quant-ph digest — 2026-05-13
Scored against Yuan's research programme (Y1–Y6):
- Y1 — arXiv:2502.09704 — iterative warm-started QAOA
- Y2 — arXiv:2304.06915 — quasi-binary portfolio QAOA
- Y3 — arXiv:2410.16265 — QAOA DGMVP portfolio (QST 2026)
- Y4 — arXiv:2603.14744 — Grover + ADMM cardinality-constrained BO
- Y5 — arXiv:2510.08292 — GW speed-ups via Gibbs states + Pauli sparsity
- Y6 — arXiv:2510.11213 — PBR test on IBM Heron2
Source
arXiv listing: https://arxiv.org/list/quant-ph/new (97 new + 30 cross = 127 entries; listing dated Tuesday, 12 May 2026)
Coverage: all 127 entries scored. 18 relevant (score ≥ 1); 109 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
Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
- Authors: Michael Mancini, Shabnam Sodagari
- arXiv: 2605.08332
- Category: new submission — Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
- Score: 9/10 (HIGH)
- Overlaps with: Y1 (warm-starting QAOA, 3-regular MaxCut), Y3 (layerwise parameter optimization) — method overlap
- Why it matters: This is the direct contemporary competitor to Y1: the authors derive per-layer (δk, Mk) from a feedback-control scheme on all 94 non-isomorphic 3-regular N=12 graphs, then feed the schedule into QAOA as a warm start — exactly Y1's pipeline but with a different seed mechanism (feedback control vs. measurement-derived bias). They report 50× Psucc improvement over fixed FALQON and ~0.28 median success when warm-starting QAOA-GD. The bibliography misses Y1.
Feedback-based adaptive quantum optimization (FALQON) is a promising approach for solving combinatorial problems on noisy intermediate-scale quantum (NISQ) devices, requiring only single circuit evaluations per layer. However, standard FALQON relies on fixed hyperparameters that severely limit convergence speed, requiring hundreds to thousands of layers for acceptable solutions. This paper proposes Optimal FALQON, an optimization-based formulation that treats the per-layer time step (δk) and scaling factor (Mk) as decision variables optimized via classical methods. We present a comprehensive empirical study on all 94 non-isomorphic 3-regular graphs with 12 vertices, comparing Optimal FALQON with standard FALQON and multiple QAOA variants. Results demonstrate statistically significant improvements in success probability, evaluation efficiency, and depth-normalized cost across the evaluated benchmarks. Furthermore, initializing QAOA with parameters from Optimal FALQON yields superior warm-start performance compared to fixed initialization.
Comparing Qubit and Qudit Encodings for EV Charging and Trip Assignment Problems
- Authors: Linus Ekström, Hao Wang, Sebastian Schmitt
- arXiv: 2605.10255
- Category: new submission — Quantum Physics (quant-ph)
- Score: 8/10 (HIGH)
- Overlaps with: Y2 (encoding choice, dimension reduction), Y3 (QAOA for constrained optimization) — method+scope overlap
- Why it matters: A direct cousin of Y2's quasi-binary encoding argument, but on EV-fleet scheduling and using full qudit encodings. The paper shows that on highly constrained problems (feasibility 0.1%–20%) the qudit trip encoding consistently matches or beats native qubit encoding under a fixed Powell-iteration budget — exactly Y2's thesis, on a different industrial problem and with a published qudit-mixer recipe Yuan can reuse. A head-to-head with quasi-binary is the obvious next step.
Variational quantum algorithms have attracted attention for their potential to solve combinatorial optimization problems. We study how the choice of encoding affects the resource requirements and optimization behavior of a variational quantum optimization algorithm. In order to quantify these effects, realistically inspired constrained electric vehicle (EV) fleet management problems were considered. These problems couple determining the optimal EV battery charging schedule with assigning EVs to trips requested by customers. We compare a conventional binary (qubit) trip encoding with an integer (qudit) encoding that represents assignments more directly. Both encodings guarantee the same feasible solution set, while the qudit encoding exponentially reduces the required Hilbert-space dimension. We solve many random instances of highly constrained uni- and bi-directional charging problems using qudit-based quantum approximate optimization algorithm (QAOA).
Decoded Quantum Interferometry for Weighted Optimization Problems
- Authors: Kaifeng Bu, Weichen Gu, Xiang Li
- arXiv: 2605.10666
- Category: new submission — Quantum Physics (quant-ph)
- Score: 8/10 (HIGH)
- Overlaps with: Y4 (quantum optimization with quantum-advantage claims, structured feasible space via decoding), Y5 (Gibbs states for commuting Pauli Hamiltonians, block structure) — method+conclusion overlap
- Why it matters: DQI is the leading non-QAOA candidate for genuine quantum optimization advantage; this work extends it from uniform-constraint Max-LINSAT to weighted-constraint problems by introducing multivariate DQI states (multi-block polynomial encodings) and gives a closed-form asymptotic expression for both expectation and concentration. The Hamiltonian-DQI extension produces approximate Gibbs states for commuting Pauli Hamiltonians with block structure — directly resonant with Y5's Pauli-sparse Gibbs state programme.
Deep analysis skipped — PDF is 57 pages (> 50-page limit).
Decoded Quantum Interferometry (DQI) is a recently introduced quantum algorithm that reduces discrete optimization to decoding with potential advantages over the best known polynomial-time classical algorithms for certain Max-LINSAT problems. In its original formulation, however, DQI treats all constraints uniformly and cannot exploit the weight structure present in most optimization problems of interest. In this work, we develop a theory of DQI for weighted optimization problems, focusing on the weighted Max-LINSAT problem over a prime field. Grouping constraints into N blocks by distinct weights, we introduce multivariate DQI states built from N-variable polynomials of bounded total degree, and derive a closed-form asymptotic expression for both their optimal expectation value and their concentration behavior. We give an explicit preparation circuit using a single decoder call, and extend the analysis to imperfect decoding.
Moderately relevant (score 5–7) — 5 papers
Quantum Hypergraph Partitioning
- Authors: Cameron Ibrahim, Bao G. Bach, Jad Salem, Reuben Tate, Kien X. Nguyen, Stephan Eidenbenz, Ilya Safro
- arXiv: 2605.10623
- Category: new submission — Quantum Physics (quant-ph)
- Score: 7/10 (MED)
- Overlaps with: Y3 (multi-angle QAOA for combinatorial optimization), Y5 (compared against classical SDP + hyperplane rounding) — method+scope overlap
- Why it matters: Multi-angle low-depth QAOA outperforms polynomial-time SDP+hyperplane-rounding baselines on hypergraph partitioning under maximin/minimax objectives. The classical SDP baseline is Goemans-Williamson-adjacent — Y5's exact target — and the demonstration that QAOA can beat it on these distributional objectives is a meaningful boundary marker for where quantum SDP relaxations should focus next.
Quantum optimization algorithms are inherently probabilistic, yet they are most often used to search for a single high-quality solution. In this paper, we instead study hypergraph partitioning problems in which the desired output is itself a probability distribution over partitions. We introduce a distributional perspective on hypergraph partitioning motivated by maximin and minimax objectives such as Fair Cut Cover, and we show how these objectives align naturally with the measurement distribution produced by QAOA. To motivate the formulation, we introduce a workforce-scheduling-inspired toy problem, the Greatest Expected Imbalance problem. We then develop QAOA-based quantum solvers... we provide optimal polynomial-time classical approximation algorithms based on semidefinite programming and hyperplane rounding.
SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis
- Authors: Sean Feeney, Pooja Rao, Andreas Klappenecker, Reuben Tate, Yuri Alexeev, Stefano Mensa, Elica Kyoseva, Stephan Eidenbenz
- arXiv: 2605.10327
- Category: new submission — Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
- Score: 6/10 (MED)
- Overlaps with: Y1 (QAOA on MaxCut 3-regular, parameter transfer across instances), Y3 (parameter optimization landscape) — method overlap
- Why it matters: SCALAR auto-generates conjectures relating optimal QAOA parameters to graph invariants on 82 MQLib MaxCut instances + 2,000 random graphs, up to 77 qubits — including periodicity of γ and parameter-transfer phenomena. If the conjectures hold, they could shortcut some of the per-instance optimization that Y1/Y3 had to do manually; even if not, the framework is a useful exploration tool for the QAOA landscape Yuan is interested in.
We present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in quantum circuit analysis built on top of the CUDA-Q open source framework. The system integrates quantum simulation, symbolic conjecture generation, and LLM-based interpretation. We evaluate SCALAR on 82 MaxCut instances from the MQLib benchmark dataset and extend the analysis to 2,000 randomly generated graphs across four topologies: regular, Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz. The framework generates conjectured bounds relating optimal QAOA parameters to graph invariants, including known relationships such as periodicity constraints on the phase separation parameter γ. SCALAR also recovers previously reported parameter transfer phenomena across structurally similar instances.
A Hybrid Classical-Quantum Annealing Algorithm for the TSP
- Authors: Siwei Hu, Victor Lopata, Salvatore Sinno, Shruthi Thuravakkath, Paolo Zuliani
- arXiv: 2605.09616
- Category: new submission — Quantum Physics (quant-ph); Emerging Technologies (cs.ET)
- Score: 6/10 (MED)
- Overlaps with: Y3 (combinatorial optimization scope), Y4 (classical-quantum hybrid for NP-hard problem) — scope overlap
- Why it matters: A graph-contraction-based decomposition that produces a sub-TSP small enough for a D-Wave annealer. The decomposition idea parallels Y4's ADMM hybrid in shape (classical reduction + quantum core), even though the engine is annealing rather than Grover. Useful as a benchmark contrast: when does dimension reduction help, and how does annealing-based reduction compare to Y4's amplitude-amplification-based one?
Hybrid quantum-classical algorithms can help mitigating the physical limitations of current quantum devices, particularly the low qubit count and the reduced topological connectivity. In this paper, we propose a hybrid technique to solve a well-known NP-hard optimization problem: the Traveling Salesperson Problem (TSP). Our approach is based on a graph contraction technique that removes most of the dimensionality of the original problem instance, producing a sub-TSP of a size suitable to be efficiently solved by a quantum device. The performance of our approach is first demonstrated on classical quantum simulation using Path Integral Monte Carlo, and then run on a D-Wave quantum annealer.
Quantum algorithms for path and cycle containment problems
- Authors: Arjan Cornelissen, Amin Shiraz Gilani, Subhasree Patro
- arXiv: 2605.09017
- Category: new submission — Quantum Physics (quant-ph); Computational Complexity (cs.CC)
- Score: 5/10 (MED)
- Overlaps with: Y4 (quantum walks / Grover-style search complexity over structured spaces) — method overlap
- Why it matters: Beats the prior O(n3/2) upper bound for constant-k path/cycle containment with a quantum-walk algorithm achieving O(n3/2−αk). The mathematics — query complexity over structured subsets — is the same toolkit Y4 used for cardinality-constrained Grover; conditional lower bounds via graph-collision are exactly the technique Y4 would reach for in tightening its O(sqrt(C(n,k)/M)) bound.
The quantum query complexity of subgraph-containment problems, which ask whether a given subgraph H is present in an input graph G, has been the subject of considerable study. However, even for relatively simple subgraphs, such as paths and cycles, a complete understanding of their query complexities remains elusive. In this work, we consider several variants of path- and cycle-containment problems in the adjacency matrix model, where we search for paths or cycles of constant length k. ... For the latter equivalence class, we prove a novel quantum-walk-based algorithm that achieves query complexity Õ(n3/2−αk), where αk∈Θ(c−k) and c=√(3+√17)/2 ≈ 1.33, beating the previous best upper bound O(n3/2) on its query complexity.
Symmetry-Protected Basin Localization in Variational Quantum Eigensolvers
- Authors: Yangshuai Wang
- arXiv: 2605.09909
- Category: new submission — Quantum Physics (quant-ph)
- Score: 5/10 (MED)
- Overlaps with: Y1 (warm-start analogue: initialization-conditioned basin selection in variational QAs) — method overlap
- Why it matters: SE(3)-equivariant preconditioner maps molecular geometry → initial circuit parameters in the correlated ground-state basin, with 38×–6250× reduction in initialization error vs. Hartree-Fock. The conceptual structure — pre-quantum classical procedure that places the variational loop in a good basin before the shot-limited part — is precisely the warm-start architecture Y1 used for QAOA, ported to VQE for chemistry.
Variational quantum eigensolvers fail before optimization begins when strong correlation splits the molecular energy landscape into competing basins and the initial state selects a non-ground-state basin. We introduce a geometry-conditioned preconditioner Peq:R→θ0 constrained by the SE(3) covariance of the molecular Hamiltonian, so that nuclear geometry is mapped directly into circuit parameters in the correlated ground-state basin. This basin localization changes the relevant gradient statistics from concentration controlled to curvature controlled. In statevector benchmarks on six stretched molecules, Peq reduces Hartree-Fock initialization errors by factors of 38×-6250×.
Tangential (score 1–4) — 10 papers
- 2605.08251 · score 3/10 · The finite-shot help-harm boundary of zero-noise extrapolation — Y3-adjacent noise-mitigation analysis for NISQ.
- 2605.10638 · score 3/10 · Quantifying the Hadamard Resilience Law: Discovery of the Coherence Gap in NISQ-Era Classifiers — IBM Kingston (Heron-class) hardware characterization, Y6-adjacent scope.
- 2605.10856 · score 3/10 · Improving search efficiency via adaptive acquisition function selection in discrete black-box optimization — QUBO/HUBO scope, but classical Bayesian optimization method.
- 2605.09558 · score 3/10 · Classical Limit: Dissipation of Spekkens' Generalised Contextuality under Decoherence — Y6-adjacent foundations: contextuality threshold under decoherence.
- 2605.08745 · score 3/10 · Exclusion reshapes the operational manifestation of preparation contextuality — Y6-adjacent prep-contextuality, dimension witness.
- 2605.08683 · score 2/10 · High-Precision Variational Quantum SVD via Classical Orthogonality Correction — Y3-adjacent VQA / hybrid quantum-classical methodology.
- 2605.09149 · score 2/10 · Battery-Explicit Energetic Witnesses of CHSH Post-Quantumness — Y6-adjacent CHSH/Tsirelson foundations.
- 2605.09474 · score 2/10 · Violation of Bell inequalities in 2×3 dimensional systems — Y6-adjacent Bell-CH inequality conditions.
- 2605.08375 · score 2/10 · The extended Wigner's friend, many- and single-worlds and reasoning from observation — Y6-adjacent foundations / interpretation.
- 2605.09486 · score 2/10 · CTQWformer: A CTQW-based Transformer for Graph Classification — quantum walks on graphs, Y4-adjacent method.
Summary table
| Score | arXiv ID | Short title | Overlaps | arXiv |
|---|---|---|---|---|
| 9 | 2605.08332 | Optimal FALQON for QAOA via layer-wise parameter tuning | Y1, Y3 | link |
| 8 | 2605.10255 | Qubit vs qudit encodings for EV charging + trip assignment | Y2, Y3 | link |
| 8 | 2605.10666 | Decoded Quantum Interferometry for weighted optimization | Y4, Y5 | link |
| 7 | 2605.10623 | Quantum hypergraph partitioning | Y3, Y5 | link |
| 6 | 2605.10327 | SCALAR neurosymbolic QAOA-conjecture framework | Y1, Y3 | link |
| 6 | 2605.09616 | Hybrid classical-quantum annealing for TSP | Y3, Y4 | link |
| 5 | 2605.09017 | Quantum algorithms for path and cycle containment | Y4 | link |
| 5 | 2605.09909 | Symmetry-protected basin localization in VQE | Y1 | link |
| 3 | 2605.08251 | Finite-shot help-harm boundary of ZNE | Y3 | link |
| 3 | 2605.10638 | Hadamard resilience law, coherence gap on IBM Kingston | Y6 | link |
| 3 | 2605.10856 | Adaptive acquisition functions for QUBO/HUBO | Y2/Y3/Y4 | link |
| 3 | 2605.09558 | Spekkens contextuality under decoherence | Y6 | link |
| 3 | 2605.08745 | Exclusion reshapes prep contextuality | Y6 | link |
| 2 | 2605.08683 | High-precision variational quantum SVD | Y3 | link |
| 2 | 2605.09149 | Battery-explicit witnesses of CHSH post-quantumness | Y6 | link |
| 2 | 2605.09474 | Bell inequalities in 2×3 systems | Y6 | link |
| 2 | 2605.08375 | Extended Wigner's friend / single vs. many worlds | Y6 | link |
| 2 | 2605.09486 | CTQWformer for graph classification | Y4 | link |