1k citations · 2.5k across the 21 of their papers we have counts for
22 papers
Bowtie VarQTE: A Resource-Efficient Quantum State Preparation Primitive
Marc Drudis, Alberto Baiardi, Mattia Chiurco +3
The preparation of quantum states is a fundamental requirement for many quantum algorithms. A native route to preparing physically structured states is based on short-time simulati…
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…
A Framework for Quantum Advantage
Olivia Lanes, Mourad Beji, Antonio D. Corcoles +12
As quantum computing approaches the threshold where certain tasks demonstrably outpace their classical machines, the need for a precise, clear, consensus-driven definition of quant…
Quantum Optimization Benchmarking Library - The Intractable Decathlon
Thorsten Koch, David E. Bernal Neira, Ying Chen +24
Through recent progress in hardware development, quantum computers have advanced to the point where benchmarking of (heuristic) quantum algorithms at scale is within reach. Particu…
Towards Practical Quantum Neural Network Diagnostics with Neural Tangent Kernels
Francesco Scala, Christa Zoufal, Dario Gerace +1
Knowing whether a Quantum Machine Learning model would perform well on a given dataset before training it can help to save critical resources. However, gathering a priori informati…
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…
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…
Stochastic Approximation of Variational Quantum Imaginary Time Evolution
Julien Gacon, Christa Zoufal, Giuseppe Carleo +1
The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning. Since simulating quantum…
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…
Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics
Elie Bermot, Christa Zoufal, Michele Grossi +4
The standard model (SM) of particle physics represents a theoretical paradigm for the description of the fundamental forces of nature. Despite its broad applicability, the SM does…
Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection
Christa Zoufal, Ryan V. Mishmash, Nitin Sharma +6
We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. Th…
Generative Quantum Machine Learning
Christa Zoufal
The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation pro…
A variational quantum algorithm for the Feynman-Kac formula
Hedayat Alghassi, Amol Deshmukh, Noelle Ibrahim +3
We propose an algorithm based on variational quantum imaginary time evolution for solving the Feynman-Kac partial differential equation resulting from a multidimensional system of…
Error Bounds for Variational Quantum Time Evolution
Christa Zoufal, David Sutter, Stefan Woerner
Variational quantum time evolution allows us to simulate the time dynamics of quantum systems with near-term compatible quantum circuits. Due to the variational nature of this meth…
Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
Julien Gacon, Christa Zoufal, Giuseppe Carleo +1
The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution.…
The power of quantum neural networks
Amira Abbas, David Sutter, Christa Zoufal +3
Fault-tolerant quantum computers offer the promise of dramatically improving machine learning through speed-ups in computation or improved model scalability. In the near-term, howe…
Variational Quantum Boltzmann Machines
Christa Zoufal, Aurélien Lucchi, Stefan Woerner
This work presents a novel realization approach to Quantum Boltzmann Machines (QBMs). The preparation of the required Gibbs states, as well as the evaluation of the loss function's…
Quantum-Enhanced Simulation-Based Optimization
Julien Gacon, Christa Zoufal, Stefan Woerner
In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective function that is computatio…
Iterative Quantum Amplitude Estimation
Dmitry Grinko, Julien Gacon, Christa Zoufal +1
We introduce a new variant of Quantum Amplitude Estimation (QAE), called Iterative QAE (IQAE), which does not rely on Quantum Phase Estimation (QPE) but is only based on Grover's A…
Option Pricing using Quantum Computers
Nikitas Stamatopoulos, Daniel J. Egger, Yue Sun +4
We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup com…
Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
Christa Zoufal, Aurélien Lucchi, Stefan Woerner
Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization of the advantage often requires the ability to load classica…