activity
20162025
most citedEnd-to-end resource analysis for quantum interior point methods and portfolio optimization

18 citations · 23 across the 3 of their papers we have counts for

collaborators

8 papers

quant-ph2025★ 1 cited

Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers

Luning Zhao, Joshua J. Goings, Willie Aboumrad +38

We demonstrate an end-to-end workflow to model chemical reaction barriers with the quantum-classical auxiliary field quantum Monte Carlo (QC-AFQMC) algorithm with quantum tomograph…

quant-ph2022★ 4 cited

Predicting Properties of Quantum Systems with Conditional Generative Models

Haoxiang Wang, Maurice Weber, Josh Izaac +1

Machine learning has emerged recently as a powerful tool for predicting properties of quantum many-body systems. For many ground states of gapped Hamiltonians, generative models ca…

quant-ph2022★ 18 cited

End-to-end resource analysis for quantum interior point methods and portfolio optimization

Alexander M. Dalzell, B. David Clader, Grant Salton +8

We study quantum interior point methods (QIPMs) for second-order cone programming (SOCP), guided by the example use case of portfolio optimization (PO). We provide a complete quant…

quant-ph2021

Differentiable quantum computational chemistry with PennyLane

Juan Miguel Arrazola, Soran Jahangiri, Alain Delgado +15

This work describes the theoretical foundation for all quantum chemistry functionality in PennyLane, a quantum computing software library specializing in quantum differentiable pro…

quant-ph2021

General parameter-shift rules for quantum gradients

David Wierichs, Josh Izaac, Cody Wang +1

Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale quantum computers. Due to the structure of conventional parametrized quantum gates, the ev…

quant-ph2017

Quantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning

Fernando G. S. L. Brandão, Amir Kalev, Tongyang Li +3

We give two quantum algorithms for solving semidefinite programs (SDPs) providing quantum speed-ups. We consider SDP instances with constraint matrices, each of dimension ,…