arXiv subjects

Eric Bertasi

Publications and source records attributed to Eric Bertasi.

3 recordsLinked to original sources

arXiv2023

One nine availability of a Photonic Quantum Computer on the Cloud toward HPC integration

The integration of Quantum Computers (QC) within High-Performance Computing (HPC) environments holds significant promise for solving real-world problems by leveraging the strengths of both computational paradigms. However, the integration of a complex QC platform in an HPC infrastructure poses several challenges, such as operation stability in non-laboratory like environments, and scarce access for maintenance. Currently, only a few fully-assembled QCs currently exist worldwide, employing highly heterogeneous and cutting-edge technologies. These platforms are mostly used for research purposes, and often bear closer resemblance to laboratory assemblies rather than production-ready, stable, and consistently-performing turnkey machines. Moreover, public cloud services with access to such quantum computers are scarce and their availability is generally limited to few days per week. In November 2022, we introduced the first cloud-accessible general-purpose quantum computer based on single photons. One of the key objectives was to maintain the platform's availability as high as possible while anticipating seamless compatibility with HPC hosting environment. In this article, we describe the design and implementation of our cloud-accessible quantum computing platform, and demonstrate one nine availability (92 %) for external users during a six-month period, higher than most online services. This work lay the foundation for advancing quantum computing accessibility and usability in hybrid HPC-QC infrastructures.

quant-ph↗

arXiv2023

A general-purpose single-photon-based quantum computing platform

Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the advantages of low decoherence, information processing with modest cryogenic requirements, and native integration with classical and quantum networks. To date, quantum computing demonstrations with light have implemented specific tasks with specialized hardware, notably Gaussian Boson Sampling which permitted quantum computational advantage to be reached. Here 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 for which hardware errors are compensated by a machine-learned transpilation process. Our full software stack allows remote control of the device to perform computations via logic gates or direct photonic operations. For gate-based computation we benchmark one-, two- and three-qubit gates with state-of-the art fidelities of $99.6\pm0.1 \%$, $93.8\pm0.6 \%$ and $86\pm1.2 \%$ respectively. We also implement a variational quantum eigensolver, which we use to calculate the energy levels of the hydrogen molecule with high accuracy. For photon native computation, we implement a classifier algorithm using a $3$-photon-based quantum neural network and report a first $6$-photon Boson Sampling demonstration on a universal reconfigurable integrated circuit. Finally, we report on a first heralded 3-photon entanglement generation, a key milestone toward measurement-based quantum computing.

quant-ph↗

arXiv2023

Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

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.

quant-ph↗