Most commonly, <boundary conditions> such as the <Dirichlet boundary condition> are taken to be fixed values in time.

But it also makes sense to think about cases where those values vary in time.

Some bibliography:
* https://math.stackexchange.com/questions/261251/heat-equation-with-time-dependent-boundary-conditions
* https://secure.math.ubc.ca/~peirce/M257_316_2012_Lecture_20.pdf

Time dependent boundary condition

{tag=Organization developing quantum hardware}


Organization developing trapped ion quantum computer

Water Margin English translation

Schrodinger equation Hamiltonian

SHA-1

The most important ones are:
* <mRNA>
* <tRNA>
* <rRNA>

Type of RNA

Hardware random number generation

$\LP$ is complete

Lp is complete

\LP is complete

Octagon

Pentagon

Git web interface

State of matter

{tag=ZX-calculus}
{title2=qsoft}

2022 page: https://www.cs.ox.ac.uk/teaching/courses/qsoft/ Half of the problems are <Jupyter Notebooks>, not bad.

Like <Quantum Processes and Computation course of the University of Oxford>, also <ZX-calculus>-heavy.

Quantum Software course of the University of Oxford

{wiki=Particle_statistics\#Quantum_statistics}


Quantum statistics

{title2=$\H$}
{title2=4}
{wiki}

Kind of extends the <complex numbers>.

Some facts that make them stand out:
* one of the only three real <associative> <division algebras> in addition to the <real numbers> and <complex numbers>, according to the <classification of associative real division algebras>
* the simplest non-<commutative> <division algebra>. Contrast for example with <complex numbers> where multiplication is <commutative>

Quaternion

Average number of steps until reaching a state of a Markov chain

Barn

Barn (unit)

{c}
{wiki}

Since DNA is the centerpiece of life, <Ciro Santilli> is extremely excited about DNA-related technologies, see also: <molecular biology technologies>.

Hexagon

{wiki}

= Matrix product
{synonym}

Since a <matrix> $M$ can be seen as a <linear map> $f_M(\vec{x})$, the product of two matrices $MN$ can be seen as the composition of two <linear maps>:
$$
f_M(f_N(\vec{x}))
$$
One cool thing about linear functions is that we can easily pre-calculate this product only once to obtain a new matrix, and so we don't have to do both multiplications separately each time.

Ciro Santilli @cirosantilli 40