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

Quantum Physics [Submitted on 21 Mar 2026] EQISA: Energy-efficient Quantum Instruction Set Architecture using Sparse Dictionary Learning Sibasish Mishra, Aritra Sarkar, Sebastian Feld 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. Comments: associated repository: this https URL Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Systems and Control (eess.SY) Cite as: arXiv:2603.20646 [quant-ph]   (or arXiv:2603.20646v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.20646 Focus to learn more Submission history From: Aritra Sarkar [view email] [v1] Sat, 21 Mar 2026 04:42:10 UTC (1,595 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.ET cs.SY eess eess.SY References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)