$T$-depth-optimized Quantum Search with Quantum Data-access Machine

TL;DR

This paper introduces a quantum data-access machine (QDAM) that enables efficient quantum search with logarithmic T-depth complexity, significantly improving runtime performance in fault-tolerant quantum computing.

Contribution

The paper presents a novel quantum data-access architecture (QDAM) and analyzes its impact on quantum search efficiency, incorporating T-depth complexity considerations in fault-tolerant quantum computation.

Findings

QDAM achieves O(log N) T-depth growth for N data points.

Quantum search with QDAM requires O(√N × log N) runtime.

QDAM-based search outperforms classical methods in speed.

Abstract

Quantum search algorithms offer a remarkable advantage of quadratic reduction in query complexity using quantum superposition principle. However, how an actual architecture may access and handle the database in a quantum superposed state has been largely unexplored so far; the quantum state of data was simply assumed to be prepared and accessed by a black-box operation -- so-called oracle, even though this process, if not appropriately designed, may adversely diminish the quantum query advantage. Here, we introduce an efficient quantum data-access process, dubbed as quantum data-access machine (QDAM), and present a general architecture for quantum search algorithm. We analyze the runtime of our algorithm in view of the fault-tolerant quantum computation (FTQC) consisting of logical qubits within an effective quantum error correction code. Specifically, we introduce a measure involving two computational complexities, i.e. quantum query and $T$-depth complexities, which can be critical to assess performance since the logical non-Clifford gates, such as the $T$ (i.e., $\pi/8$ rotation) gate, are known to be costliest to implement in FTQC. Our analysis shows that for $N$ searching data, a QDAM model exhibiting a logarithmic, i.e., $O(\log{N})$, growth of the $T$-depth complexity can be constructed. Further analysis reveals that our QDAM-embedded quantum search requires $O(\sqrt{N} \times \log{N})$ runtime cost. Our study thus demonstrates that the quantum data search algorithm can truly speed up over classical approaches with the logarithmic $T$-depth QDAM as a key component.