Ilya Safro

Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction

Bao Gia Bach, Ilya Safro, Ed Younis

With the growth of quantum platforms for gate-based quantum computation, compilation holds a crucial role in deciding the success of the implementation. While there has been rich research in compilation techniques for the superconducting-qubit regime. The trapped-ion architectures, currently leading in robust …

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Exponentially many initializations to avoid barren plateaus

Ankit Kulshrestha, Ricard Puig, Diego García-Martín, Łukasz Cincio et autres

Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure for barren plateaus is simply to initialize the parameters more carefully. Here we show …

Exponentially many initializations to avoid barren plateaus

Ankit Kulshrestha, Ricard Puig, Diego García-Martín, Łukasz Cincio et autres

Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure for barren plateaus is simply to initialize the parameters more carefully. Here we show …

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UniHetCO: A Unified Heterogeneous Representation for Multi-Problem Learning in Unsupervised Neural Combinatorial Optimization

Kien X. Nguyen, Ilya Safro

Unsupervised neural combinatorial optimization (NCO) offers an appealing alternative to supervised approaches by training learning-based solvers without ground-truth solutions, directly minimizing instance objectives and constraint violations. Yet for graph node subset-selection problems (e.g., Maximum Clique and Maximum Independent Set), existing unsupervised methods …

UniHetCO: A Unified Heterogeneous Representation for Multi-Problem Learning in Unsupervised Neural Combinatorial Optimization

Kien X. Nguyen, Ilya Safro

Unsupervised neural combinatorial optimization (NCO) offers an appealing alternative to supervised approaches by training learning-based solvers without ground-truth solutions, directly minimizing instance objectives and constraint violations. Yet for graph node subset-selection problems (e.g., Maximum Clique and Maximum Independent Set), existing unsupervised methods …

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Biomedical Hypothesis Explainability with Graph-Based Context Retrieval

Ilya Tyagin, Saeideh Valipour, Aliaksandra Sikirzhytskaya, Michael S. Shtutman et autres

Abstract We introduce an explainability method for biomedical hypothesis generation systems, built on top of the novel Hypothesis Generation Context Retriever framework. Our approach combines semantic graph-based retrieval and relevant data-restrictive training to simulate real-world discovery constraints. Integrated with large language models …

Neural Architecture Search Algorithms for Quantum Autoencoders

Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima‐Mwesigwa, Ilya Safro

The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the task. However this …

QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks

Hanjing Xu, Xiaoyuan Liu, Alex Pothen, Ilya Safro

The quantum approximate optimization algorithm (QAOA) is one of the promising variational approaches of quantum computing to solve combinatorial optimization problems. In QAOA, variational parameters need to be optimized by solving a series of nonlinear, nonconvex optimization programs. In this work, we …

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Biomedical Hypothesis Explainability with Graph-Based Context Retrieval

Ilya Tyagin, Saeideh Valipour, Aliaksandra Sikirzhytskaya, Michael S. Shtutman et autres

We introduce an explainability method for biomedical hypothesis generation systems, built on top of the novel Hypothesis Generation Context Retriever framework. Our approach combines semantic graph-based retrieval and relevant data-restrictive training to simulate real-world discovery constraints. Integrated with large language models (LLMs) …

QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits

Ilya Tyagin, Marwa Farag, Kyle Sherbert, Karunya Shirali et autres

Quantum computing has the potential to improve our ability to solve certain optimization problems that are computationally difficult for classical computers by offering new algorithmic approaches that may provide speedups under specific conditions. In this work, we introduce QAOA-GPT, a generative framework …

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Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: a Machine Learning Approach

Kien X. Nguyen, Bao Bach, Ilya Safro

Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems. The process of finding a good set of variational parameters in the QAOA circuit has proven to be challenging due …

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Optimal Fermion-Qubit Mappings via Quadratic Assignment

Mitchell Chiew, Cameron Ibrahim, Ilya Safro, Sergii Strelchuk

Simulation of fermionic systems is one of the most promising applications of quantum computers. It spans problems in quantum chemistry, high-energy physics and condensed matter. Underpinning the core steps of any quantum simulation algorithm, fermion-qubit mappings translate the fermionic interactions to the …

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