「第56回Q-LEAP量子AIセミナー」のお知らせです。
今回は、東京大学の山崎隼汰さんによる量子機械学習に関するオンラインセミナーを行います。使用言語は日本語です。
参加を希望される方は下記の参加登録フォームよりご登録をお願いします。
- 日時： 2024年2月6日（火） 13:00～14:00
- 場所： オンライン（ZOOM）
- 講演タイトル： 量子計算の一般的な優位性に基づく量子機械学習の優位性
- 講師： 山崎 隼汰 氏（東京大学大学院 理学系研究科物理学専攻 助教）
概要：
An overarching milestone of quantum machine learning (QML) is to demonstrate the advantage of QML over all possible classical learning methods in accelerating a common type of learning task as represented by supervised learning with classical data. However, the provable advantages of QML in supervised learning have been known so far only for the learning tasks designed for using the advantage of specific quantum algorithms, i.e., Shor’s algorithms. Here we explicitly construct an unprecedentedly broader family of supervised learning tasks with classical data to offer the provable advantage of QML based on general quantum computational advantages, progressing beyond Shor’s algorithms. Our learning task is feasibly achievable by executing a general class of functions that can be computed efficiently in polynomial time for a large fraction of inputs by arbitrary quantum algorithms but not by any classical algorithm. We prove the hardness of achieving this learning task for any possible polynomial-time classical learning method. We also clarify protocols for preparing the classical data to demonstrate this learning task in experiments. These results open routes to exploit a variety of quantum advantages in computing functions for the experimental demonstration of the advantage of QML. The talk is based on the following paper.
https://arxiv.org/abs/2312.03057
本セミナーシリーズは量子AIやその周辺分野に関する最近の研究内容などを共有するために企画した、オープンなセミナーです。
皆さまのご参加をお待ちしています。