91 citations · 231 across the 14 of their papers we have counts for
15 papers
Polynomial-time exact diagonalization via sparse guided eigenwalks
Zachary E. Chin, Mario Motta, Javier Robledo Moreno +3
Computing quantum ground states is generically difficult, but additional structure can sometimes allow diagonalization to be recast as a more feasible problem. For example, when th…
Quantum Finite Temperature Lanczos Method
Gian Gentinetta, Friederike Metz, William Kirby +1
The computation of thermal properties of quantum many-body systems is a central challenge in our understanding of quantum mechanics. We introduce the Quantum Finite Temperature Lan…
Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer
William Kirby, Bibek Pokharel, Javier Robledo Moreno +25
Demonstrating quantum advantage over classical algorithms for ground state energy problems is an outstanding open problem in quantum computation. We experimentally demonstrate that…
Scaling Sample-Based Quantum Diagonalization on GPU-Accelerated Systems using OpenMP Offload
Robert Walkup, Juha Jäykkä, Igor Pasichnyk +14
Hybrid quantum-HPC algorithms advance research by delegating complex tasks to quantum processors and using HPC systems to orchestrate workflows and complementary computations. Samp…
Closed-loop calculations of electronic structure on a quantum processor and a classical supercomputer at full scale
Tomonori Shirakawa, Javier Robledo-Moreno, Toshinari Itoko +18
Quantum computers must operate in concert with classical computers to deliver on the promise of quantum advantage for practical problems. To achieve that, it is important to unders…
Quantum Krylov Algorithm for Szegö Quadrature
William Kirby, Yizhi Shen, Daan Camps +3
We present a quantum algorithm to evaluate matrix elements of functions of unitary operators. The method is based on calculating quadrature nodes and weights using data collected f…
Quantum chemistry with provable convergence via randomized sample-based Krylov quantum diagonalization
Samuele Piccinelli, Alberto Baiardi, Stefano Barison +12
Quantum algorithms based on classical processing of individual samples have recently emerged as a promising route to perform large-scale quantum-chemical calculations on pre-fault-…
Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization
Jeffery Yu, Javier Robledo Moreno, Joseph T. Iosue +17
Approximating the ground state of many-body systems is a key computational bottleneck underlying important applications in physics and chemistry. The most widely known quantum algo…
The quantum super-Krylov method
Adam Byrne, William Kirby, Kirk M. Soodhalter +1
The problem of estimating the ground-state energy of a quantum system is ubiquitous in chemistry and condensed matter physics. Krylov quantum diagonalization (KQD) has emerged as a…
Diagonalization of large many-body Hamiltonians on a quantum processor
Nobuyuki Yoshioka, Mirko Amico, William Kirby +16
The estimation of low energies of many-body systems is a cornerstone of computational quantum sciences. Variational quantum algorithms can be used to prepare ground states on pre-f…
Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer
Javier Robledo-Moreno, Mario Motta, Holger Haas +14
A universal quantum computer can simulate diverse quantum systems, with electronic structure for chemistry offering challenging problems for practical use cases around the hundred-…
Analysis of quantum Krylov algorithms with errors
William Kirby
This work provides a nonasymptotic error analysis of quantum Krylov algorithms based on real-time evolutions, subject to generic errors in the outputs of the quantum circuits. We p…
Practical Benchmarking of Randomized Measurement Methods for Quantum Chemistry Hamiltonians
Arkopal Dutt, William Kirby, Rudy Raymond +4
Many hybrid quantum-classical algorithms for the application of ground state energy estimation in quantum chemistry involve estimating the expectation value of a molecular Hamilton…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Subspace methods for electronic structure simulations on quantum computers
Mario Motta, William Kirby, Ieva Liepuoniute +7
Quantum subspace methods (QSMs) are a class of quantum computing algorithms where the time-independent Schrodinger equation for a quantum system is projected onto a subspace of the…