# Design draft: data layer separation

This is an early design document about the separation of data layer in QuTiP as part of my Google Summer of Code project. This is a very early-stage document, which is significantly liable to change, but is indicative of the direction we were planning to go.

Originally written on the 1st of June, 2020.

## Definitions

• Low-level components: parts of QuTiP which necessarily interact with the underlying data representations. Typically these components will access Qobj.data and munge the fast_csr_matrix properties, for example state creation.
• High-level components: parts of QuTiP which interact only with Qobj as abstract objects, for example the qip package.
• accelerated method: a method which takes only one particular data-layer implementation, rather than acting abstractly on all data-layer objects.

## Data layer

There will be a separated data layer, which will expose operations that are guaranteed to succeed on all data layer objects. In order to achieve this, the data layer will have two facets—the library-facing side expected to be used by low-level QuTiP components, and the data-layer internal side which will implement dispatch and implicit casting rules for all operations.

### Data layer internals

A core set of operations must be provided with concrete implementations by any new data-layer implementation. This is likely to include

• cast to np.ndarray
• construct from np.ndarray
• copy self
• multiplication by scalar
• matrix multiplication by the same type
• addition of the same type
• equality to the same type

In order to allow these implementations to be added to a large package like QuTiP, this minimal required set should be kept as small as is absolutely possible. We do not want a new implementation to have to fully implement the entire set of linear algebra operations used in QuTiP, because this will make it exceptionally difficult to add further capabilities.

In addition, there will be many other functions that exist on the data layer. Examples are

• conjugation
• transpose
• trace
• matrix exponential
• partial trace

and many many others. At first, these will not need to be implemented by a concrete implementation. Instead, the data layer will expose the methods, and upon failing to find a suitable method in the dispatch tables, will convert to the reference type (say, np.ndarray), perform the operation, and convert back. This is not intended to be fast, it is intended to ensure that the operations will always succeed. We can issue a DEBUG logging statement or an EfficiencyWarning when this happens for internal development. This extends to far more complex methods. If the data layer has a method, it is guaranteed to work on types which implement the minimal interface.

However, we also care about speed. Instances may also implement many, or all of the additional methods. In this case, they will register an accelerated method with the data-layer dispatcher, and no casting will happen. These underlying implementations will still be available to be called by other underlying implementations of the same type, without having to pass-through the dispatch layer.

I have used np.ndarray as the reference type here. While we are initially effecting the switch over, it may be simpler to use fast_csr_matrix as the underlying type, since all methods are already written for it. In the future, we can swap to the conceptually simple ndarray if that is desirable.

### Low-level QuTiP components

Just because we have separated out a data layer doesn’t mean that low-level components will not want to provide accelerated methods if certain underlying representations are in use. If a particular method is expected to be used throughout QuTiP, it may be prudent to register it on the data layer, but there are plenty which will not be.

Let’s take qutip.destroy as an example. This is common enough that it should have accelerated methods, but it is not itself a data-layer method.

To start, we would declare qutip.destroy as being an accelerated-dispatch method, with a default ndarray form (NOTE: the form of the dispatch decorator is wildly subject to change right now):

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8from . import Qobj, data

def _destroy_ndarray(size):
return np.diag(np.sqrt(np.arange(1, size)), 1)

@data.dispatch(default=(data.ndarray, _destroy_ndarray))
def destroy(size: int) -> Qobj:
"""docstring"""
We may then also register further methods, say for fast_csr_matrix:
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5def _destroy_fast_csr_matrix(size):
...
return out

destroy.register(type=data.fast_csr_matrix, method=_destroy_fast_csr_matrix)
We would then call qutip.destroy simply as qutip.destroy(3) or qutip.destroy(3, type=data.fast_csr_matrix). The default output type could also be controlled by global QuTiP settings.

If we had a third data layer type, say data.tensorflow (or whatever), which does not have the method defined, then the dispatch would do something equivalent to

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7def dispatch(self, datatype, args, kwargs):
if datatype in self._dispatch:
method = self._dispatch[datatype]
else:
warnings.warn("No accelerated method exists.", EfficiencyWarning)
method = self._dispatch[self._default]
return data.cast(method(*args, **kwargs), datatype)
where data.cast is a data-layer-internal dispatcher which will call an accelerated method to cast a data-layer object to the correct type, or go via np.ndarray if no accelerated method exists.

Important points:

1. I don’t actually need to provide a body for destroy, because the dispatch table will fill it in. It’s convenient to do a proper def for a function so it’s easier to declare a docstring and type hints.
2. _destroy_ndarray does not construct the Qobj, because the dispatch will do it for us
3. Rules on casting, how defaults are handled and everything like this may be the subject of global settings.

## Changes to Qobj

Currently Qobj is inherently tied to the fast_csr_matrix implementation, and various components all over QuTiP assume that Qobj.data will always be an instance of fast_csr_matrix. In order for us to seamlessly use multiple implementations of the underlying matrix structures, there must be a decoupling between Qobj, which represents abstract quantum objects, and the data layer which implements concrete operations.

In general, we expect that the majority of Qobj methods will become fairly simple pass-throughs to the data layer, and the type of Qobj.data will become an instance of the ABC DataLayer, i.e. with few implementation guarantees.

High-level QuTiP components will automatically be able to use any underlying data representation which fulfils the data model, as they only access Qobj methods.

Low-level methods will be changed to go via the data layer or use accelerated methods, as described in the previous sections.

## Package organisation

All of the data layer and Qobj will exist in a new subpackage, qutip.core. The first order of business of the conversion will be to separate out this package, and update all references in QuTiP to use it.

The next most important step is to define the data layer interface, and ensure that the dispatch works. The dispatch in particular will allow QuTiP to continue functioning, even while new accelerated methods are being written for ndarray.