Search Results for author: Jakob S. Kottmann

Found 8 papers, 6 papers with code

Quantum compression with classically simulatable circuits

no code implementations6 Jul 2022 Abhinav Anand, Jakob S. Kottmann, Alán Aspuru-Guzik

As we continue to find applications where the currently available noisy devices exhibit an advantage over their classical counterparts, the efficient use of quantum resources is highly desirable.

A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers

1 code implementation11 Nov 2020 Jakob S. Kottmann, Abhinav Anand, Alán Aspuru-Guzik

We show that, within our framework, the gradient of an expectation value with respect to a parameterized n-fold fermionic excitation can be evaluated by four expectation values of similar form and size, whereas most standard approaches based on the direct application of the parameter-shift-rule come with an associated cost of O(2^(2n)) expectation values.

Quantum Physics Chemical Physics Computational Physics

Tequila: A platform for rapid development of quantum algorithms

4 code implementations5 Nov 2020 Jakob S. Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza, Alba Cervera-Lierta, Cyrille Lavigne, Tzu-Ching Yen, Vladyslav Verteletskyi, Philipp Schleich, Abhinav Anand, Matthias Degroote, Skylar Chaney, Maha Kesibi, Artur F. Izmaylov, Alán Aspuru-Guzik

As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in high demand for flexible and reliable ways to implement, test, and share new ideas.

Quantum Physics Chemical Physics Computational Physics

The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation

3 code implementations28 Sep 2020 Alba Cervera-Lierta, Jakob S. Kottmann, Alán Aspuru-Guzik

We present the meta-VQE, an algorithm capable to learn the ground state energy profile of a parametrized Hamiltonian.

Quantum Physics

Reducing qubit requirements while maintaining numerical precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach

1 code implementation6 Aug 2020 Jakob S. Kottmann, Philipp Schleich, Teresa Tamayo-Mendoza, Alán Aspuru-Guzik

We present a basis-set-free approach to the variational quantum eigensolver using an adaptive representation of the spatial part of molecular wavefunctions.

Quantum Physics Chemical Physics Computational Physics

Quantum Computer-Aided design of Quantum Optics Hardware

2 code implementations4 Jun 2020 Jakob S. Kottmann, Mario Krenn, Thi Ha Kyaw, Sumner Alperin-Lea, Alán Aspuru-Guzik

It is not clear how the full potential of large quantum systems can be exploited.

Quantum Physics Computational Physics Optics

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

1 code implementation5 Jul 2015 Robert J. Harrison, Gregory Beylkin, Florian A. Bischoff, Justus A. Calvin, George I. Fann, Jacob Fosso-Tande, Diego Galindo, Jeff R. Hammond, Rebecca Hartman-Baker, Judith C. Hill, Jun Jia, Jakob S. Kottmann, M-J. Yvonne Ou, Laura E. Ratcliff, Matthew G. Reuter, Adam C. Richie-Halford, Nichols A. Romero, Hideo Sekino, William A. Shelton, Bryan E. Sundahl, W. Scott Thornton, Edward F. Valeev, Álvaro Vázquez-Mayagoitia, Nicholas Vence, Yukina Yokoi

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations.

Mathematical Software Computational Engineering, Finance, and Science Numerical Analysis

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