DeComp2: Description Complexity aware Decomposition

2026-07-27Emerging Technologies

Emerging Technologies
AI summary

The authors explain that current quantum compilers focus only on how complex a circuit is to run, ignoring how complicated it is to describe or store the circuit information. They propose combining both the running difficulty and the description complexity to better represent the overall cost, inspired by ideas from quantum complexity theory. By testing their method on certain quantum operations, they find that including description complexity changes which circuits are considered best, showing that their approach could help compilers make smarter optimization choices beyond just runtime efficiency.

quantum compilercircuit complexityKolmogorov complexityquantum gatesminimum description lengthhierarchical intermediate representationSU(2)Haar measurequantum circuit optimization
Authors
Aritra Sarkar
Abstract
Quantum compilers optimize execution-only proxies such as gate count, depth, and fidelity, treating the compiled circuit as the unit of cost. This conflates two distinct resources, how much the substrate has to do at run time, and how much has to be said to describe what to do. Unrolling a looped program leaves run-time cost unchanged while erasing the hierarchical structure on which downstream optimization and reuse rely. We propose lifting the compiler objective from execution to life cycle complexity by pairing the circuit complexity $\mathcal{C}_{\mathrm{circ}}$ with a Kolmogorov-style description complexity $\mathcal{C}_{\mathrm{Kol}}$ of the emitted circuit, and minimizing the additive total $\mathcal{C}_{\mathrm{tot}} = α\,\mathcal{C}_{\mathrm{circ}} + β\,\mathcal{C}_{\mathrm{Kol}}$. The mirrors the additive positional and kinetic entropy of the second law of quantum complexity, and reads as a minimum-description-length regularizer over the otherwise degenerate set of $\mathcal{C}_{\mathrm{circ}}$-minimizers. We instantiate the objective on $SU(2)$ through exhaustive HT-enumeration with compression-based surrogates upper-bounding $\mathcal{C}_{\mathrm{Kol}}$. Across a Haar-representative target grid the two surrogates correlate positively with $\mathcal{C}_{\mathrm{circ}}$ yet break its rank order on a non-trivial fraction of points, ruling out a tight functional dependence. With calibrated weights the joint cost selects, for a small but operationally meaningful fraction of targets, a candidate that is neither the shortest nor the most compressible HT-string in the $\varepsilon$-ball, exhibiting the compilation choices a $\mathcal{C}_{\mathrm{circ}}$-only optimizer discards and motivating $\mathcal{C}_{\mathrm{Kol}}$ as an active optimization signal for hierarchical intermediate representation for compiler.