Benchmarking quantum co-processors in an application-centric, hardware-agnostic and scalable way

Existing protocols for benchmarking current quantum co-processors fail to meet the usual standards for assessing the performance of High-Performance-Computing platforms. After a synthetic review of these protocols -- whether at the gate, circuit or application level -- we introduce a new benchmark, dubbed Atos Q-score (TM), that is application-centric, hardware-agnostic and scalable to quantum advantage processor sizes and beyond. The Q-score measures the maximum number of qubits that can be used effectively to solve the MaxCut combinatorial optimization problem with the Quantum Approximate Optimization Algorithm. We give a robust definition of the notion of effective performance by introducing an improved approximation ratio based on the scaling of random and optimal algorithms. We illustrate the behavior of Q-score using perfect and noisy simulations of quantum processors. Finally, we provide an open-source implementation of Q-score that makes it easy to compute the Q-score of any quantum hardware

Reconstruction, recalage et modélisation 4D du mouvement du ventricule gauche du cœur humain pour le traitement d'images médicales

Les travaux exposés dans ce mémoire ont été menés aux Laboratoires d'Electronique Philips, devenus Philips Recherche France, dans le groupe Medical Imaging Systems d'octobre 1999 à novembre 2001 dans la cadre d'une convention CIFRE entre la société Philips France et l'INRIA, laboratoire d'accueil EPIDAURE. Notre thème initial était le "recalage spatio-temporel d'organes évoluant dans le temps". Notre attention s'est très vite focalisée sur l'imagerie cardiaque, pour les intérêts scientifique, clinique et industriel particuliers qu'elle suscite. Nous avons commencé nos travaux sur l'imagerie par résonance magnétique dite "marquée" (MR tagging). Ils forment la première partie de ce mémoire. Nous avons mis au point un algorithme de détection des lignes de marquage (tags) très efficace, une nouvelle classe de déformation appliquée au mouvement des parois, et les méthodes de calcul de paramètres cliniques qui en sont dérivées...The present PhD thesis is devoted novel 3D and 4D reconstruction and registration techniques for cardiac imaging. It was made within the "Philips Research France" laboratories, Medical Imaging Systems group, from October 1999 to November 2001, with academic collaboration with INRIA's EPIDAURE project. Part I describes my work on MRI tagging images, including a very fast and accurate tag detection algorithm, a new interpolation technique for wall motion computation from the grid points and applications to the acquisation of quantitative and clinical motion parameters. Part II introduces novel methods for the building of a 3D compact deformation model of the human left ventricle, then for a statistical 4D (3D+t) model. Part III is devoted to the application of Part II models to surface-based registration. Relevant applications are shown on the wall motion computation in 3D echocardiography...ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Quantum circuits synthesis using Householder transformations

International audienceThe synthesis of a quantum circuit consists in decomposing a unitary matrix into a series of elementary operations. In this paper, we propose a circuit synthesis method based on the QR factorization via Householder transformations. We provide a two-step algorithm: during the rst step we exploit the speci c structure of a quantum operator to compute its QR factorization, then the factorized matrix is used to produce a quantum circuit. We analyze several costs (circuit size and computational time) and compare them to existing techniques from the literature. For a nal quantum circuit twice as large as the one obtained by the best existing method, we accelerate the computation by orders of magnitude

Decoding techniques applied to the compilation of CNOT circuits for NISQ architectures

International audienceCurrent proposals for quantum compilers require the synthesis and optimization of linear reversible circuits and among them CNOT circuits. Since these circuits represent a significant part of the cost of running an entire quantum circuit, we aim at reducing their size. In this paper we present a new algorithm for the synthesis of CNOT circuits based on the solution of the syndrome decoding problem. Our method addresses the case of ideal hardware with an all-to-all qubit connectivity and the case of near-term quantum devices with restricted connectivity. For both cases, we present benchmarks showing that our algorithm outperforms existing algorithms

Reducing the depth of linear reversible quantum circuits

International audienceIn quantum computing the decoherence time of the qubits determines the computation time available and this time is very limited when using current hardware. In this paper we minimize the execution time (the depth) for a class of circuits referred to as linear reversible circuits, which has many applications in quantum computing (e.g., stabilizer circuits, “CNOT+T” circuits, etc.). We propose a practical formulation of a divide and conquer algorithm that produces quantum circuits that are twice as shallow as those produced by existing algorithms. We improve the theoretical upper bound of the depth in the worst case for some range of qubits. We also propose greedy algorithms based on cost minimization to find more optimal circuits for small or simple operators. Overall, we manage to consistently reduce the total depth of a class of reversible functions, with up to 92% savings in an ancilla-free case and up to 99% when ancillary qubits are available

Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

International audienceLinear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: The custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150), while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible

< QC | HPC >: Quantum for HPC

Quantum Computing (QC) describes a new way of computing based on the principles of quantum mechanics. From a High Performance Computing (HPC) perspective, QC needs to be integrated: at a system level, where quantum computer technologies need to be integrated in HPC clusters; at a programming level, where the new disruptive ways of programming devices call for a full hardware-software stack to be built; at an application level, where QC is bound to lead to disruptive changes in the complexity of some applications so that compute-intensive or intractable problems in the HPC domain might become tractable in the future. The White Paper QC for HPC focuses on the technology integration of QC in HPC clusters, gives an overview of the full hardware-software stack and QC emulators, and highlights promising customised QC algorithms for near-term quantum computers and its impact on HPC applications. In addition to universal quantum computers, we will describe non-universal QC where appropriate. Recent research references will be used to cover the basic concepts. Thetarget audience of this paper is the European HPC community: members of HPC centres, HPC algorithm developers, scientists interested in the co-design for quantum hardware, benchmarking, etc

Progress of the CHARA/SPICA project

International audienceCHARA/SPICA (Stellar Parameters and Images with a Cophased Array) is currently being developed at Observatoire de la Cote d’Azur. It will be installed at the visible focus of the CHARA Array by the end of 2021. It has been designed to perform a large survey of fundamental stellar parameters with, in the possible cases, a detailed imaging of the surface or environment of stars. To reach the required precision and sensitivity, CHARA/SPICA combines a low spectral resolution mode R = 140 in the visible and single-mode fibers fed by the AO stages of CHARA. This setup generates additional needs before the interferometric combination: the compensation of atmospheric refraction and longitudinal dispersion, and the fringe stabilization. In this paper, we present the main features of the 6-telescopes fibered visible beam combiner (SPICA-VIS) together with the first laboratory and on-sky results of the fringe tracker (SPICA-FT). We describe also the new fringe-tracker simulator developed in parallel to SPICA-FT

ETP4HPC's SRA 5 - Strategic Research Agenda for High-Performance Computing in Europe - 2022

International audienc