collaborators

10 papers

quant-ph2026

Scalable quantum circuit knitting using a weak-coupling approximation

John P. T. Stenger, Daniel Gunlycke, Nikos Chrisochoides

We present a method for performing distributed quantum computing with controlled approximations. Exact distributed quantum computing requires exponential classical information to r…

quant-ph2026

Hybrid VQE-CVQE algorithm using diabatic state preparation

John P. T. Stenger, C. Stephen Hellberg, Daniel Gunlycke

We propose a hybrid variational quantum algorithm that has variational parameters used by both the quantum circuit and the subsequent classical optimization. Similar to the Variati…

quant-ph2026

Probability Distribution Analysis of the Cascaded Variational Quantum Eigensolver

Yi-Hua Lai, John P. T. Stenger, Gloria Bazargan +2

The cascaded variational quantum eigensolver (CVQE) circumvents the need for iterative communication between the quantum and classical processing units that is necessary in the con…

quant-ph2026

Ground-state energies of Ising models calculated using the samples from a quantum computer that simulates short-time evolution

John P. T. Stenger, C. Stephen Hellberg, Daniel Gunlycke

We find the ground-state energy of the Ising model using the Cascaded Variational Quantum Eigensolver (CVQE) algorithm with the Guided-Sampling Ansatz (GSA) using up to 63 qubits o…

quant-ph2026

Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search

Dominik Soós, Marc Paterno, John Stenger +1

Optimization problems become fundamentally challenging as the number of variables increases. Because the volume of the search space grows exponentially, classical algorithms freque…

quant-ph2026

Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites

Qiben Yan, John P. T. Stenger, Daniel Gunlycke

Data flow scheduling for high-throughput multibeam satellites is a challenging NP-hard combinatorial optimization problem. As the problem scales, traditional methods, such as Mixed…