286 citations · 1.9k across the 63 of their papers we have counts for
66 papers
Large-scale portfolio optimization on a trapped-ion quantum computer
Alejandro Gomez Cadavid, Ananth Kaushik, Pranav Chandarana +10
We present an end-to-end pipeline for large-scale portfolio selection with cardinality constraints and experimentally demonstrate it on trapped-ion quantum processors using hardwar…
Quantum Speedups for Derivative Pricing Beyond Black-Scholes
Dylan Herman, Yue Sun, Jin-Peng Liu +5
This paper explores advancements in quantum algorithms for derivative pricing of exotics, a computational pipeline of fundamental importance in quantitative finance. For such cases…
A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values
Tyler Chen, Akshay Seshadri, Mattia J. Villani +7
Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is diffic…
Spin-Boson Mapping of the Quantum Approximate Optimization Algorithm
Sami Boulebnane, Abid Khan, Minzhao Liu +4
The Quantum Approximate Optimization Algorithm (QAOA) achieves monotonically improving performance with circuit depth , yet the study of the high-depth regime has been obstructe…
Threshold for Fault-tolerant Quantum Advantage with the Quantum Approximate Optimization Algorithm
Sivaprasad Omanakuttan, Zichang He, Zhiwei Zhang +9
Optimization is often cited as a promising application of quantum computers. However, the low degree of provable quantum speedups has led prior rigorous end-to-end resource analyse…
Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm
Yuwei Jin, Zichang He, Tianyi Hao +5
The rapid progress in quantum hardware is expected to make them viable tools for the study of quantum algorithms in the near term. The timeline to useful algorithmic experimentatio…
Provably faster randomized and quantum algorithms for -means clustering via uniform sampling
Tyler Chen, Archan Ray, Akshay Seshadri +6
The -means algorithm (Lloyd's algorithm) is a widely used method for clustering unlabeled data. A key bottleneck of the -means algorithm is that each iteration requires time…
Iterative Interpolation Schedules for Quantum Approximate Optimization Algorithm
Anuj Apte, Shree Hari Sureshbabu, Ruslan Shaydulin +5
Quantum Approximate Optimization Algorithm (QAOA) is a promising quantum heuristic with empirical evidence of speedup over classical state-of-the-art for some problems. QAOA uses a…
On Speedups for Convex Optimization via Quantum Dynamics
Shouvanik Chakrabarti, Dylan Herman, Jacob Watkins +4
We explore the potential for quantum speedups in convex optimization using discrete simulations of the Quantum Hamiltonian Descent (QHD) framework, as proposed by Leng et al., and…
Fast Convex Optimization with Quantum Gradient Methods
Brandon Augustino, Dylan Herman, Enrico Fontana +4
We study quantum algorithms based on quantum (sub)gradient estimation using noisy function evaluation oracles, and demonstrate the first dimension-independent query complexities (u…
qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem
Martin J. A. Schuetz, Romina Yalovetzky, Ruben S. Andrist +8
We propose and implement a quantum-informed reduction algorithm for the maximum independent set problem that integrates classical kernelization techniques with information extracte…
Quantum Approximate Optimization Algorithm in Finite Size and Large Depth and Equivalence to Quantum Annealing
Sami Boulebnane, James Sud, Ruslan Shaydulin +1
The quantum approximate optimization algorithm (QAOA) and quantum annealing are two of the most popular quantum optimization heuristics. While QAOA is known to be able to approxima…
Tensor networks for quantum computing
Aleksandr Berezutskii, Minzhao Liu, Atithi Acharya +25
In the rapidly evolving field of quantum computing, tensor networks serve as an important tool due to their multifaceted utility. In this paper, we review the diverse applications…
Non-Variational Quantum Random Access Optimization with Alternating Operator Ansatz
Zichang He, Rudy Raymond, Ruslan Shaydulin +1
Solving hard optimization problems is one of the most promising application domains for quantum computers due to the ubiquity of such problems in industry and the availability of b…
Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups
Martin J. A. Schuetz, Ruben S. Andrist, Grant Salton +7
We propose and implement a comprehensive quantum compilation toolkit for solving the maximum independent set (MIS) problem on quantum hardware based on Rydberg atom arrays. Our end…
Generalized Short Path Algorithms: Towards Super-Quadratic Speedup over Markov Chain Search for Combinatorial Optimization
Shouvanik Chakrabarti, Dylan Herman, Guneykan Ozgul +6
We analyze generalizations of quantum algorithms based on the short path framework first proposed by Hastings~[\textit{Quantum} 2, 78 (2018)], which has been extended and shown by…
Adaptive and Robust Watermark for Generative Tabular Data
Dung Daniel Ngo, Archan Ray, Akshay Seshadri +6
In recent years, watermarking generative tabular data has become a prominent framework to protect against the misuse of synthetic data. However, while most prior work in watermarki…
Performance of Quantum Approximate Optimization with Quantum Error Detection
Zichang He, David Amaro, Ruslan Shaydulin +1
Quantum algorithms must be scaled up to tackle real-world applications. Doing so requires overcoming the noise present on today's hardware. The quantum approximate optimization alg…
Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing
Atithi Acharya, Romina Yalovetzky, Pierre Minssen +10
Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work…
Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
Zichang He, Ruslan Shaydulin, Dylan Herman +4
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum heuristics for combinatorial optimization. While QAOA has been shown to perform well on small…
End-to-End Protocol for High-Quality QAOA Parameters with Few Shots
Tianyi Hao, Zichang He, Ruslan Shaydulin +2
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classic…
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest
Romina Yalovetzky, Niraj Kumar, Changhao Li +1
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data…
The computational power of random quantum circuits in arbitrary geometries
Matthew DeCross, Reza Haghshenas, Minzhao Liu +49
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimens…
Model-Agnostic Utility-Preserving Biometric Information Anonymization
Chun-Fu Chen, Bill Moriarty, Shaohan Hu +4
The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerpr…