  Quantum Random Access Memory (QRAM) is a critical component for loading
classical data into quantum computers. While constructing a practical QRAM
presents several challenges, including the impracticality of an infinitely
large QRAM size and a fully error-correction implementation, it is essential to
consider a practical case where the QRAM has a limited size. In this work, we
focus on the access of larger data sizes without keeping on increasing the size
of the QRAM. Firstly, we address the challenge of word length, as real-world
datasets typically have larger word lengths than the single-bit data that most
previous studies have focused on. We propose a novel protocol for loading data
with larger word lengths $k$ without increasing the number of QRAM levels $n$.
By exploiting the parallelism in the data query process, our protocol achieves
a time complexity of $O(n+k)$ and improves error scaling performance compared
to existing approaches. Secondly, we provide a data-loading method for
general-sized data access tasks when the number of data items exceeds $2^n$,
which outperforms the existing hybrid QRAM+QROM architecture. Our method
contributes to the development of time and error-optimized data access
protocols for QRAM devices, reducing the qubit count and error requirements for
QRAM implementation, and making it easier to construct practical QRAM devices
with a limited number of physical qubits.
