arXiv subjects

Colin Krawchuk

Publications and source records attributed to Colin Krawchuk.

5 recordsLinked to original sources

arXiv2026

Decoded Quantum Interferometry Beyond Hamming: Rank-Metric and Translation Association Schemes

Decoded Quantum Interferometry (DQI) uses coherent decoding and a quantum Fourier transform to find high-quality solutions of structured optimisation problems. Existing analyses are closely tied to Hamming space, which underlies the optimisation objective, Dicke state preparation and the decoding step of the algorithm. Here we extend the core DQI mechanism beyond Hamming space to finite geometries with translation symmetry, where points are grouped into shells by their distance from a basepoint. Mathematically, these geometries are translation association schemes. In this setting the algorithm can be analysed by tracking one amplitude per shell, and biasing the prepared state towards high-quality solutions becomes a finite tridiagonal eigenvalue problem. As a non-Hamming example, we develop an efficient DQI protocol for finding an m x n finite-field matrix with smallest rank difference to a target matrix. Initial states are uniform superpositions over fixed-rank matrices, and Gabidulin codes provide candidates for efficient low-rank decoding up to a cutoff l. For this objective, this finds solutions with an effective-rank proxy near min(m,n)-l, and the corresponding expected score can be converted into a constant-probability bound on the residual rank of a sample. For Gabidulin nearest-codeword instances, a covering-radius obstruction shows that this bound does not imply an additive guarantee for the true optimum, and we do not claim a quantum advantage for the rank-metric construction. The results instead identify the geometric and coding ingredients for DQI beyond Hamming space.

quant-ph↗

arXiv2026

Compositional Quantum Heuristics for Max-Clique Detection

Quantum machine learning holds the promise of combining the success of classical machine learning methods with the power of quantum computing, however one of the largest obstacles facing the field is the problem of barren plateaus. Parameterised quantum circuits offer a flexible framework for developing quantum machine learning models, but their practicality is constrained by a trade-off between trainability and classical simulability. In general, circuits that are sufficiently expressive to model complex behaviour often exhibit barren plateaus, where gradients vanish and optimisation fails. In this work we investigate a compositional approach to mitigate this trade-off by assembling larger quantum models from smaller subcomponents. To ensure trainability of these subcomponents, we describe a framework for constructing group-invariant loss functions, which introduce symmetry-induced inductive bias and lead to improved gradient behaviour and generalisation. In particular, we use this framework to design permutation-equivariant quantum graph neural networks for identifying maximal cliques in graphs. The models we construct exhibit superior training gradients through symmetry-induced bias, and our experiments demonstrate that the trained models generalise to larger, more complex problem instances. Finally, inspired by Quantum-Informed Recursive Optimisation Algorithms (arXiv:2308.13607), we implement a recursive hybrid quantum-classical heuristic using the learned quantum models to guide a classical search procedure, demonstrating improved inference accuracy and scalability. Together, these results suggest that compositional circuits could be a viable pathway towards scalable quantum learning models that remain challenging to reproduce classically.

quant-ph↗

arXiv2026

Bounds on Decorated Sweep Covers in Tree Posets

We introduce decorated sweep covers as a colouring on maximal antichains in tree posets such that if two elements have the same colour they are siblings. DSCs appear in applications wherever maximal antichains require structural differentiation among parallel options that have a common ancestry, e.g., distributed systems, drone routing in logistics, and Monte Carlo Tree Search. We restrict our analysis to enumerating $k$-coloured DSCs in $n$-ary tree posets and prove i) their ordinary generating function in Theorem 1, ii) new Schur-convexity results for binomial coefficients in Theorem 2 and iii) bounds on the OGF coefficients which scale as $\Theta(D_n^k k^\beta)$ in Theorem 3 where $D_n > n$ is the exponential growth constant for each $n$.

math.CO↗

arXiv2025

Efficient Generation of Parameterised Quantum Circuits from Large Texts

Quantum approaches to natural language processing (NLP) are redefining how linguistic information is represented and processed. While traditional hybrid quantum-classical models rely heavily on classical neural networks, recent advancements propose a novel framework, DisCoCirc, capable of directly encoding entire documents as parameterised quantum circuits (PQCs), besides enjoying some additional interpretability and compositionality benefits. Following these ideas, this paper introduces an efficient methodology for converting large-scale texts into quantum circuits using tree-like representations of pregroup diagrams. Exploiting the compositional parallels between language and quantum mechanics, grounded in symmetric monoidal categories, our approach enables faithful and efficient encoding of syntactic and discourse relationships in long and complex texts (up to 6410 words in our experiments) to quantum circuits. The developed system is provided to the community as part of the augmented open-source quantum NLP package lambeq Gen II.

quant-ph↗

arXiv2024

A gluing operation for dimer quivers

In this article we introduce a gluing operation on dimer models. This allows us to construct dimer quivers on arbitrary surfaces. We study how the associated dimer and boundary algebras behave under the gluing and how to determine them from the gluing components. We also use this operation to construct homogeneous dimer quivers on annuli.

math.CO↗