For PK and Lucy,
my best boy and best
girl, whose constant warmth I could never repay. Even with a lifetime of dog treats and scratches. This website
provides a place to share what I enjoy learning.
Using quantum computing, the authors exploit quantum mechanics for the algorithmic complexity optimization of a Support Vector Machine with high-dimensional feature space. Where the high-dimensional classical data is mapped non-linearly to Hilbert Space and a hyperplane in quantum space is used to separate and label the data. By using the... read more
A quantum convolutional neural network circuit can be viewed as a combination of a multi-scale entanglement renormalization ansatz, which is a variational ansatz for many-body wavefunctions, and nested quantum error correction, which detects and corrects local quantum errors without collapsing the wavefunction. We will look at how quantum computing can be... read more
Consider a square lattice \(\mathcal{L}\) in \(D\) spatial dimensions with \(N\) sites, where each site \(s_{1},...,s_{N} \in \mathcal{L}\) is a complex vector space \(\mathbb{V}\) with finite dimension \(\chi\), termed the bond dimension. Site \(s\) can be described by a pure state \(|\Psi\rangle \in \mathbb{V}^{\otimes N}\), which has a reduced density matrix \(\rho^{[s]}=\operatorname{tr}_{\bar{s}}(|\Psi\rangle\langle\Psi|)\). State \(|\Psi\rangle\) can be generated by a quantum circut \(\mathcal{C}\) with depth \(\Theta \equiv 2 \log _2(N)-1\)... read more
Let \(\mathscr{N}\) denote noise, \(\mathscr{R}\) denote the recovery process, and
\(L(H)\) denote the space of linear operators on \(H\). The noise and recovery processes
can be described as quantum operations \(\mathscr{N}, \mathscr{R}: L(H) \rightarrow L(H)\),
from where the mapping can be represented as an operator-sum such as:
\(\mathscr{N}(\rho)=\sum_i E_i \rho E_i^{\dagger},\) \(\quad E_i \in L(H),\) \(\quad
\mathscr{N}=\left\{E_i\right\}\)
Given the quantum code \(C_{Q}\), a subspace of Hilbert space \(H\), there exists a
recovery
...read more
The formula in the case of \(SU(2)\) is as follows. Choose a polynomial \(Q\). For each \(k \in
\mathbb{Z}_{+}\), let
\(\xi(k, t)\) be the formal power series in \(t\) that represents a unique critical point of the
function in \(\xi\).
\(F(\xi ; h, k, t)\) \(:=\frac{1}{2}(\xi-k)^2+t \cdot \frac{h}{2 \pi^2} \cdot Q(\pi \xi
/ h)\)
where, \(t\) is treated as a formal variable. \(\xi(k,t)\) undergoes \(t\)-series expansion near the minimum
\(\xi(k,0)=k\), formulated as:
\(\displaystyle{ \int_{F(\Sigma ; G)} \exp \left\{h \omega+t \cdot[\Sigma] \backslash
Q\left(u^* \phi_2\right)\right\} }\)
\(\displaystyle{ =\# Z(G) \cdot h^{3 g-3} \operatorname{vol}(G)^{2 g-2} }\)
\(\displaystyle{ \sum_{k>0}\left[\frac{1+\frac{t}{2 h} Q^{\prime \prime}(\xi(k, t))}{\xi(t,
k)^2}\right]^{g-1} }\)
such that, \(\#Z(G)=2\) for \(SU(2)\)...
read more
A Riemannian manifold is a smooth manifold \(M\) with a Riemannian metric denoted \((M,g)\). For every point
\(p\in M\) the tangent bundle of \(M\) assigns a vector space \(T_{p}M\), termed the tangent space of \(M\)
at
\(p\). The Riemanniam metric defines a positive-definite inner product:
\(g_p: T_p M \times T_p M \rightarrow \mathbb{R}\)
with a norm \(|\cdot|_p: T_p M \rightarrow \mathbb{R}\) defined as:
\(|v|_p=\sqrt{g_p(v, v)}\)
...read more
The thalamic nuclei are paired structures of the thalamus divided into three main groups: the lateral nuclear, medial nuclear, and anterior nuclear groups. The internal medullary lamina, a Y-shaped structure that splits these groups, is present on each side of the thalamus. A midline, thin thalamic nuclei, adjacent to the... read more