181 citations · 227 across the 7 of their papers we have counts for
7 papers
Efficient re-sampling in quasi-probability decompositions
Sara Santos, Stefan Woerner, Vincenzo Savona +1
Near-term quantum devices are limited by noise and hardware constraints, motivating algorithmic approaches that trade circuit complexity for increased sampling overhead. Quasi-prob…
Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting
Filip B. Maciejewski, Stuart Hadfield, Oscar Wallis +5
Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-awa…
Quantum-enhanced Markov Chain Monte Carlo for Combinatorial Optimization
Kate V. Marshall, Daniel J. Egger, Michael Garn +4
Quantum computing offers an alternative paradigm for addressing combinatorial optimization problems compared to classical computing. Despite recent hardware improvements, the execu…
Quantum Theory and Application of Contextual Optimal Transport
Nicola Mariella, Albert Akhriev, Francesco Tacchino +9
Optimal Transport (OT) has fueled machine learning (ML) across many domains. When paired data measurements are coupled to covariates, a challenging c…
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…
Provable bounds for noise-free expectation values computed from noisy samples
Samantha V. Barron, Daniel J. Egger, Elijah Pelofske +4
In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the im…
Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
Alistair Letcher, Stefan Woerner, Christa Zoufal
The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central bottleneck in the scalabili…