Zixy
Zixy is a high performance library for the manipulation of Pauli strings and other quantum algebraic objects.
Installation
From PyPI
The package is available via PyPI and can be installed using the preferred package manager,
such as pip.
pip install zixy
From source
Developers may wish to install from source.
The recommended method is by using maturin from the top level directory.
git clone https://github.com/quantinuum/zixy
cd zixy
maturin develop
Usage
Overview
The package is written in Rust, with Python bindings enabled via PyO3. The base containers in the Python interface are defined in a general fashion. The user-facing API separates these objects into three important classes:
Coeff: a coefficient, defining a scalar value of varying types.Cmpnt: a component, defining a basic algebraic building block.Term: a term, consisting of aCmpntscaled by aCoeff.
Coefficients are separated into built-in Python types such as float or complex, symbolic sympy expressions,
and roots of unity such as Sign and ComplexSign,
which have custom implementations.
Additionally, each such class has associated containers for collections of that type. Implementations of these base classes may narrow their implementation depending on the coefficient type.
Example
These general building blocks are sufficient to define many different quantum algebraic objects.
An example of one such specification of the objects in Zixy is Pauli strings.
These are made available in the zixy.qubit.pauli submodule,
with the qubits forming a basis for their definition available in zixy.qubit.
import zixy.qubit as zq
import zixy.qubit.pauli as zqp
qubits = zq.Qubits.from_count(4)
print(len(qubits)) # 4
strings = zqp.Strings.from_str("X0 Y1 Z3", qubits)
print(str(strings)) # X0 Y1 Z3
terms = zqp.RealTerms.from_str("X0 Y1 Z3")
print(str(terms)) # (1, X0 Y1 Z3)
For other specifications such as computational basis vectors, further example notebooks are available.
Rust
The trait NumRepr (meaning "number representation") is implemented by all types that can represent the numbers multiplying components.
In collections, these are stored contiguously in vector-like structures.
Values of NumRepr types are closed under multiplication, but do not implement addition.
Implementers of NumRepr are
Unity: a unit type representing+1and only+1. The corresponding collection containerUnityVeconly stores a length, the number of unit values.Sign: the square roots of unity (+1,-1). The corresponding collection container,SignVecis implemented as a bitset.ComplexSign: the fourth roots of unity (+1,+i,-1,-i). The corresponding collection container,ComplexSignVecis implemented as a paired bitset.
The FieldElem subtrait of NumRepr is implemented by types whose values form a field with multiplication and addition.
Implementers of both NumRepr and FieldElem use Vec<T> for collection storage, and are
Real: Floating point valuef64Complex: Complex floating point valueComplex64fromnum_complexcrate.
The Python interface further supports symbolic types via SymPy, which are not currently available within the Rust library.
On the Rust side, components are stored contiguously in CmpntList types.
These implement the business logic applicable to their elements.
There are three main kinds of container that encapsulate CmpntList with generic coefficient type C
Terms<C: NumRepr>: stores aC::Vectorof the same length as theCmpntList, offers mutable access to the components and coefficients.TermSet<C: NumRepr>: stores aC::Vectorof the same length as theCmpntListand via an indirect hash map, provides constant complexity lookup and enforces uniqueness among components. Offers mutable access only to the coefficients.TermSum<C: FieldElem>: works likeSet<C>but with linear combination semantics and is only defined forFieldElem-implementing coefficient types.
3 maintainers
Metadata
3 maintainers
Release files for zixy 0.4.3
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