61 citations · 264 across the 16 of their papers we have counts for
16 papers
Quantum Optimization with Classical Chaos
Malick A. Gaye, Omar Shehab, Paraj Titum +1
The Quantum Approximate Optimization Algorithm (QAOA) is a powerful tool in solving various combinatorial problems such as Maximum Satisfiability and Maximum Cut. Hard computationa…
Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice
Rui-Hao Li, Hakan Doga, Bryan Raubenolt +16
In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode…
Towards Quantum Tensor Decomposition in Biomedical Applications
Myson Burch, Jiasen Zhang, Gideon Idumah +12
Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present a comprehensive analysis of tensor decomp…
Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations
Yasuo Oda, Kevin Schultz, Leigh Norris +2
The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characte…
A perspective on protein structure prediction using quantum computers
Hakan Doga, Bryan Raubenolt, Fabio Cumbo +5
Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. W…
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…
Energy risk analysis with Dynamic Amplitude Estimation and Piecewise Approximate Quantum Compiling
Kumar J. B. Ghosh, Kavitha Yogaraj, Gabriele Agliardi +6
We generalize the Approximate Quantum Compiling algorithm into a new method for CNOT-depth reduction, which is apt to process wide target quantum circuits. Combining this method wi…
Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing
Gabriele Agliardi, Corey O'Meara, Kavitha Yogaraj +8
Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely ri…
Markov Chain Monte-Carlo Enhanced Variational Quantum Algorithms
Taylor L. Patti, Omar Shehab, Khadijeh Najafi +1
Variational quantum algorithms are poised to have significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condense…
Quantum kernels for real-world predictions based on electronic health records
Zoran Krunic, Frederik F. Flöther, George Seegan +2
In recent years, research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures)…
Noise reduction using past causal cones in variational quantum algorithms
Omar Shehab, Isaac H. Kim, Nhung H. Nguyen +5
We introduce an approach to improve the accuracy and reduce the sample complexity of near term quantum-classical algorithms. We construct a simpler initial parameterized quantum st…
Toward convergence of effective field theory simulations on digital quantum computers
Omar Shehab, Kevin A. Landsman, Yunseong Nam +5
We report results for simulating an effective field theory to compute the binding energy of the deuteron nucleus using a hybrid algorithm on a trapped-ion quantum computer. Two inc…
Quantum Fourier Sampling is Guaranteed to Fail to Compute Automorphism Groups of Easy Graphs
Omar Shehab, Samuel J. Lomonaco
The quantum hidden subgroup approach is an actively studied approach to solve combinatorial problems in quantum complexity theory. With the success of the Shor's algorithm, it was…
Exponential capacity of associative memories under quantum annealing recall
Siddhartha Santra, Omar Shehab, Radhakrishnan Balu
Associative memory models, in theoretical neuro- and computer sciences, can generally store a sublinear number of memories. We show that using quantum annealing for recall tasks en…
Experimental quantum annealing: case study involving the graph isomorphism problem
Kenneth M. Zick, Omar Shehab, Matthew French
Quantum annealing is a proposed combinatorial optimization technique meant to exploit quantum mechanical effects such as tunneling and entanglement. Real-world quantum annealing-ba…
Five Quantum Algorithms Using Quipper
Safat Siddiqui, Mohammed Jahirul Islam, Omar Shehab
Quipper is a recently released quantum programming language. In this report, we explore Quipper's programming framework by implementing the Deutsch's, Deutsch-Jozsa's, Simon's, Gro…