quant-ph digest — 2026-04-22
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 (86 new + 33 cross = 119 entries, Tuesday 21 April 2026 announce cycle).
Coverage: all 119 entries scored. 5 relevant (score ≥ 1); 114 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) — 1 paper
EQE-QAOA: An Equivalence-Preserving Qubit Efficient Framework for Combinatorial Optimization
- Authors: Xiaoyu Ma, Fang Fang, Ximing Xie, Xianbin Wang, Lajos Hanzo
- arXiv: 2604.18285
- Category: cross submission — Emerging Technologies (cs.ET); Quantum Physics (quant-ph)
- Score: 9/10 (HIGH)
- Overlaps with: Y2 (method — log₂-qubit encoding with hard-constraint mixer, direct sibling of quasi-binary encoding), Y1 (method/scope — QAOA Max-Cut), Y3 (scope — cardinality-constrained optimisation), Y4 (scope — cardinality-constrained binary optimisation)
- Why it matters: Proves exact equivalence between a QAOA on n qubits and a QAOA on m = ⌈log₂ M⌉ qubits whenever the problem has a non-trivial commutant (constraints or permutation symmetry), constructed via an explicit isometry. For complete graphs this is an exponential compression. The constrained experiments use a Hamming-weight XY mixer — the same hard-constraint-preserving design Y2 pioneered. This is arguably the most direct methodological peer of Y2 published in 2026 so far and should be cited in any follow-up.
The limited number of qubits is a major bottleneck in Quantum Approximate Optimization Algorithm (QAOA) for large-scale combinatorial optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. To make progress, existing techniques rely on qubit reduction at the cost of information loss, hence leading to degraded computational performance. As a remedy, we propose the Equivalence-preserving Qubit Efficient QAOA (EQE-QAOA), which significantly reduces the required number of qubits without degrading the performance of QAOA. By exploiting intrinsic symmetries and conserved quantities, we first demonstrate that the QAOA dynamics are strictly confined to an invariant subspace of the Hilbert space.
Moderately relevant (score 5–7) — 2 papers
Quantangle-SAT: A Quantum SAT Solver Based on Entanglement and Equivalence Checking
- Authors: Shang-Wei Lin, Ji-Qing Yan, Yean-Ru Chen, Zhe Hou, David Sanán
- arXiv: 2604.18218
- Category: new submission — Quantum Physics (quant-ph)
- Score: 6/10 (MED)
- Overlaps with: Y4 (method — Grover-family quantum search for combinatorial problems; also addresses the "unknown number of satisfying assignments" problem that Y4's Grover rotation count O(√(C(n,k)/M)) also has to handle)
- Why it matters: Directly targets a known weakness of Grover-based combinatorial search — needing prior knowledge of the solution count M — and replaces quantum counting (which is orders of magnitude more expensive than Grover) with an entanglement-and-equivalence-checking procedure. Claims expected O(1) time over random Boolean functions. Worth reading for the contrast with Y4's ADMM-based epsilon-approximation approach to the same problem.
Satisfiability (SAT) is a central problem in computer science, and advances in SAT-solving algorithms have a far-reaching impact across many fields. Recent works have proposed quantum SAT solvers based on Grover's algorithm, a quantum search technique. However, Grover-based approaches face a key limitation: they typically require prior knowledge of the number of satisfying assignments of the target Boolean formula. This information is unavailable in most practical settings. Quantum counting can be used to estimate this quantity, but it incurs a computational overhead that is several orders of magnitude higher than Grover search. In this paper, we propose a novel quantum SAT solver based on entanglement and equivalence checking.
Scalable Quantum Error Mitigation with Physically Informed Graph Neural Networks
- Authors: Huaxin Wang, Xinge Wu, Jiajun Liu, Ruiqing He, Jiandong Shang, Hengliang Guo, Qiang Chen
- arXiv: 2604.16815
- Category: new submission — Quantum Physics (quant-ph); Machine Learning (cs.LG)
- Score: 5/10 (MED)
- Overlaps with: Y3 (scope — NISQ noise modeling with T₁, T₂, readout, and two-qubit gate errors; same noise channel family Y3 identified as blocking quantum advantage for DGMVP), Y6 (scope — superconducting-processor execution)
- Why it matters: GEM encodes the device's calibration data as node/edge features of the circuit-as-graph and learns to correct observable bias. Benchmarks at 10 and 16 qubits on superconducting processors. Relevant to the question Y3 left open: if error mitigation improves, does the thermal-relaxation crossover shift enough to recover the favourable shot-noise-regime scaling? Worth running on QAOA observables.
Quantum error mitigation (QEM) provides a practical route for estimating reliable observables on noisy intermediate-scale quantum (NISQ) devices. Traditional QEM strategies, including zero-noise extrapolation (ZNE) and Clifford data regression (CDR), rely on noise scaling or global regression, and their performance is constrained by the exponential growth of the system degrees of freedom. We construct a graph-enhanced mitigation (GEM) framework, which incorporates physical information into the model representation. In this work, quantum circuits are encoded as attributed graphs. Hardware-level physical information is mapped to node and edge features: local noise parameters such as calibration parameters T₁, T₂, and readout errors are encoded at nodes, while coupling-related information such as two-qubit gate errors is encoded as edge features.
Tangential (score 1–4) — 2 papers
- 2604.17515 · score 3/10 · Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation — NISQ noise evaluation (Y3-adjacent scope), but the task is HQNN classification on the Iris dataset, not optimisation; mitigation gains for ZNE/DDD/LRE are modest.
- 2604.18238 · score 2/10 · Equivalence of Local Dynamical Hidden-Variable Models to Static Bell Locality — Foundations/no-go result adjacent to Y6's PBR-style work, but the target is Bell locality not psi-ontic vs psi-epistemic models; different no-go regime.
Summary table
| Score | arXiv ID | Short title | Overlaps | arXiv |
|---|---|---|---|---|
| 9 | 2604.18285 | EQE-QAOA: equivalence-preserving qubit-efficient QAOA | Y1, Y2, Y3, Y4 | link |
| 6 | 2604.18218 | Quantangle-SAT: Grover-alternative SAT solver | Y4 | link |
| 5 | 2604.16815 | GEM: graph-NN quantum error mitigation | Y3, Y6 | link |
| 3 | 2604.17515 | HQNN robustness under noise | Y3 | link |
| 2 | 2604.18238 | Local dynamical hidden-variable models ≡ static Bell | Y6 | link |