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pith:TANPRPY2

pith:2026:TANPRPY2HZ5R7ZL6IGOGWAL2SN

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Neural QAOA$^{2}$: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization

Jiahao Wu, Shengcai Liu, Zubin Zheng

A neural generator learns graph partitions and QAOA starting parameters together by back-propagating through a differentiable quantum evaluator.

arxiv:2605.13072 v1 · 2026-05-13 · quant-ph · cs.AI

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Claims

C1strongest claim

our gradient-driven approach broadly outperforms heuristic baselines, ranking first on 101 instances. It exhibits zero-shot generalization across out-of-distribution graph topologies and scales.

C2weakest assumption

The differentiable quantum evaluator acts as a high-fidelity performance surrogate that supplies accurate gradient guidance for the joint generator.

C3one line summary

A differentiable generative evaluative network jointly learns graph partitions and QAOA parameter initializations, outperforming heuristic baselines on 101 of 183 tested QUBO, Ising, and MaxCut instances with zero-shot generalization.

References

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[1] We collect all applicants for partitionjinto a candidate setU j

[2] • If|U j| ≤C (j) remain, all candidates are accepted

[3] weakly-coupled 2013

[4] These features are standardized (zero mean, unit variance) within each graph instance to handle variations in graph scale

[5] Edge weight normalization: Edge weights are normalized by the maximum absolute weight in the graph, ensuring wij ∈[−1,1]. Construction of the Offline Dataset (Doffline).To pre-train the quantum evalua

Receipt and verification

First computed	2026-05-18T03:08:58.844246Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

981af8bf1a3e7b1fe57e419c6b017a937a24fc61483a24caa4a1ffd72d15901c

Aliases

arxiv: 2605.13072 · arxiv_version: 2605.13072v1 · doi: 10.48550/arxiv.2605.13072 · pith_short_12: TANPRPY2HZ5R · pith_short_16: TANPRPY2HZ5R7ZL6 · pith_short_8: TANPRPY2

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/TANPRPY2HZ5R7ZL6IGOGWAL2SN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 981af8bf1a3e7b1fe57e419c6b017a937a24fc61483a24caa4a1ffd72d15901c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7ff84f3392eed9410c5ded34b1c292cd947e77a119313044b5706f2ec61ba1ee",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "quant-ph",
    "submitted_at": "2026-05-13T06:43:10Z",
    "title_canon_sha256": "4c40e405ade12da2fc9d1803dc0db31df2c6c37a513d5bcbd44f261989db1b24"
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  "source": {
    "id": "2605.13072",
    "kind": "arxiv",
    "version": 1
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