quant-ph digest — 2026-04-20
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 (59 new + 18 cross = 77 entries)
Coverage: all 77 entries scored. 6 relevant (score ≥ 1); 71 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) — 0 papers
None today.
Moderately relevant (score 5–7) — 2 papers
Quantum Search without Global Diffusion
- Authors: John Burke, Ciaran McGoldrick
- arXiv: 2604.15435
- Category: new submission — Quantum Physics (quant-ph); Data Structures and Algorithms (cs.DS)
- Score: 7/10 (MED)
- Overlaps with: Y4 (method: Grover search variants for structured spaces)
- Why it matters: Presents a recursive Grover amplitude-amplification scheme in which the diffusion operator acts only locally on non-overlapping partitions of the search register while preserving the quadratic speedup — closely parallel in spirit to Y4, where Grover is applied over the structured cardinality-constrained set C(n,k). The closed-form analysis of principal-angle degeneracies may transfer to cardinality-constrained searches with tensor-product structure.
Quantum search is among the most important algorithms in quantum computing. At its core is quantum amplitude amplification, a technique that achieves a quadratic speedup over classical search by combining two global reflections: the oracle, which marks the target, and the diffusion operator, which reflects about the initial state. We show that this speedup can be preserved when the oracle is the only global operator, with all other operations acting locally on non-overlapping partitions of the search register. We present a recursive construction that, when the initial and target states both decompose as tensor products over these chosen partitions, admits an exact closed-form solution for th
Asymptotic optimality of Grover-Radhakrishnan-Korepin algorithm
- Authors: Kun Zhang, Kang-Yuan Chen, Xiao-Hui Wang, Vladimir Korepin
- arXiv: 2604.15886
- Category: new submission — Quantum Physics (quant-ph)
- Score: 6/10 (MED)
- Overlaps with: Y4 (method: Grover partial-search optimality)
- Why it matters: Proves asymptotic optimality of the Grover-Radhakrishnan-Korepin partial-search algorithm via Pontryagin maximum principle and bang-bang control. Adjacent to Y4 where Grover rotations of O(sqrt(C(n,k)/M)) drive a cardinality-constrained optimisation — the control-theoretic lens on Grover schedules is worth knowing even though the problem setting differs.
Grover's algorithm is a cornerstone of quantum algorithms and is strictly optimal in oracle-query complexity. While the full search problem admits no further improvement, one may trade accuracy for speed in the partial search problem, where the task is to identify only the block containing the target item. The best known quantum algorithm for the partial search problem is the Grover-Radhakrishnan-Korepin (GRK) algorithm, whose optimality has long been conjectured but not proved. In this work, we prove the optimality of GRK in the large-block limit. We formulate partial search as a time-optimal control problem and apply the Pontryagin maximum principle to derive the switching-function dynamic
Tangential (score 1–4) — 4 papers
- 2604.15427 · score 4/10 · Tensor Networks with Belief Propagation Cannot Feasibly Simulate Google's Quantum Echoes Experiment — Argues TN+BP is not a viable classical simulator of the Google Echoes experiment — tangential to Y5 quantum-inspired/dequantisation discourse and to Y3 discussion of when classical methods become incapable of matching noisy hardware.
- 2604.15693 · score 4/10 · Observable-Guided Generator Selection for Improving Trainability in Quantum Machine Learning with a $ \mathfrak{g} $-Purity Interpretation under Restricted Settings — Observable-guided selection of Pauli-string generators for QML ansätze — relates to QAOA ansatz/mixer design (Y1–Y3) and Pauli-structure reasoning (Y5).
- 2604.15441 · score 3/10 · Quantum computation at the edge of chaos — VQA/barren-plateau regularisation via topological entanglement entropy — only tangentially relevant to QAOA trainability in Y1–Y3.
- 2604.16179 · score 3/10 · Quantum-Inspired Simulation of 2D Turbulent Rayleigh-Bénard Convection — Tensor-network (MPS) simulation of 2D Rayleigh-Bénard convection — tangential to Y5 quantum-inspired classical SDP algorithms, same family of "classical simulation borrowing quantum structure" but different application.
Summary table
| Score | arXiv ID | Short title | Overlaps | arXiv |
|---|---|---|---|---|
| 7 | 2604.15435 | Quantum Search without Global Diffusion | Y4 (method: Grover search variants for structured spaces) | link |
| 6 | 2604.15886 | Asymptotic optimality of Grover-Radhakrishnan-Korepin algorithm | Y4 (method: Grover partial-search optimality) | link |
| 4 | 2604.15427 | Tensor Networks with Belief Propagation Cannot Feasibly Simulate Google's Quantu | Y3, Y5 (conclusion: classical-simulability boundary) | link |
| 4 | 2604.15693 | Observable-Guided Generator Selection for Improving Trainability in Quantum Mach | Y1–Y3, Y5 (method: Pauli-string ansatz design) | link |
| 3 | 2604.15441 | Quantum computation at the edge of chaos | Y1–Y3 (method: VQA trainability) | link |
| 3 | 2604.16179 | Quantum-Inspired Simulation of 2D Turbulent Rayleigh-Bénard Convection | Y5 (method: quantum-inspired classical simulation) | link |