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

2026-03-21

AI Summary

This work addresses the scalability bottleneck in quantum computing imposed by the high energy consumption and limited cryogenic bandwidth of classical control systems, particularly when transmitting lengthy quantum instruction streams. The authors propose a novel quantum instruction compression framework that integrates sparse dictionary learning with information-theoretic entropy optimization. By uniquely combining discrete Solovay–Kitaev basis synthesis, sparse dictionaries derived from Haar-random unitary matrices, Huffman coding, and bzip2 lossless compression, the method achieves high-efficiency compression of quantum circuit instruction streams while preserving computational fidelity. The approach further enables the discovery of high-level composable abstractions and facilitates algorithmic complexity estimation. Evaluated across multiple benchmark circuits, it consistently achieves over 60% average compression ratio, substantially reducing classical control energy demands and communication overhead.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Data Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

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.

Problem

• quantum instruction set architecture
• energy efficiency
• classical control
• cryogenic bandwidth
• quantum scalability

Innovation

• Quantum Instruction Set Architecture
• Sparse Dictionary Learning
• Instruction Compression
• Energy-Efficient Quantum Control
• Algorithmic Complexity