377 citations · 2.5k across the 36 of their papers we have counts for
37 papers
"Train classical, deploy quantum" requires rethinking generalization
Snehal Raj, Natansh Mathur, Alejandro Perdomo-Ortiz
Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum generative models are considered…
Generative Learning of Continuous Data by Tensor Networks
Alex Meiburg, Jing Chen, Jacob Miller +3
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervise…
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla +1
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in th…
Qubit seriation: Improving data-model alignment using spectral ordering
Atithi Acharya, Manuel Rudolph, Jing Chen +2
With the advent of quantum and quantum-inspired machine learning, adapting the structure of learning models to match the structure of target datasets has been shown to be crucial f…
Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization
Javier Lopez-Piqueres, Jing Chen, Alejandro Perdomo-Ortiz
Constrained combinatorial optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence…
Decomposition of Matrix Product States into Shallow Quantum Circuits
Manuel S. Rudolph, Jing Chen, Jacob Miller +2
The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed tensor network (TN) algorithms to scale to even…
Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
Manuel S. Rudolph, Jacob Miller, Danial Motlagh +3
While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the us…
Do Quantum Circuit Born Machines Generalize?
Kaitlin Gili, Mohamed Hibat-Allah, Marta Mauri +2
In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distributio…
Neural network enhanced measurement efficiency for molecular groundstates
Dmitri Iouchtchenko, Jérôme F. Gonthier, Alejandro Perdomo-Ortiz +1
It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state pr…
Generalization Metrics for Practical Quantum Advantage in Generative Models
Kaitlin Gili, Marta Mauri, Alejandro Perdomo-Ortiz
As the quantum computing community gravitates towards understanding the practical benefits of quantum computers, having a clear definition and evaluation scheme for assessing pract…
ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
Manuel S. Rudolph, Sukin Sim, Asad Raza +5
Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of near to mid-term quantum computers. There has been an increasing effort to stud…
FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits
Frederic Sauvage, Sukin Sim, Alexander A. Kunitsa +3
When compared to fault-tolerant quantum computational strategies, variational quantum algorithms stand as one of the candidates with the potential of achieving quantum advantage fo…
GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models
Javier Alcazar, Mohammad Ghazi Vakili, Can B. Kalayci +1
We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator-Enhanced Optimization (GEO) strategy is…
Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer
Manuel S. Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa +3
Generating high-quality data (e.g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such task…
Classical versus Quantum Models in Machine Learning: Insights from a Finance Application
Javier Alcazar, Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz
Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of class…
Entanglement types for two-qubit states with real amplitudes
Oscar Perdomo, Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz
We study the set of two-qubit pure states with real amplitudes and their geometrical representation in the three-dimensional sphere. In this representation, we show that the maxima…
Robust Implementation of Generative Modeling with Parametrized Quantum Circuits
Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz, Oscar Perdomo
Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansatz, a robust and thorough assess…
Training of Quantum Circuits on a Hybrid Quantum Computer
D. Zhu, N. M. Linke, M. Benedetti +10
Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to tak…
A generative modeling approach for benchmarking and training shallow quantum circuits
Marcello Benedetti, Delfina Garcia-Pintos, Oscar Perdomo +3
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propos…
Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
Marcello Benedetti, John Realpe-Gómez, Alejandro Perdomo-Ortiz
Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work…
Readiness of Quantum Optimization Machines for Industrial Applications
Alejandro Perdomo-Ortiz, Alexander Feldman, Asier Ozaeta +9
There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current…
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez +1
With quantum computing technologies nearing the era of commercialization and quantum supremacy, machine learning (ML) appears as one of the promising "killer" applications. Despite…
A NASA Perspective on Quantum Computing: Opportunities and Challenges
Rupak Biswas, Zhang Jiang, Kostya Kechezhi +10
In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms. For most problems, howeve…
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas +1
Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is i…