output
20162025
most citedA density-matrix renormalization group algorithm for simulating quantum circuits with a finite fidelity

69 citations

Showing 2022Show all

11 papers · 1 filter

quant-ph2022★ 1 cited

A graph-state based synthesis framework for Clifford isometries

Timothée Goubault de Brugière, Simon Martiel, Christophe Vuillot

We tackle the problem of Clifford isometry compilation, i.e, how to synthesize a Clifford isometry into an executable quantum circuit. We propose a simple framework for synthesis t…

cs.DC2022★ 2 cited

Node-Type-Based Load-Balancing Routing for Parallel Generalized Fat-Trees

John Gliksberg, Jean-Noel Quintin, Pedro Javier Garcia

High-Performance Computing (HPC) clusters are made up of a variety of node types (usually compute, I/O, service, and GPGPU nodes) and applications don't use nodes of a different ty…

cs.DC2022★ 3 cited

High-Quality Fault-Resiliency in Fat-Tree Networks (Extended Abstract)

John Gliksberg, Antoine Capra, Alexandre Louvet +2

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast d…

quant-ph2022★ 11 cited

QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum Systems

Turbasu Chatterjee, Arnav Das, Subhayu Kumar Bala +3

In the recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state-of-the-art classical simulato…

quant-ph2022★ 69 cited

A density-matrix renormalization group algorithm for simulating quantum circuits with a finite fidelity

Thomas Ayral, Thibaud Louvet, Yiqing Zhou +3

We develop a density-matrix renormalization group (DMRG) algorithm for the simulation of quantum circuits. This algorithm can be seen as the extension of time-dependent DMRG from t…

cs.LG2022

FFCNN: Fast FPGA based Acceleration for Convolution neural network inference

F. Keddous, H-N. Nguyen, A. Nakib

We present a new efficient OpenCL-based Accelerator for large scale Convolutional Neural Networks called Fast Inference on FPGAs for Convolution Neural Network (FFCNN). FFCNN is ba…

quant-ph2022

Open Source Variational Quantum Eigensolver Extension of the Quantum Learning Machine (QLM) for Quantum Chemistry

Mohammad Haidar, Marko J. Rančić, Thomas Ayral +2

Quantum Chemistry (QC) is one of the most promising applications of Quantum Computing. However, present quantum processing units (QPUs) are still subject to large errors. Therefore…

cs.DC2022★ 58 cited

Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence

Jorge Ejarque, Rosa M. Badia, Loïc Albertin +34

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The co…

quant-ph2022★ 16 cited

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

Timothée Goubault de Brugière, Marc Baboulin, Benoît Valiron +2

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical compute…

quant-ph2022★ 9 cited

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

Timothée Goubault de Brugière, Marc Baboulin, Benoît Valiron +2

Current proposals for quantum compilers require the synthesis and optimization of linear reversible circuits and among them CNOT circuits. Since these circuits represent a signific…

quant-ph2022★ 35 cited

Reducing the Depth of Linear Reversible Quantum Circuits

Timothée Goubault de Brugière, Marc Baboulin, Benoît Valiron +2

In 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 minimi…