127 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Boehringer Ingelheim (Germany)DE4 papers
- Covestro (Germany)DE3 papers
- Institut de Recherche en Informatique FondamentaleFR3 papers
- Aisin (Japan)JP2 papers
- Aisin (United States)US2 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Université Paris CitéFR2 papers
- Alpine Quantum Technologies (Austria)AT1 paper
- BASF (Germany)DE1 paper
- Cornell UniversityUS1 paper
- Google (United States)US1 paper
- Itaúsa (Brazil)BR1 paper
13 papers
Molecular Properties from Quantum Krylov Subspace Diagonalization
Oumarou Oumarou, Pauline J. Ollitrault, Cristian L. Cortes +3
Quantum Krylov subspace diagonalization is a prominent candidate for early fault tolerant quantum simulation of many-body and molecular systems, but so far the focus has been mainl…
Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization
Dario Rocca, Cristian L. Cortes, Jerome Gonthier +7
Quantum phase estimation based on qubitization is the state-of-the-art fault-tolerant quantum algorithm for computing ground-state energies in chemical applications. In this contex…
Estimation of electrostatic interaction energies on a trapped-ion quantum computer
Pauline J. Ollitrault, Matthias Loipersberger, Robert M. Parrish +12
We present the first hardware implementation of electrostatic interaction energies using a trapped-ion quantum computer. As test system for our computation, we focus on the reducti…
Quantum Fourier Networks for Solving Parametric PDEs
Nishant Jain, Jonas Landman, Natansh Mathur +1
Many real-world problems, like modelling environment dynamics, physical processes, time series etc., involve solving Partial Differential Equations (PDEs) parameterised by problem-…
Improved Financial Forecasting via Quantum Machine Learning
Sohum Thakkar, Skander Kazdaghli, Natansh Mathur +3
Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to im…
Fault-tolerant quantum computation of molecular observables
Mark Steudtner, Sam Morley-Short, William Pol +8
Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, compara…
Drug design on quantum computers
Raffaele Santagati, Alan Aspuru-Guzik, Ryan Babbush +12
Quantum computers promise to impact industrial applications, for which quantum chemical calculations are required, by virtue of their high accuracy. This perspective explores the c…
Quantum Neural Networks for a Supply Chain Logistics Application
Randall Correll, Sean J. Weinberg, Fabio Sanches +2
Problem instances of a size suitable for practical applications are not likely to be addressed during the noisy intermediate-scale quantum (NISQ) period with (almost) pure quantum…
Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization
Oumarou Oumarou, Maximilian Scheurer, Robert M. Parrish +2
We propose the regularized compressed double factorization (RC-DF) method to classically compute compressed representations of molecular Hamiltonians that enable efficient simulati…
Reinforcement Learning for Multi-Truck Vehicle Routing Problems
Joshua Levin, Randall Correll, Takanori Ide +3
Deep reinforcement learning (RL) has been shown to be effective in producing approximate solutions to some vehicle routing problems (VRPs), especially when using policies generated…
Quantum Reinforcement Learning via Policy Iteration
El Amine Cherrat, Iordanis Kerenidis, Anupam Prakash
Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning. Reinforcement learning, o…
Expectation Values from the Single-Layer Quantum Approximate Optimization Algorithm on Ising Problems
Asier Ozaeta, Wim van Dam, Peter L. McMahon
We report on the energy-expectation-value landscapes produced by the single-layer () Quantum Approximate Optimization Algorithm (QAOA) when being used to solve Ising problems.…
Low Rank Density Matrix Evolution for Noisy Quantum Circuits
Yi-Ting Chen, Collin Farquhar, Robert M. Parrish
In this work, we present an efficient rank-compression approach for the classical simulation of Kraus decoherence channels in noisy quantum circuits. The approximation is achieved…