QIQB(大阪大学先導的学際研究機構量子情報?量子生命研究部門)セミナー でのスライドを加筆したもの。量子コンピュータを用いた量子化学計算の現在の状況と展望を述べた.
伝統的なゲート式位相推定による方法とvariational eigen solverによるものと2つ。ごく最近虚時間発展法の実装もされており、それは別スライドで概観した。
Documentul include o serie de puncte bifeaz? ?i aspecte numerotate, suger?nd o list? de criterii sau elemente organizate. Exist? referin?e repetate la diverse numere care pot reprezenta date, valori sau categorii. Informa?iile sunt formate din marcaje ?i simboluri, ceea ce sugereaz? o structur? tehnic? sau un formular standardizat.
This document discusses various methods for calculating Wasserstein distance between probability distributions, including:
- Sliced Wasserstein distance, which projects distributions onto lower-dimensional spaces to enable efficient 1D optimal transport calculations.
- Max-sliced Wasserstein distance, which focuses sampling on the most informative projection directions.
- Generalized sliced Wasserstein distance, which uses more flexible projection functions than simple slicing, like the Radon transform.
- Augmented sliced Wasserstein distance, which applies a learned transformation to distributions before projecting, allowing more expressive matching between distributions.
These sliced/generalized Wasserstein distances have been used as loss functions for generative models with promising
Documentul include o serie de puncte bifeaz? ?i aspecte numerotate, suger?nd o list? de criterii sau elemente organizate. Exist? referin?e repetate la diverse numere care pot reprezenta date, valori sau categorii. Informa?iile sunt formate din marcaje ?i simboluri, ceea ce sugereaz? o structur? tehnic? sau un formular standardizat.
This document discusses various methods for calculating Wasserstein distance between probability distributions, including:
- Sliced Wasserstein distance, which projects distributions onto lower-dimensional spaces to enable efficient 1D optimal transport calculations.
- Max-sliced Wasserstein distance, which focuses sampling on the most informative projection directions.
- Generalized sliced Wasserstein distance, which uses more flexible projection functions than simple slicing, like the Radon transform.
- Augmented sliced Wasserstein distance, which applies a learned transformation to distributions before projecting, allowing more expressive matching between distributions.
These sliced/generalized Wasserstein distances have been used as loss functions for generative models with promising
14. Gauss曲率
Gauss曲率 K := κ1κ2
K =
LN ? M2
EG ? F2
= det
(
MIIM?1
I
)
※ K の符号で曲面の形がわかる.
15. 例(トーラス)1
トーラスのパラメータ表示
p(u, v) =
?
?
(R + r cos u) cos v
(R + r cos u) sin v
r sin u
?
?
E = r2
F = 0 G = (R + r cos u)2
L = r M = 0 N = (R + r cos u) cos u
27. 例(トーラス)
p(u, v) =
?
?
(R + r cos u) cos v
(R + r cos u) sin v
r sin u
?
?
[
θ1
θ2
]
=
[
rdu
(R + r cos u)dv
]
ω1
2 = sin udv
K =
cos u
r(R + r cos u)
28. 例(球)
p(u, v) =
?
?
r cos u cos v
r cos u sin v
r sin u
?
?
[
θ1
θ2
]
=
[
rdu
r cos udv
]
ω1
2 = sin udv
K =
1
r2
29. 例(カテノイド)
p(u, v) =
?
?
u cos v
u sin v
cosh?1
u
?
?
[
θ1
θ2
]
=
[
udu/
√
u2 ? 1
udv
]
ω1
2 = ?
√
u2 ? 1dv/u
K = ?1/u4
30. 例(擬球)
p(u, v) =
?
?
e?u
cos v
e?u
sin v∫ u
0
√
1 ? e?2tdt
?
?
[
θ1
θ2
]
=
[
du
e?u
dv
]
ω1
2 = e?u
dv
K = ?1