119 citations · 429 across the 17 of their papers we have counts for
19 papers
The a-number of the Serre-type maximal curves of Abdón-Bezerra-Quoos
Mahyar Shafighi
For every prime power , we determine the -number of the Serre-type maximal curves : over , odd, introduced…
Quantum-Informed Portfolio Selection: An End-to-End Pipeline Validated on Trapped-Ion Hardware with Real Market Data
Romina Yalovetzky, Martin J. A. Schuetz, Zichang He +11
Portfolio diversification - a cornerstone of modern investment management - can be formulated as a Maximum Independent Set (MIS) problem on asset correlation graphs. Solving this p…
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2
Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing…
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…
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 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…
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…
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…
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…
Des-q: a quantum algorithm to provably speedup retraining of decision trees
Niraj Kumar, Romina Yalovetzky, Changhao Li +2
Decision trees are widely adopted machine learning models due to their simplicity and explainability. However, as training data size grows, standard methods become increasingly slo…
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…
Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti +26
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tac…
Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups
Ruben S. Andrist, Martin J. A. Schuetz, Pierre Minssen +9
Rydberg atom arrays are among the leading contenders for the demonstration of quantum speedups. Motivated by recent experiments with up to 289 qubits [Ebadi et al., Science 376, 12…
Quantum Deep Hedging
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis +12
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where dee…
Universal Quantum Speedup for Branch-and-Bound, Branch-and-Cut, and Tree-Search Algorithms
Shouvanik Chakrabarti, Pierre Minssen, Romina Yalovetzky +1
Mixed Integer Programs (MIPs) model many optimization problems of interest in Computer Science, Operations Research, and Financial Engineering. Solving MIPs is NP-Hard in general,…
Constrained Optimization via Quantum Zeno Dynamics
Dylan Herman, Ruslan Shaydulin, Yue Sun +6
Constrained optimization problems are ubiquitous in science and industry. Quantum algorithms have shown promise in solving optimization problems, yet none of the current algorithms…
Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
Pradeep Niroula, Ruslan Shaydulin, Romina Yalovetzky +4
Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consi…
Solving Linear Systems on Quantum Hardware with Hybrid HHL++
Romina Yalovetzky, Pierre Minssen, Dylan Herman +1
The limited capabilities of current quantum hardware significantly constrain the scale of experimental demonstrations of most quantum algorithmic primitives. This makes it challeng…
Quantum Machine Learning for Finance
Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar +10
Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, part…