Path to a utility scale quantum computer

Sravan Kumar Nidiganti

Drug Utilization Review (DUR) is needed within health care to assess prescribing, dispensing, and medication practices, and to determine if those practices are safe, effective, and affordable.The scale of DUR is currently growing, and will continue to grow with the availability of electronic health records (EHR), claims and real world evidence (RWE).As this scale increases, so do the challenges surrounding contracting with high dimensional data and identifying intricate relationships (e.g.polypharmacy, drug-disease interactions).These challenges are in part due to the current paradigm of classical computers.Quantum Computing (QC) creates the opportunity to advanced these challenges due to its ability to work through massive amounts of information and solve problems on the fly.Under this premise, this work explores the potential role of QC, specifically hybrid quantum-classical models, in the advancement of large scale DUR.This includes the current state of the QC drug discovery and computational chemistry DUR, the application QC could provide to several key DUR challenges, and the creation of a conceptual model which incorporates QC into the current state of DUR.The work also discusses the current state technical, regulatory, and practical impediments to the QC use case within health care, the foundational research needed to closed these gaps, and the use case benefits the work seeks to enable.Ultimately, this work creates a path to the integration of QC and the optimization of drug utilization practices to improve health systems cost effectiveness and patient outcomes.

Cheng-Ju Lin, Hrant Gharibyan, Vincent Paul Su

We use Pauli Path simulation to variationally obtain parametrized circuits for preparing ground states of various quantum many-body Hamiltonians. These include the quantum Ising model in one dimension, in two dimensions on square and heavy-hex lattices, and the Kitaev honeycomb model, all at system sizes of one hundred qubits or more – sizes at which generic quantum circuits are beyond the reach of exact state-vector simulation – thereby reaching utility scale. We benchmark the Pauli Path simulation results against exact ground-state energies when available, and against density-matrix renormalization group calculations otherwise, finding strong agreement. To further assess the quality of the variational states, we evaluate the magnetization in the x and z directions for the quantum Ising models and compute the topological entanglement entropy for the Kitaev honeycomb model. Finally, we prepare approximate ground states of the Kitaev honeycomb model with 48 qubits, in both the gapped and gapless regimes, on Quantinuum's System Model H2 quantum computer using parametrized circuits obtained from Pauli Path simulation. We achieve a relative energy error of approximately 5 % without error mitigation and demonstrate the braiding of Abelian anyons on the quantum device beyond fixed-point models.

Benedict N Murdin

Jack D. Hidary

Maho Hirahara, Aohan Li

With the rapid advancement of smart agriculture, Unmanned Aerial Vehicles (UAVs) have become indispensable for efficient coverage and path planning in grid-mapped agricultural fields. However, optimizing UAV flight paths in large-scale environments remains a complex Nondeterministic Polynomial-time (NP)-hard problem. Traditional path planning methods are often constrained by local optima, limited scalability, and slow convergence, which significantly restrict their effectiveness in solving large-scale problems. To address these limitations, this paper proposes a novel optimization framework utilizing a Graphics Processing Unit (GPU)-parallelized Ising solver. Our method mimics the operational principles of quantum annealing on GPU hardware, enabling rapid search for the minimum-energy state of Ising models. Unlike physical quantum devices, which are often constrained by the number of qubits, our approach leverages the Fixstars Amplify (FA) platform to perform parallel annealing on highly parallelized GPUs. This enables the simultaneous evaluation of thousands of potential path candidates and vast state transitions. By leveraging large-scale parallel processing, this framework's core strength lies in minimizing computation time even as the problem scale increases. Simulation results demonstrate that our proposed method consistently identifies superior flight paths while maintaining stable computational performance compared with the genetic algorithm and simulated annealing method.