Squash: A Scalable Quantum Mapper Considering Ancilla Sharing

TL;DR

This paper introduces Squash, a scalable quantum mapping approach that efficiently manages large quantum circuits and ancilla sharing through a multi-core architecture, improving resource utilization.

Contribution

It proposes Squash, a novel quantum mapper that divides circuits into kernels for multi-core processing and supports ancilla sharing, enhancing scalability and resource efficiency.

Findings

Handles large-scale quantum algorithms effectively

Enables ancilla qubit sharing among quantum operations

Demonstrates improved resource management in quantum mapping

Abstract

Quantum algorithms for solving problems of interesting size often result in circuits with a very large number of qubits and quantum gates. Fortunately, these algorithms also tend to contain a small number of repetitively-used quantum kernels. Identifying the quantum logic blocks that implement such quantum kernels is critical to the complexity management for realizing the corresponding quantum circuit. Moreover, quantum computation requires some type of quantum error correction coding to combat decoherence, which in turn results in a large number of ancilla qubits in the circuit. Sharing the ancilla qubits among quantum operations (even though this sharing can increase the overall circuit latency) is important in order to curb the resource demand of the quantum algorithm. This paper presents a multi-core reconfigurable quantum processor architecture, called Requp, which supports a layered approach to mapping a quantum algorithm and ancilla sharing. More precisely, a scalable quantum mapper, called Squash, is introduced, which divides a given quantum circuit into a number of quantum kernels- each kernel comprises k parts such that each part will run on exactly one of k available cores. Experimental results demonstrate that Squash can handle large-scale quantum algorithms while providing an effective mechanism for sharing ancilla qubits.