Pith Number

pith:YUARJKYZ

pith:2024:YUARJKYZDCMZ7KFRTEFDJKN7I4

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Quantum computing with Qiskit

Ali Javadi-Abhari, Andrew W. Cross, Blake R. Johnson, Christopher J. Wood, Jake Lishman, Jay M. Gambetta, Julien Gacon, Kevin Krsulich, Lev S. Bishop, Matthew Treinish, Paul D. Nation, Simon Martiel

Qiskit provides a layered architecture for representing, optimizing, and executing quantum circuits to solve condensed matter physics problems via hybrid computations.

arxiv:2405.08810 v3 · 2024-05-14 · quant-ph · cs.ET

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Claims

C1strongest claim

We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits.

C2weakest assumption

The described software architecture and workflow features accurately reflect the current state and performance of the Qiskit implementation without undisclosed limitations or version-specific behaviors.

C3one line summary

Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.

References

112 extracted · 112 resolved · 4 Pith anchors

[1] In quantum computing there exist two main primitives for captur- ing the output of a quantum circuit: sampling output bitstrings, or estimating observable expectation values

[2] First, a classical problem is mapped to quan- tum computation by generating circuits that encode the problem

[3] This includes advanced circuit visualizers that display large circuits of varying characteristics (e.g

[4] quantum computer util- ity

[5] URL https: //qiskit.github.io/qiskit/

Formal links

1 machine-checked theorem link

Cited by

232 papers in Pith

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QCNN with Rough Path Signature Kernels

SQGen: Structured Quantum Image Generation with Latent-Modulated Quantized Tensor Trains

Receipt and verification

First computed	2026-07-05T08:34:18.914714Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c50114ab1918999fa8b1990a34a9bf4730eef6518413ab02ef117e0d04c369ca

Aliases

arxiv: 2405.08810 · arxiv_version: 2405.08810v3 · doi: 10.48550/arxiv.2405.08810 · pith_short_12: YUARJKYZDCMZ · pith_short_16: YUARJKYZDCMZ7KFR · pith_short_8: YUARJKYZ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YUARJKYZDCMZ7KFRTEFDJKN7I4 \
  | 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: c50114ab1918999fa8b1990a34a9bf4730eef6518413ab02ef117e0d04c369ca

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4ec081d857ee282bb047c4a8c9f4bf7c547b71b2fa7d9138d03aede4baab9e1e",
    "cross_cats_sorted": [
      "cs.ET"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "quant-ph",
    "submitted_at": "2024-05-14T17:55:32Z",
    "title_canon_sha256": "03795928ddc58606bfef23357762fb235b76fc104c80836572c4b9213927a84d"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 3
  }
}