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n_qubits
int64
20
320
fields
listlengths
20
320
quantum_features
listlengths
60
960
label
float64
7.55
211
20
[ 1.3465310371786177, 0.6856160847749666, 0.27375234308515045, 0.2297497439513524, 1.6638864305604903, 1.842960039099899, 1.2919443963809238, 1.513093809771197, 1.1785249846377612, 1.8831303628179827, 1.668536397418758, 0.20492930030626658, 1.7433276978576249, 0.26045403554983587, 1.513379...
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10.636339
20
[ 0.2509754080618333, 0.4237098976992151, 1.4071239464485346, 1.3649411208336502, 1.307693200666257, 0.8906195976713902, 1.9949778844205799, 1.965503609797214, 1.4339755720652505, 1.3708266972820693, 1.4392041150276922, 0.9000585631623867, 0.4431737090403402, 1.498679012349347, 1.145637780...
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9.178971
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12.218947
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10.191157
20
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7.545669
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11.158531
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10.267303
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11.620383
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8.185913
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10.772272
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11.655468
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11.330395
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8.453502
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10.287392
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10.336645
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10.117123
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11.719292
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10.477102
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12.679227
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9.242397
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8.984851
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9.995622
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9.129926
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12.923744
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9.267033
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9.166745
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Quantum Ground States — Exact Representations to 320 Qubits

Exact quantum ground states of disordered transverse-field Ising chains, from 20 to 320 qubits, each paired with its local quantum representation and a physical label. A benchmark for learning properties of quantum states at a scale no state-vector simulator can reach — every example is exact ground truth.

Benchmark

Predict the physical observable Σᵢ ⟨ZᵢZᵢ₊₁⟩ from a representation of the ground state. Two representations are compared under one protocol (kernel-ridge regression; error normalised so that chance = 1.0; lower is better):

  • Quantum — the local single-qubit measurements of the ground state (the 1-RDM).
  • Classical — the parameters that generate the chain (the disorder fields).
qubits quantum representation classical representation
20 0.10 0.48
40 0.11 0.45
80 0.13 0.53
160 0.21 0.61
320 0.40 0.80

The quantum representation is 2–5× more accurate at every scale and remains well clear of chance at 320 qubits. The sample-efficiency advantage holds past the point where the full quantum state — 2³²⁰ ≈ 10⁹⁶ amplitudes — can no longer be written down. Mean over 3 seeds; standard deviations in verification.json.

What's in it

field type description
n_qubits int chain length: 20, 40, 80, 160, 320
fields list[float] disorder parameters hᵢ ~ U(0.2, 2.0), length n_qubits
quantum_features list[float] local 1-RDM ⟨Xᵢ⟩, ⟨Yᵢ⟩, ⟨Zᵢ⟩, length 3·n_qubits
label float Σᵢ ⟨ZᵢZᵢ₊₁⟩, an exact physical observable

750 examples, 150 per scale.

Exact ground truth at 320 qubits

The transverse-field Ising chain is exactly solvable by the Jordan–Wigner / Bogoliubov–de Gennes transformation: its ground state is fixed by an n × n matrix, not a 2ⁿ state vector. Every representation and label here is exact, validated to 10⁻¹⁴ against full diagonalisation. That is what makes a clean benchmark possible at hundreds of qubits — exact answers to measure against, no approximation.

The representation

quantum_features are the local 1-RDM — single-qubit expectation values of the ground state. This is the representation that scales:

  • Hardware-native. Single-qubit measurements, the standard readout of a quantum processor — validated on IQM and IBM hardware to a few percent. No full-state tomography.
  • Stable under scale. Representations that compare whole states by global overlap lose their signal as the system grows; local representations do not. This is why the benchmark runs to 320 qubits.

Produced by the ReLab quantum data engine — the same representation it generates from a customer's own systems.

Quickstart

from datasets import load_dataset

ds = load_dataset("SiriusQuantum/quantum-ground-states-320-qubits")["train"]
X, y = ds["quantum_features"], ds["label"]     # filter by ds["n_qubits"] for the scaling curve

Scope

  • A benchmark of representation and sample efficiency against exact ground truth; the exactly-solvable model is what enables verification at this scale.
  • Results are reported under a fixed protocol; the headline is the consistent gap between the quantum and classical representations across scales.
  • The representation is computed by the exact solver here and is identical to what a quantum processor measures on-device.

Reproducibility

Every example regenerates deterministically from recipe.json (engine commit, seed, generator). verification.json carries the benchmark result. The solver is validated to 10⁻¹⁴ against exact diagonalisation for n ≤ 12.

References

  • Lieb, Schultz, Mattis 1961 — exact free-fermion solution of the spin-½ XY / TFIM chain.
  • Huang, Broughton, Mohseni, et al. 2021 — arXiv:2011.01938 — projected (local) quantum kernels.
  • Huang, Kueng, Torlai, Albert, Preskill 2022 — arXiv:2106.12627 (Science) — provably efficient ML for quantum many-body ground-state properties.
  • Lewis, Huang, Tran, et al. 2024 — Nat. Commun. 15:895, doi:10.1038/s41467-024-45014-7 — O(log n) sample complexity for ground-state properties.
  • Thanasilp, Wang, Cerezo, Holmes 2022 — arXiv:2208.11060 — concentration of global quantum kernels.

Citation

@misc{siriusquantum2026qgs320,
  title        = {Quantum Ground States: Exact Representations to 320 Qubits},
  author       = {{Sirius Quantum}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/SiriusQuantum/quantum-ground-states-320-qubits}}
}

Produced with the ReLab quantum data engine, Sirius Quantum — https://github.com/Sirius-Quantum

License

CC-BY-4.0 — use is permitted with attribution to Sirius Quantum.

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