# Circuit-Based Quantum Random Access Memory for Classical Data

## TL;DR

This paper introduces a circuit-based quantum random access memory (QRAM) system for efficiently encoding classical data into quantum states, enabling advanced quantum data processing and machine learning applications.

## Contribution

The work presents a systematic, flexible QRAM architecture requiring O(n) qubits and O(Mn) steps for classical data storage, with extensions for continuous data and quantum forking to improve efficiency.

## Key findings

- Efficient encoding of classical data into quantum states demonstrated.
- Method supports both discrete and continuous data storage.
- Quantum forking reduces the number of state preparation queries.

## Abstract

A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of $M$ entries, each represented by $n$ bits, the method requires $O(n)$ qubits and $O(Mn)$ steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.