2 citations · 3 across the 3 of their papers we have counts for
6 papers · 1 filter
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
M. Emre Sahin, Edoardo Altamura, Oscar Wallis +6
We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primi…
Variational preparation of normal matrix product states on quantum computers
Ben Jaderberg, George Pennington, Kate V. Marshall +6
Preparing matrix product states (MPSs) on quantum computers is an essential routine in the simulation of many-body physics. However, widely-used schemes based on staircase circuits…
Evolving a multi-population evolutionary-QAOA on distributed QPUs
Francesca Schiavello, Edoardo Altamura, Ivano Tavernelli +2
Our work integrates an Evolutionary Algorithm (EA) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize ansatz parameters in place of traditional gradient-based m…
Boosted Imaginary Time Evolution of Matrix Product States
Benjamin C. B. Symons, Dilhan Manawadu, David Galvin +1
In this work, we consider the imaginary time evolution of matrix product states. We present a novel quantum-inspired classical method that, when combined with time evolving block d…
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
M. Emre Sahin, Benjamin C. B. Symons, Pushpak Pati +5
Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…