Efficient Quantum Counting and Quantum Content-Addressable Memory for DNA similarity

TL;DR

This paper introduces QCAM, a quantum content-addressable memory system optimized with Grover's algorithm and advanced circuit design, enabling efficient DNA sequence similarity analysis through quantum computing.

Contribution

The paper presents a novel quantum CAM architecture with optimized circuits and a hardware-efficient quantum counting method for DNA similarity measurement.

Findings

QCAM effectively finds matches in DNA sequences using quantum algorithms.

The proposed circuits reduce depth through parallel controlled rotations.

Quantum counting accurately infers the number of matches with minimal measurements.

Abstract

We present QCAM, a quantum analogue of Content-Addressable Memory (CAM), useful for finding matches in two sequences of bit-strings. Our QCAM implementation takes advantage of Grover's search algorithm and proposes a highly-optimized quantum circuit implementation of the QCAM oracle. Our circuit construction uses the parallel uniformly controlled rotation gates, which were used in previous work to generate QBArt encodings. These circuits have a high degree of quantum parallelism which reduces their critical depth. The optimal number of repetitions of the Grover iterator used in QCAM depends on the number of true matches and hence is input dependent. We additionally propose a hardware-efficient implementation of the quantum counting algorithm (HEQC) that can infer the optimal number of Grover iterations from the measurement of a single observable. We demonstrate the QCAM application for computing the Jaccard similarity between two sets of k-mers obtained from two DNA sequences.