A Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz
Milana Bazayeva, Abigail McClain Gomez, Kenneth M. Merz Jr

TL;DR
This paper presents a quantum multi-programming framework for quantum chemistry that effectively manages resources and mitigates cross-talk, achieving highly accurate ground-state energy estimations on simulated molecular systems.
Contribution
It introduces a novel parallel experiment class within Qiskit for quantum chemistry, demonstrating robustness against cross-talk in multi-programming environments.
Findings
The workflow effectively mitigates cross-talk noise in quantum simulations.
Final energy estimates are within 0.001 kcal/mol of classical references.
The approach is validated on ethanol molecule conformations.
Abstract
In the context of quantum computing, efficient resource management is crucial for optimizing throughput on cloud-based platforms and maximizing hardware utilization. In the present work, we propose an approach to tackle quantum chemistry problems via quantum multi-programming of the Local Unitary Cluster Jastrow (LUCJ) ans\"atze. The ground-state energy of the molecular system is obtained via Sample-based quantum diagonalization (SQD), further refined by its extended version (ext-SQD). Building upon the Qiskit Experiments package, which already supports parallel execution functionality for general tasks, we developed a novel parallel experiment class tailored for quantum chemistry problems. Cross-talk is a known issue in the multi-programming frameworks and can corrupt the ground-energy estimation of the simulated systems. To assess its impact within our approach, we simulated two…
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