The quantum algorithm execution frameworks currently available on the market (IBM
Qiskit, Google Cirq) implement their computations using unitary matrices of exponential
size in the number of qubits. Consequently, they require large amounts of memory,
even
for small inputs. Although existing frameworks use some optimization methods, these
often cannot provide improvements of an order of magnitude (e.g. sparse matrix storage
mode) or are only applicable in special cases (Clifford gates). In practice, in contrast
to
a large company, the average user cannot experiment within reasonable limits, for
many
algorithms, even with relatively small inputs, as this would incur outstanding hardware
costs.
Algorithms that save memory in exchange for increased runtime can reduce these hardware
expenses. For example, any submatrix of the unitary matrix can be computed on-the-fly
during runtime, or the equivalent conventional algorithm can replace the unitary matrix
operation. Although the currently available frameworks are open-source, they store
the
unitary matrices in memory as an integral part of their architecture, making it impossible
to incorporate these memory optimization techniques.
In my paper, I focus on developing these memory optimization methodologies and implementing
them in a general-purpose quantum algorithm simulation framework. I present
the classical algorithm and architecture design steps that form the basis of the system
and
demonstrate how this system can be used in quantum algorithm research. The framework
is primarily intended to be used in a resource-constrained environment to enable running
tests on a larger number of qubits, thus facilitating theoretical research. Accordingly,
I will
make the system and its documentation available to everyone in an open-source licensed
form.
