RIKEN Quantumセミナー
6 イベント
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セミナー
Architectures and algorithms for early FTQC
2025年1月27日(月) 16:00 - 17:15
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.
会場: セミナー室 (359号室) 3階 359号室 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Simulating Parton Fragmentation on Quantum Computers
2024年12月11日(水) 13:30 - 15:00
ティエンイン・リー (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.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
Quantum Computing in Omics Medicine
2024年5月10日(金) 16:00 - 17:15
角田 達彦 (東京大学 大学院理学系研究科 生物科学専攻 教授)
(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.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Quantum Fine-Grained Complexity
2024年4月18日(木) 10:30 - 12:00
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.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Bayesian mechanics of classical, neural, and quantum systems
2024年1月17日(水) 16:30 - 17:45
磯村 拓哉 (理化学研究所 脳神経科学研究センター (CBS) 脳型知能理論研究ユニット ユニットリーダー)
(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.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Methods for neural decoding using machine learning, deep learning, and quantum-inspired algorithms
2024年1月17日(水) 15:00 - 16:15
間島 慶 (量子科学技術研究開発機構 (QST) 研究員)
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.
会場: via Zoom
イベント公式言語: 英語
6 イベント