Quantum Brain
← Back to papers

Automated Quantum Memory Compilation with Improved Dynamic Range

Aviraj Sinha, Elena R. Henderson, Jessie M. Henderson, Mitchell A. Thornton·November 1, 2022·DOI: 10.1109/QCS56647.2022.00008
Computer SciencePhysics

AI Breakdown

Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.

Abstract

Emerging quantum algorithms that process data require that classical input data be represented as a quantum state. These data-processing algorithms often follow the gate model of quantum computing-which requires qubits to be initialized to a basis state, typically| 0 〉-and thus often employ state generation circuits to transform the initialized basis state to a data-representation state. There are many ways to encode classical data in a qubit, and the oft-applied approach of basis encoding does not allow optimization to the extent that other variants do. In this work, we thus consider automatic synthesis of addressable, quantum read-only memory (QROM) circuits, which act as data-encoding state-generation circuits. We investi-gate three data encoding approaches, one of which we introduce to provide improved dynamic range and precision. We present experimental results that compare these encoding methods for QROM synthesis to better understand the implications of and applications for each.

Related Research

Quantum Intelligence

Ask about quantum research, companies, or market developments.