activity
20032026
most citedOptimal Co-linear Gaussian Beams for Spontaneous Parametric Down-Conversion

139 citations · 749 across the 25 of their papers we have counts for

collaborators

28 papers

quant-ph2026

Explainable quantum regression algorithm with encoded data structure

C. -C. Joseph Wang, F. Perkkola, I. Salmenperä +2

Hybrid variational quantum algorithms are promising for solving practical problems, such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and…

quant-ph2025

Optimizing State Preparation for Variational Quantum Regression on NISQ Hardware

Frans Perkkola, Ilmo Salmeperä, Arianne Meijer-van de Griend +3

The execution of quantum algorithms on modern hardware is often constrained by noise and qubit decoherence, limiting the circuit depth and the number of gates that can be executed.…

quant-ph2024★ 50 cited

Integrating Quantum Computing Resources into Scientific HPC Ecosystems

Thomas Beck, Alessandro Baroni, Ryan Bennink +15

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces cha…

quant-ph2023★ 9 cited

TISCC: A Surface Code Compiler and Resource Estimator for Trapped-Ion Processors

Tyler LeBlond, Justin G. Lietz, Christopher M. Seck +1

We introduce the Trapped-Ion Surface Code Compiler (TISCC), a software tool that generates circuits for a universal set of surface code patch operations in terms of a native trappe…

quant-ph2023★ 13 cited

Realistic Cost to Execute Practical Quantum Circuits using Direct Clifford+T Lattice Surgery Compilation

Tyler LeBlond, Christopher Dean, George Watkins +1

We report a resource estimation pipeline that explicitly compiles quantum circuits expressed using the Clifford+T gate set into a surface code lattice surgery instruction set. The…

quant-ph2023★ 5 cited

Explainable quantum regression algorithm with encoded data structure

C. -C. Joseph Wang, F. Perkkola, I. Salmenperä +3

Hybrid variational quantum algorithms (VQAs) are promising for solving practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning…