quant-ph digest — 2026-05-31

Generated 2026-05-31 · 103 entries scored · 10 relevant

Scored against Yuan's research programme (Y1–Y6):

Source

arXiv listing: https://arxiv.org/list/quant-ph/new (85 new + 18 cross = 103 entries, announce cycle Friday 29 May 2026)

Coverage: all 103 entries scored. 10 relevant (score ≥ 1); 93 SKIP (score 0, omitted).

Scoring rubric

0–10 on method / scope / conclusion overlap — max wins. HIGH 8–10 · MED 5–7 · LOW 1–4 · SKIP 0.

Highly relevant (score 8–10) — 3 papers

Non-Abelian Mixer for QAOA on Hybrid Oscillator-Qubit Quantum Processors

The realization of universal control in hybrid oscillator-qubit quantum processors enables the systematic design and implementation of quantum algorithms. However, the algorithmic development for such platforms remains at an early stage. While the Quantum Approximate Optimization Algorithm (QAOA) has been extensively studied in both continuous-variable (CV) and discrete-variable (DV) quantum systems, its development in the hybrid CV-DV setting remains limited. In this paper, we propose a hardware-native non-Abelian mixer for QAOA on hybrid CV-DV quantum processors and develop a corresponding hybrid ansatz for the Max-Cut problem.

Evaluating Parameter Transfer in FALQON Across Graph Families

We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors (n ∈ {8,10,12}) to 14-node recipients. Using 3-regular and Erdős–Rényi families, we show that transfer success is dictated by the recipient graph, not the donor. Transfer excels for dense recipients — achieving high approximation ratios regardless of the donor — but remains challenging in sparse cross-family cases. Crucially, performance is highly resilient to donor size, with 8-node donors matching larger instances. Thus, cheap small graphs can provide robust parameters for larger targets, significantly reducing the measurement overhead of the feedback loop.

Quantitative semidefinite certificates for ground-state energies of Pauli Hamiltonians

The k-local Hamiltonian problem is a central model for quantum many-body systems and Hamiltonian complexity. Semidefinite programming and noncommutative sum-of-squares hierarchies provide systematic certificates for ground-state energies, but existing finite-convergence results give no quantitative guarantee on the accuracy of the low hierarchy levels accessible in computation. We prove explicit finite-level convergence rates for these hierarchies in the Pauli setting.

Moderately relevant (score 5–7) — 4 papers

Quantum optimization beyond QUBO for industrial logistics and scheduling

The increasing complexity of industrial scheduling and transport routing problems motivates the study of alternative optimization formulations and computational paradigms. In this work, we study how higher-order unconstrained binary optimization (HUBO) formulations of such problems map onto quantum optimization workflows in both noisy and fault-tolerant regimes. We consider three representative logistics and manufacturing use cases and formulate each as a HUBO problem.

High-Fidelity ROI CT Reconstruction with Limited Quantum Resources via Hybrid Classical-Quantum Refinement

Quantum optimization for computed tomography (CT) reconstruction is constrained by the number of binary variables required for image representation. We propose a hybrid region-of-interest (ROI) refinement framework in which a coarse global image is first reconstructed by classical or quantum tomographic methods, and quantum optimization is then applied only to the selected ROI through a residual projection-image formulation. This strategy reduces the effective QUBO size while preserving high-fidelity reconstruction in the target region.

Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool

Certifying the robustness of QML models, particularly on NISQ hardware, is a fundamental step toward trustworthy quantum AI. The core of our framework is a fidelity-based robustness lower bound computable directly from the measurement outcome distribution. The optimal bound can be computed via semidefinite programming (SDP). We provide an efficient formal verification framework, VeriQR (the first dedicated QML robustness verification tool), and the first experimental benchmark of quantum adversarial robustness on a 20-qubit superconducting processor.

On the question of noise as a resource in quantum computing

Noise is usually regarded as the main obstacle to achieving a scalable quantum advantage, but recent evidence in quantum reservoir computing suggests that certain channels can, in appropriate regimes, improve performance. We propose a geometric mechanism to explain how non-unital noise applied together with a universal gate set leads to a faster approach to Haar-like distributions of the final states. We find that noise of this kind induces an effective volume expansion on the manifold of pure states.

Tangential (score 1–4) — 3 papers

Summary table

ScorearXiv IDShort titleOverlapsarXiv
92605.30234Non-Abelian Mixer for QAOA on Hybrid CV-DV ProcessorsY1, Y2 (method)link
82605.29917FALQON Parameter Transfer Across Graph FamiliesY1, Y3 (method)link
82605.29959SDP Certificates for Pauli Hamiltonian Ground StatesY5 (method)link
62605.30252Quantum Optimization Beyond QUBO for LogisticsY2, Y3, Y4 (scope)link
52605.29472Hybrid Classical-Quantum CT Refinement (QUBO)Y3, Y4 (method)link
52605.29877Verifying Adversarial Robustness in QML (SDP)Y5, Y6 (method/scope)link
52605.30026Noise as a Resource in Quantum ComputingY3, Y6 (conclusion/scope)link
32605.29181VQA for Nonlinear FEA of Hyperelastic MaterialsVQA-adjacentlink
32605.29242Hybrid Gaussian-Exponential ZNE for Periodic CircuitsNISQ (Y3/Y6)link
32605.30261Qubit-Efficient VQE for Nuclear Structure on IBM HWNISQ (Y6)link