Perceval: A Software Platform for Discrete Variable Photonic Quantum
Computing
- URL: http://arxiv.org/abs/2204.00602v2
- Date: Mon, 13 Feb 2023 17:40:57 GMT
- Title: Perceval: A Software Platform for Discrete Variable Photonic Quantum
Computing
- Authors: Nicolas Heurtel, Andreas Fyrillas, Gr\'egoire de Gliniasty, Rapha\"el
Le Bihan, S\'ebastien Malherbe, Marceau Pailhas, Eric Bertasi, Boris
Bourdoncle, Pierre-Emmanuel Emeriau, Rawad Mezher, Luka Music, Nadia Belabas,
Beno\^it Valiron, Pascale Senellart, Shane Mansfield, and Jean Senellart
- Abstract summary: We introduce Perceval, an open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers.
Its Python front-end allows photonic circuits to be composed from basic photonic building blocks like photon sources, beam splitters, phase-shifters and detectors.
We give examples of Perceval in action by reproducing a variety of photonic experiments and simulating photonic implementations of a range of quantum algorithms.
- Score: 1.3767989047174227
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce Perceval, an open-source software platform for simulating and
interfacing with discrete-variable photonic quantum computers, and describe its
main features and components. Its Python front-end allows photonic circuits to
be composed from basic photonic building blocks like photon sources, beam
splitters, phase-shifters and detectors. A variety of computational back-ends
are available and optimised for different use-cases. These use state-of-the-art
simulation techniques covering both weak simulation, or sampling, and strong
simulation. We give examples of Perceval in action by reproducing a variety of
photonic experiments and simulating photonic implementations of a range of
quantum algorithms, from Grover's and Shor's to examples of quantum machine
learning. Perceval is intended to be a useful toolkit for experimentalists
wishing to easily model, design, simulate, or optimise a discrete-variable
photonic experiment, for theoreticians wishing to design algorithms and
applications for discrete-variable photonic quantum computing platforms, and
for application designers wishing to evaluate algorithms on available
state-of-the-art photonic quantum computers.
Related papers
- Demonstration of quantum projective simulation on a single-photon-based quantum computer [0.0]
Variational quantum algorithms show potential in effectively operating on noisy intermediate-scale quantum devices.
We present the implementation of this algorithm on Ascella, a single-photon-based quantum computer from Quandela.
arXiv Detail & Related papers (2024-04-19T09:17:15Z) - Piquasso: A Photonic Quantum Computer Simulation Software Platform [1.7708236183599542]
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.
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.
arXiv Detail & Related papers (2024-03-06T19:31:50Z) - QuantumReservoirPy: A Software Package for Time Series Prediction [44.99833362998488]
We have developed a software package to allow for quantum reservoirs to fit a common structure.
Our package results in simplified development and logical methods of comparison between quantum reservoir architectures.
arXiv Detail & Related papers (2024-01-19T13:31:29Z) - Hybrid quantum transfer learning for crack image classification on NISQ
hardware [62.997667081978825]
We present an application of quantum transfer learning for detecting cracks in gray value images.
We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
arXiv Detail & Related papers (2023-07-31T14:45:29Z) - A general-purpose single-photon-based quantum computing platform [36.56899230501635]
We report a first user-ready general-purpose quantum computing prototype based on single photons.
The device comprises a high-efficiency quantum-dot single-photon source feeding a universal linear optical network on a reconfigurable chip.
We report on a first heralded 3-photon entanglement generation, a key milestone toward measurement-based quantum computing.
arXiv Detail & Related papers (2023-06-01T16:35:55Z) - Simulation of Entanglement Generation between Absorptive Quantum
Memories [56.24769206561207]
We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories.
We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe.
We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
arXiv Detail & Related papers (2022-12-17T05:51:17Z) - A compiler for universal photonic quantum computers [0.0]
In one-way computing, the input state is not an initial product state, but a so-called cluster state.
We propose a pipeline to convert a QASM circuit into a graph representation named measurement-graph (m-graph)
We optimize the graph using ZX-Calculus before evaluating the execution on an experimental discrete variable photonic platform.
arXiv Detail & Related papers (2022-10-17T16:47:45Z) - Fock State-enhanced Expressivity of Quantum Machine Learning Models [0.0]
photonic-based bosonic data-encoding scheme embeds classical data points using fewer encoding layers.
We propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources.
arXiv Detail & Related papers (2021-07-12T07:07:39Z) - Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
We introduce new tools in qutip-qip, QuTiP's quantum information processing package.
These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features.
We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
arXiv Detail & Related papers (2021-05-20T17:06:52Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Advantages and Bottlenecks of Quantum Machine Learning for Remote
Sensing [63.69764116066747]
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, and discuss the bottlenecks of performing these algorithms on currently available open source platforms.
Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.
arXiv Detail & Related papers (2021-01-26T09:31:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.