Science, The University of Tokyo)

(The speaker is also the team leader of Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences. This is a joint seminar with the iTHEMS Biology Group.) In medical science, the recent explosive development of omics technologies has enabled the measurement not only of bulk data from entire tissues, but also data for individual cells and their spatial location information, and even allowed collection of such information in real-time. Meaningful interpretation of these rich data requires an ability to understand high-order and complex relationships that underpin biological phenomena such as drug response, simulating their dynamics, and selecting the optimal treatment for each patient based on these results. While these data are large-scale and of ultra-high dimensionality, they are also often sparse, with many missing values in the measurements and frequent higher-order interactions among variables, making them hard to handle with conventional statistics. To make further progress, machine learning – especially deep learning – is emerging as one of the promising ways forward. We have developed a method to transform omics data into an image-like representation for analysis with deep learning (DeepInsight) and have successfully used it to predict drug response and to identify original cell types from single-cell RNA-seq data. However, anticipation of the vast amount of medical data being accumulated gives particular urgency to addressing the problems of the time it actually takes to train deep learning models and the complexity of the necessary computational solutions. One possible way to resolve many of these problems is “quantum transcendence”, which is made possible by quantum superposition computation. Among all the different ways to apply quantum computation to medical science, we are particularly interested in quantum deep learning based on optimization and search problems, quantum modeling of single nucleotide detection by observational systems and statistical techniques such as regression analysis by inverse matrix computation and eigenvalue computation. In this seminar, I will first present an overview of how quantum machine learning and quantum deep learning can be used to formulate treatment strategies in medicine. We will discuss how to implement the quantum DeepInsight method, the challenges of noise in quantum computation when training QCNNs, feature mapping issues, problems of pretraining in quantum deep learning, and concerns relating to handling sensitive data such as genomic sequences. I hope this seminar will enhance our understanding of how to effectively facilitate medical research with quantum computing.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Quantum Fine-Grained Complexity

April 18 (Thu) 10:30 - 12:00, 2024

Harry Buhrman (Chief Scientist for Algorithms and Innovation, Quantinuum, UK)

(The speaker is also a professor at University of Amsterdam & QuSoft. This is a joint seminar with the iTHEMS Quantum Computation Study Group.) One of the major challenges in computer science is to establish lower bounds on the resources, typically time, that are needed to solve computational problems, especially those encountered in practice. A promising approach to this challenge is the study of fine-grained complexity, which employs special reductions to prove time lower bounds for many diverse problems based on the conjectured hardness of key problems. For instance, the problem of computing the edit distance between two strings, which is of practical interest for determining the genetic distance between species based on their DNA, has an algorithm that takes O(n^2) time. Through a fine-grained reduction, it can be demonstrated that a faster algorithm for edit distance would imply a faster algorithm for the Boolean Satisfiability (SAT) problem. Since faster algorithms for SAT are generally considered unlikely to exist, this implies that faster algorithms for the edit distance problem are also unlikely to exist. Other problems used for such reductions include the 3SUM problem and the All Pairs Shortest Path (APSP) problem. The quantum regime presents similar challenges; almost all known lower bounds for quantum algorithms are defined in terms of query complexity, which offers limited insight for problems where the best-known algorithms take super-linear time. Employing fine-grained reductions in the quantum setting, therefore, represents a natural progression. However, directly translating classical fine-grained reductions to the quantum regime poses various challenges. In this talk, I will present recent results in which we overcome these challenges and prove quantum time lower bounds for certain problems in BQP, conditioned on the conjectured quantum hardness of, for example, SAT (and its variants), the 3SUM problem, and the APSP problem. This presentation is based on joint works with Andris Ambainis, Bruno Loff, Florian Speelman, and Subhasree Patro.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Bayesian mechanics of classical, neural, and quantum systems

January 17 (Wed) 16:30 - 17:45, 2024

Takuya Isomura (Unit Leader, Brain Intelligence Theory Unit, RIKEN Center for Brain Science (CBS))

(This is a joint seminar with iTHEMS Biology group.) Bayesian mechanics is a framework that addresses dynamical systems that can be conceptualised as Bayesian inference. However, the elucidation of requisite generative models is required for empirical applications to realistic self-organising systems. This talk introduces that the Hamiltonian of generic dynamical systems constitutes a class of generative models, thus rendering their Helmholtz energy naturally equivalent to variational free energy under the identified generative model. The self-organisation that minimises the Helmholtz energy entails matching the system's Hamiltonian with that of the environment, leading to an ensuing emergence of their generalised synchrony. In short, these self-organising systems can be read as performing variational Bayesian inference of the interacting environment. These properties have been demonstrated with coupled oscillators, simulated and living neural networks, and quantum computers. This notion offers foundational characterisations and predictions regarding asymptotic properties of self-organising systems exchanging with the environment, providing insights into potential mechanisms underlying emergence of intelligence.

Venue: Seminar Room #359 (Main Venue) / via Zoom

Event Official Language: English

Methods for neural decoding using machine learning, deep learning, and quantum-inspired algorithms

January 17 (Wed) 15:00 - 16:15, 2024

Kei Majima (Researcher, National Institutes for Quantum Science and Technology (QST))

Note: The format of this event has changed from hybrid to Zoom only. However, you will still be able to watch it on the screen in Room #359 of the Main Research Building. (This is a joint seminar with iTHEMS Biology group.) Recent advances in machine learning have enabled the extraction of intrinsic information from neural activities, a field known as neural decoding. In this presentation, I will introduce several machine learning methods recently developed for neural decoding analysis: 1) a method for visualizing subjective images in the human mind based on brain activity [1], 2) a supervised algorithm designed for predicting discrete ordinal variables [2], and 3) a fast classical algorithm algorithm inspired by quantum computation for approximating principal component analysis (PCA) and canonical correlation analysis (CCA), potentially allowing for the analysis of vast-dimensional neural data [3]. Following these presentations, I am eager to engage in discussions with participants at the RIKEN Quantum Seminar regarding potential collaborations.

Venue: via Zoom

Event Official Language: English