Study on Use Cases and Robustness of Quantum Random Access Coding in Quantum Machine Learning

• Item type: SIG Technical Reports(1)
• 公開日: 2020-10-09
• タイトル: Study on Use Cases and Robustness of Quantum Random Access Coding in Quantum Machine Learning
• タイトル / 言語: en
• タイトル: Study on Use Cases and Robustness of Quantum Random Access Coding in Quantum Machine Learning
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: University of Tokyo
• 著者所属: University of Tokyo
• 著者所属: IBM Quantum, IBM Research - Tokyo／Quantum Computing Center, Keio University
• 著者所属(英): en
• 著者所属(英): University of Tokyo
• 著者所属(英): en
• 著者所属(英): University of Tokyo
• 著者所属(英): en
• 著者所属(英): IBM Quantum, IBM Research - Tokyo / Quantum Computing Center, Keio University
• 著者名: Napat, Thumwanit; Chayaphol, Lortararprasert; Rudy, Raymond
• 著者名(英): Napat, Thumwanit; Chayaphol, Lortararprasert; Rudy, Raymond
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12894105
• 書誌情報: 研究報告量子ソフトウェア（QS）; 巻 2020-QS-1, 号 18, p. 1-8, 発行日 2020-10-09
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2435-6492
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会