EQISA: Energy-efficient Quantum Instruction Set Architecture using Sparse Dictionary Learning

TL;DR

This paper presents EQISA, a novel quantum instruction set architecture that compresses quantum instruction streams using sparse dictionary learning, significantly reducing energy consumption and communication overhead in quantum control systems.

Contribution

Introduction of EQISA, an energy-efficient quantum ISA that employs sparse dictionary learning and compression techniques to optimize quantum circuit execution and control energy usage.

Findings

Achieves over 60% compression of quantum instruction streams

Reduces classical control energy and communication overhead proportionally

Enables discovery of higher-level quantum circuit abstractions

Abstract

The scalability of quantum computing in supporting sophisticated algorithms critically depends not only on qubit quality and error handling, but also on the efficiency of classical control, constrained by the cryogenic control bandwidth and energy budget. In this work, we address this challenge by investigating the algorithmic complexity of quantum circuits at the instruction set architecture (ISA) level. We introduce an energy-efficient quantum instruction set architecture (EQISA) that synthesizes quantum circuits in a discrete Solovay-Kitaev basis of fixed depth and encodes instruction streams using a sparse dictionary learned from decomposing a set of Haar-random unitaries, followed by entropy-optimal Huffman coding and an additional lossless bzip2 compression stage. This approach is evaluated on benchmark quantum circuits demonstrating over 60% compression of quantum instruction streams across system sizes, enabling proportional reductions in classical control energy and communication overhead without loss of computational fidelity. Beyond compression, EQISA facilitates the discovery of higher-level composable abstractions in quantum circuits and provides estimates of quantum algorithmic complexity. These findings position EQISA as an impactful direction for improving the energy efficiency and scalability of quantum control architectures.