160 citations · 279 across the 11 of their papers we have counts for
13 papers
From Quantum Tunneling to Classical Optimization through Instantons
Jiayu Shen, Dylan Herman, Guneykan Ozgul +5
We use an instanton-based analysis to show that Path Integral Stochastic Gradient Descent (PISGD), a classical optimization algorithm instantiated from path integral Monte Carlo (P…
Quantum algorithms for orthogonal polynomial transforms
Anupam Prakash, Shree Hari Sureshbabu, Dylan Herman +1
A quantum orthogonal polynomial transform (QOPT) is an algorithmic primitive that maps a superposition of standard basis states coherently to a basis of norm…
Global Multi-Maturity SPX-VIX Calibration Beyond Markovian Stitching
Atithi Acharya, Yue Sun, Brandon Augustino +3
We develop a global martingale optimal transport framework for joint S&P 500 (SPX)-VIX smile calibration across multiple maturities, without the conditional independence that Marko…
Faster Algorithms for Multimarginal Optimal Transport
Brandon Augustino, Yue Sun, Atithi Acharya +4
We study constructive discrete multimarginal optimal transport (MOT) among distributions on points, where the cost tensor has entries. For additive accuracy $\v…
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…
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…
Prospects of Privacy Advantage in Quantum Machine Learning
Jamie Heredge, Niraj Kumar, Dylan Herman +5
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collab…
Quantum option pricing via the Karhunen-Loève expansion
Anupam Prakash, Yue Sun, Shouvanik Chakrabarti +8
We consider the problem of pricing discretely monitored Asian options over monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide tw…
Expressive variational quantum circuits provide inherent privacy in federated learning
Niraj Kumar, Jamie Heredge, Changhao Li +3
Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standar…
The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze
Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti +5
Using tools from the representation theory of compact Lie groups, we formulate a theory of Barren Plateaus (BPs) for parameterized quantum circuits whose observables lie in their d…
Parameter Setting in Quantum Approximate Optimization of Weighted Problems
Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin +4
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA…
Finite-Size Scaling on a Digital Quantum Simulator using Quantum Restricted Boltzmann Machine
Bilal Khalid, Shree Hari Sureshbabu, Arnab Banerjee +1
The critical point and the critical exponents for a phase transition can be determined using the Finite-Size Scaling (FSS) analysis. This method assumes that the phase transition o…
Quantum Machine Learning for Chemistry and Physics
Manas Sajjan, Junxu Li, Raja Selvarajan +5
Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automat…