Improving quantum simulation efficiency of final state radiation with dynamic quantum circuits

Plato Deliyannis (Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA) ; James Sud (Department of Physics, UC Berkeley, Berkeley, California 94720, USA) ; Diana Chamaki (Department of Physics, UC Berkeley, Berkeley, California 94720, USA) ; Zoë Webb-Mack (Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA) ; Christian W. Bauer (Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA) ; et al. - Show all 6 authors

B. Nachman et al. [Phys. Rev. Lett. 126, 062001 (2021)] recently introduced an algorithm (QPS) for simulating parton showers with intermediate flavor states using polynomial resources on a digital quantum computer. We make use of a new quantum hardware capability called dynamical quantum computing to improve the scaling of this algorithm, which significantly improves the method precision. In particular, we modify the quantum parton shower circuit to incorporate midcircuit qubit measurements, resets, and quantum operations conditioned on classical information. This reduces the computational depth from O(N5log2(N)2) to O(N3log2(N)2) and the qubit requirements from O(Nlog2(N)) to O(N). Using “matrix product state” state vector simulators, we demonstrate that the improved algorithm yields expected results for 2, 3, 4, and 5-steps of the algorithm. We compare absolute costs with the original QPS algorithm, and show that dynamical quantum computing can significantly reduce costs in the class of digital quantum algorithms representing quantum walks (which includes the QPS). Python code that implements QPS, both with and without dynamic gates, is publicly available on Github.

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      "value": "B. Nachman et al. [Phys. Rev. Lett. 126, 062001 (2021)] recently introduced an algorithm (QPS) for simulating parton showers with intermediate flavor states using polynomial resources on a digital quantum computer. We make use of a new quantum hardware capability called dynamical quantum computing to improve the scaling of this algorithm, which significantly improves the method precision. In particular, we modify the quantum parton shower circuit to incorporate midcircuit qubit measurements, resets, and quantum operations conditioned on classical information. This reduces the computational depth from <math><mi>O</mi><mo>(</mo><msup><mi>N</mi><mn>5</mn></msup><msub><mi>log</mi><mn>2</mn></msub><mo>(</mo><mi>N</mi><msup><mo>)</mo><mn>2</mn></msup><mo>)</mo></math> to <math><mi>O</mi><mo>(</mo><msup><mi>N</mi><mn>3</mn></msup><msub><mi>log</mi><mn>2</mn></msub><mo>(</mo><mi>N</mi><msup><mo>)</mo><mn>2</mn></msup><mo>)</mo></math> and the qubit requirements from <math><mi>O</mi><mo>(</mo><mi>N</mi><msub><mi>log</mi><mn>2</mn></msub><mo>(</mo><mi>N</mi><mo>)</mo><mo>)</mo></math> to <math><mi>O</mi><mo>(</mo><mi>N</mi><mo>)</mo></math>. Using \u201cmatrix product state\u201d state vector simulators, we demonstrate that the improved algorithm yields expected results for 2, 3, 4, and 5-steps of the algorithm. We compare absolute costs with the original QPS algorithm, and show that dynamical quantum computing can significantly reduce costs in the class of digital quantum algorithms representing quantum walks (which includes the QPS). Python code that implements QPS, both with and without dynamic gates, is publicly available on Github."
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Published on:
10 August 2022
Publisher:
APS
Published in:
Physical Review D , Volume 106 (2022)
Issue 3
DOI:
https://doi.org/10.1103/PhysRevD.106.036007
arXiv:
2203.10018
Copyrights:
Published by the American Physical Society
Licence:
CC-BY-4.0

Fulltext files: