Efficiency in handling instructions within compilation and control processes is essential for scalability and fault-tolerant quantum computation. To mitigate the limited bandwidth for transmission of instructions and energy bottlenecks in cryogenic control architectures, this thesis aims to develop a compressed representation of quantum circuits. To achieve this goal, we study the concepts of algorithmic information theory and resource theory of computation. We focus on description complexity and establish compression as a useful estimate of algorithmic description complexity. With this motivation, we develop a generalized framework for the synthesis of quantum unitaries into a set of native gates and present a Huffman-encoded representation of the instruction stream that has a short code dictionary and offers a 60% compression over binary encoded representations. The developed framework offers 2 major contributions: an energy-efficient encoded representation of the quantum instruction stream and an estimate of the description complexity for quantum circuits. It qualifies as a successful algorithmic approach towards optimizing the QISA and aids the discovery of high-level quantum programming constructs.