Piquasso: A Photonic Quantum Computer Simulation Software Platform

Zoltán Kolarovszki1,2, Tomasz Rybotycki3, Péter Rakyta4, Ágoston Kaposi1,2, Boldizsár Poór2,5, Szabolcs Jóczik1,2,6, Dániel T. R. Nagy1,4, Henrik Varga2, Kareem H. El-Safty1,7, Gregory Morse2, Michał Oszmaniec3,8, Tamás Kozsik2, and Zoltán Zimborás1,2,9

1Quantum Computing and Quantum Information Research Group, HUN-REN Wigner Research Centre for Physics, Konkoly–Thege Miklós út 29-33, H-1525 Budapest, Hungary
2Department of Programming Languages and Compilers, Eötvös Loránd University, Pázmány Péter sétány 1/a, H-1117 Budapest, Hungary
3Center for Theoretical Physics, Polish Academy of Sciences, Al. Lotników 32/46, 02-668 Warszawa, Poland
4Department of Physics of Complex Systems, Eötvös Loránd University, Pázmány Péter sétány 1/a, H-1117 Budapest, Hungary
5Quantinuum, 17 Beaumont Street, Oxford, OX1 2NA, United Kingdom
6Robert Bosch Kft., Gyömrői út 104., H-1103 Budapest, Hungary
7Department of Computer Engineering, Technical University of Munich, Arcisstraße 21, 80333 München, Germany
8NASK National Research Institute, Kolska 12, 01-045 Warsaw, Poland
9Algorithmiq Ltd, Kanavakatu 3C 00160 Helsinki, Finland

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Abstract

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results.

Photonic quantum computing has attracted growing interest due to experimental demonstrations of quantum advantage and promising routes toward fault-tolerant architectures. Photons offer several advantages, including room-temperature operation, mature optical technologies, and naturally robust entanglement. Nonetheless, photonic quantum computers still face significant limitations, underscoring the importance of efficient classical simulators. These simulators are crucial for benchmarking noisy hardware, designing circuits, exploring heuristic algorithms using quantum neural networks, and assessing algorithmic resilience to noise.

To aid photonic quantum computing research, we introduce Piquasso (PhotonIc QUAntum computer Simulator SOftware), a simulator for both discrete and continuous-variable photonic quantum computing. Piquasso provides a high-performance, flexible, and extensible platform for simulating, analyzing, and differentiating photonic quantum circuits. It supports quantum advantage schemes such as (Gaussian) Boson Sampling and enables photonic quantum machine learning through integration with TensorFlow or JAX.

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[19] Priya J. Nadkarni, Narayanan Rengaswamy, and Bane Vasić, "Tutorial on Quantum Error Correction for 2024 Quantum Information Knowledge (QuIK) Workshop", arXiv:2407.12737, (2024).

[20] Zoltán Kolarovszki, Dániel T. R. Nagy, and Zoltán Zimborás, "On the learning abilities of photonic continuous-variable Born machines", arXiv:2410.11785, (2024).

[21] Yong Kwon, Martino Bernard, Leonardo Limongi, Gioele Piccoli, Mher Ghulinyan, and Byung-Soo Choi, "Full Software Control on MZI-Based Photonic Integrated Circuit", IEEE Access 12, 146291 (2024).

[22] Simon Sekavčnik, Kareem El-Safty, and Janis Nötzel, "PhotonWeave", The Journal of Open Source Software 10 107, 7468 (2025).

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