Pith Number

pith:VH5WU2S2

pith:2026:VH5WU2S2IPZA6JEYHSNZLKJRVL

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Physics Guided Generative Optimization for Trotter Suzuki Decomposition

WenBin Yan

A conditional diffusion model guided by physics-informed fidelity feedback produces Trotter-Suzuki decompositions that reach 85.6 percent of fourth-order baseline accuracy at 22 percent circuit depth on the transverse Ising model.

arxiv:2605.13268 v1 · 2026-05-13 · quant-ph · cs.LG

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Claims

C1strongest claim

On the transverse field Ising model under the primary comparison setup, the method reaches 85.6% of the fidelity of a fourth order Qiskit baseline (0.856) at roughly 21.8% of the circuit depth and 19.2% of the baseline CNOT count. Under an equal depth budget, fine tuning in the loop reached a best observed fidelity of 0.9994.

C2weakest assumption

The physics-informed neural network supplies reliable differentiable fidelity feedback that remains accurate across the discrete grouping and order choices explored during training and that the REINFORCE-based optimization converges to strategies that generalize beyond the specific TFIM instances and hyperparameter settings used.

C3one line summary

A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.

References

35 extracted · 35 resolved · 2 Pith anchors

[1] Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg 2021

[2] Physical Review Letters , author = 2015 · doi:10.1103/physrevlett.114.090502

[3] https://doi.org/10.1103/RevModPhys.94.015004 2022 · doi:10.1103/revmodphys.94.015004

[4] Theory of Trotter error with commutator scaling 2021 · doi:10.1103/physrevx.11.011020

[5] Efficient quantum measurement of Pauli operators in the presence of finite sampling error.Quantum, 5:385, 2021 2021 · doi:10.22331/q-2021-01-20-385

Receipt and verification

First computed	2026-05-18T02:44:49.298575Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a9fb6a6a5a43f20f24983c9b95a931aad56a6f83af2f9978bbc83f647d484a69

Aliases

arxiv: 2605.13268 · arxiv_version: 2605.13268v1 · doi: 10.48550/arxiv.2605.13268 · pith_short_12: VH5WU2S2IPZA · pith_short_16: VH5WU2S2IPZA6JEY · pith_short_8: VH5WU2S2

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/VH5WU2S2IPZA6JEYHSNZLKJRVL \
  | 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: a9fb6a6a5a43f20f24983c9b95a931aad56a6f83af2f9978bbc83f647d484a69

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5df33a0ff3f8235550387f506e2792a0f0ca48fa1e73275d70403ebbdacd6cd2",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "quant-ph",
    "submitted_at": "2026-05-13T09:48:33Z",
    "title_canon_sha256": "c379176237dc6f6c7746dc639c7dd1f0cb92e932b0e51e0f28f9c1dd83946d67"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13268",
    "kind": "arxiv",
    "version": 1
  }
}