Simulating photonic devices with noisy optical elements
Quantum computers are inherently affected by noise. While in the long term, error correction codes will account for noise at the cost of increasing physical qubits, in the near term, the performance of any quantum algorithm should be tested and simulated in the presence of noise. As noise acts on the hardware, the classical simulation of a quantum algorithm should not be agnostic on the platform used for the computation. In this paper, we apply the recently proposed noisy gates approach to efficiently simulate noisy optical circuits described in the dual rail framework. The evolution of the state vector is simulated directly, without requiring the mapping to the density matrix framework. Notably, we test the method on both the gate-based and measurement-based quantum computing models, showing that the approach is very versatile. We also evaluate the performance of a photonic variational quantum algorithm to solve the MAX-2-CUT problem. In particular we design and simulate an ansatz, which is resilient to photon losses up to <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mi mathvariant="normal">p</a:mi><a:mo>∼</a:mo><a:msup><a:mn>10</a:mn><a:mrow><a:mo>−</a:mo><a:mn>3</a:mn></a:mrow></a:msup></a:mrow></a:math> making it relevant for near-term applications. Published by the American Physical Society 2024
Authors
- Angelo Bassi (ORCID: https://orcid.org/0000-0001-7500-387X)
- Michele Vischi (ORCID: https://orcid.org/0000-0002-5724-7421)
- Filippo Cerocchi (ORCID: https://orcid.org/0000-0002-0303-4738)
- Massimiliano Proietti (ORCID: https://orcid.org/0000-0003-4680-9369)
- Giovanni Di Bartolomeo (ORCID: https://orcid.org/0000-0002-1792-7043)
- Seid Koudia (ORCID: https://orcid.org/0000-0002-7533-6778)
- Massimiliano Dispenza
Institutions
- University of Trieste (IT)
- Istituto Nazionale di Fisica Nucleare (IT)
- Leonardo (United Kingdom) (GB)
Publication Details
- Journal
- Physical Review Research
- Published
- 2024-09-26
- DOI
- https://doi.org/10.1103/physrevresearch.6.033337
- Citations
- 5
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.74
Funders
- Istituto Nazionale di Fisica Nucleare Sezione di Padova
- Università degli Studi di Trieste
- Ministero dell'Università e della Ricerca