arXiv subjects

Vikram Khipple Mulligan

Publications and source records attributed to Vikram Khipple Mulligan.

4 recordsLinked to original sources

arXiv2026

Qubit-efficient and gate-efficient encodings of graph partitioning problems for quantum optimization

We introduce a qubit- and gate-efficient higher-order unconstrained binary optimization (HUBO) encoding for graph partitioning problems requiring label-count minimization. This widely applicable class of problems includes minimum graph coloring, minimum \textit{k}-cut, and community detection. To the best of our knowledge, this is the first work to address the optimization versions of these problems in a quantum setting, rather than only their decision counterparts. Our construction encodes each vertex variable in a number of bits logarithmic in the number of available labels---the information-theoretic minimum---and employs a novel lexicographic penalty system that implicitly minimizes partition count without requiring dedicated indicator variables. We derive provably sufficient conditions on all penalty coefficients, including those arising from Rosenberg quadratization, guaranteeing feasibility and optimality of the lowest-energy solution. Analogous conditions are derived for a one-hot encoding to enable controlled comparison. We also show that our encoding reduces two-qubit gate count per layer of the quantum approximate optimization algorithm (QAOA). Benchmarking on a quantum annealer demonstrates that our logarithmic encoding greatly improves solution quality and time-to-solution for minimum graph coloring relative to one-hot encoding, with greater advantage as problem size increases.

quant-ph↗

arXiv2026

Playing Dice with the Universe: Programming Quantum Computers to Play Traditional Games

The challenge of programming classical computers to play traditional, competitive games against human players has helped to advance classical hardware and software. Quantum computers have the potential to play games in a unique way: programmed \textit{only} with the rules of a game, they should be able to implicitly represent all future paths of a game leading to wins, losses, or draws, and to sample from this path set to identify moves that maximize the likelihood of a win. This permits skilled play without hard-coded or machine-learned strategy. As a proof of principle, we present early results obtained after programming the D-Wave quantum annealer with the rules of tic-tac-toe, enabling it to play against a human or classical computer opponent. We anticipate that, as it has for classical computers, game-playing will serve as an important real-world benchmark for quantum computers.

cs.ET↗

arXiv2026

Entangled happily ever after: Wedding reception seating mapped to classical and quantum optimizers

Although optimization is one of the most promising applications of quantum computers, the development of effective optimization strategies requires real-world test cases. When planning our recent wedding reception, we realized that the problem of optimally seating our guests, given constraints related to guests' relatedness, shared interests, and physical needs, could be mapped to a cost function network (CFN) form solvable with classical or quantum optimization algorithms. We compared the seating optimization performance of classical Monte Carlo CFN solvers in the Masala software suite to that of quantum annealing-based CFN optimization algorithms using one-hot, domain-wall, and approximate binary mappings, which we had developed for protein design problems. Surprisingly, the D-Wave Advantage 2 system, which performs well on similarly-structured CFN problems for protein design, struggled to return optimal seating arrangements that were easily found by classical Monte Carlo methods. We provide our seating optimization benchmark set, and code to convert seating optimization problems to CFN problems, as a plugin library for Masala, permitting this class of real-world problems to be used to benchmark performance of current and future classical CFN solvers, quantum optimization algorithms, and quantum computing hardware.

cs.ET↗

arXiv2026

Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents

Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design provides a natural testbed: although machine learning (ML) methods achieve strong results, these are largely restricted to canonical amino acids and narrow objectives, leaving unfilled need for a generalist tool for broad design pipelines. We introduce Agent Rosetta, an LLM agent paired with a structured environment for operating Rosetta, the leading physics-based heteropolymer design software, capable of modeling non-canonical building blocks and geometries. Agent Rosetta iteratively refines designs to achieve user-defined objectives, combining LLM reasoning with Rosetta's generality. We evaluate Agent Rosetta on design with canonical amino acids, matching specialized models and expert baselines, and with non-canonical residues -- where ML approaches fail -- achieving comparable performance. Critically, prompt engineering alone often fails to generate Rosetta actions, demonstrating that environment design is essential for integrating LLM agents with specialized software. Our results show that properly designed environments enable LLM agents to make scientific software accessible while matching specialized tools and human experts.

cs.AI↗