1k citations · 4.8k across the 38 of their papers we have counts for
41 papers
Binary Optimization with Complex Constraints via Quantum Approximate Multi-Objective Optimization
Andres Ruiz, Soumyadip Ghosh, Stefan Woerner
We show that a class of binary optimization problems with complex non-quadratic objectives or constraints can be reformulated as multi-objective quadratic unconstrained binary opti…
Towards quantum-enabled cell-centric therapeutics
Saugata Basu, Jannis Born, Aritra Bose +30
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future qua…
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…
Variational Quantum Time Evolution without the Quantum Geometric Tensor
Julien Gacon, Jannes Nys, Riccardo Rossi +2
The real- and imaginary-time evolution of quantum states are powerful tools in physics, chemistry, and beyond, to investigate quantum dynamics, prepare ground states or calculate t…
Well-conditioned multi-product formulas for hardware-friendly Hamiltonian simulation
Almudena Carrera Vazquez, Daniel J. Egger, David Ochsner +1
Simulating the time-evolution of a Hamiltonian is one of the most promising applications of quantum computers. Multi-Product Formulas (MPFs) are well suited to replace standard pro…
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…
The complexity of quantum support vector machines
Gian Gentinetta, Arne Thomsen, David Sutter +1
Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any kn…
Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware
Johannes Weidenfeller, Lucia C. Valor, Julien Gacon +4
Quantum computers may provide good solutions to combinatorial optimization problems by leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often presented…
Effective dimension of machine learning models
Amira Abbas, David Sutter, Alessio Figalli +1
Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i.e., to understand the generalization power…
Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms
Nikitas Stamatopoulos, Guglielmo Mazzola, Stefan Woerner +1
We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing qu…
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…
Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan +20
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In…
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.…
Quasiprobability decompositions with reduced sampling overhead
Christophe Piveteau, David Sutter, Stefan Woerner
Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability me…
A Threshold for Quantum Advantage in Derivative Pricing
Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola +3
We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum d…
Quantum speedups for convex dynamic programming
David Sutter, Giacomo Nannicini, Tobias Sutter +1
We present a quantum algorithm to solve dynamic programming problems with convex value functions. For linear discrete-time systems with a -dimensional state space of size , t…
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…
Warm-starting quantum optimization
Daniel J. Egger, Jakub Marecek, Stefan Woerner
There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, wh…
Enhancing the Quantum Linear Systems Algorithm using Richardson Extrapolation
Almudena Carrera Vazquez, Ralf Hiptmair, Stefan Woerner
We present a quantum algorithm to solve systems of linear equations of the form , where is a tridiagonal Toeplitz matrix and results from d…
Quantum algorithm for alchemical optimization in material design
Panagiotis Kl. Barkoutsos, Fotios Gkritsis, Pauline J. Ollitrault +3
The development of tailored materials for specific applications is an active field of research in chemistry, material science and drug discovery. The number of possible molecules t…
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 Computing for Finance: State of the Art and Future Prospects
Daniel J. Egger, Claudio Gambella, Jakub Marecek +6
This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to qua…