collaborators

13 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

Quantum Data Loading for Carleman Linearized Systems: Application to the Lattice-Boltzmann Equation

Reuben Demirdjian, Thomas Hogancamp, Abeynaya Gnanasekaran +2

Nonlinear ordinary and partial differential equations are ubiquitous in science and engineering, yet finding their solutions is often computationally intractable for classical hard…

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

Auto-regressive Neural Quantum State Sampling for Selected Configuration Interaction

Shane Thompson, Daniel Gunlycke

Accurate ground-state energy calculations remain a central challenge in quantum chemistry due to the exponential scaling of the many-body Hilbert space. Variational Monte Carlo and…