181 citations · 397 across the 13 of their papers we have counts for
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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…
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…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Quantum Kernel Alignment with Stochastic Gradient Descent
Gian Gentinetta, David Sutter, Christa Zoufal +2
Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantu…