「量子アニーリングの基礎」を読む
https://qiita.com/kaizen_nagoya/items/29580dc526e142cb64e9

量子アニーリングの基礎　西森秀稔, 大関真之, 共立出版, 2018

https://www.amazon.co.jp/dp/4320035380

2019年7月19日から、読書会を毎週第三金曜日に予定しています。
T-QARDの日々量子コンピュータへのお勧めの入口
https://qiita.com/kaizen_nagoya/items/fb869e5f38ae354e6294

参加条件は、上記の動画の一つ以上を見て、感想を16文字以上（言語は問わない）書いて出してくださることです。あわせて、担当の希望（どれか１つの章）を書いていただけると幸いです。
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4

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The agreement of FERL using quantum annealer reads treated as classical Boltzmann samples, with that of FERL using SA and classical Boltzmann machines, suggests that, at least for this task, and this size of Boltzmann machine, the measurements provided by the D-Wave 2000Q can be considered good approximations of Boltzmann distribu- tion samples of classical Ising models.
VI. CONCLUSION
In this paper, we perform free energy-based reinforce- ment learning using existing quantum hardware, namely the D-Wave 2000Q. Our methods rely on Suzuki–Trotter decomposition and realization of the measured configu- rations by the quantum device as replicas of an effective classical Ising model of one dimension higher. Future research can employ these principles to solve larger-scale reinforcement learning tasks in the emerging field of quan- tum machine learning.
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􏰄 e−Ei
Z |i⟩⟨i|, (B1)
where Z = 􏶣i e−Ei is the normalization constant. Then, plug this expansion into the intractable expression and use Jensen’s inequality
ln⟨u|ρ|u⟩
ρ=
9
i
􏰄 e−Ei = ln⟨u| Z
|i⟩⟨i|u⟩ = ln 􏰄 |⟨i|u⟩|2 e−Ei
i
Z
≥ 􏰄 |⟨i|u⟩|2 ln e−Ei (B2)
i
i
Z
|i⟩⟨i|u⟩
􏰄 e−Ei = ⟨u| ln Z
i
= ⟨u| ln ρ|u⟩,
where |⟨i|u⟩|2 are probabilities and sum up to 1.
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