Solving Constrained Optimization Problems Using Hybrid Qubit-Qumode Quantum Devices

doi:10.1021/acs.jctc.6c00230

Solving Constrained Optimization Problems Using Hybrid Qubit-Qumode Quantum Devices

2026-05-14

0

PRE

AI

DOI：10.1021/acs.jctc.6c00230

Original

Original request for help

Save

Abstract

En 中文

Variational Quantum Algorithms (VQAs) provide a promising framework for tackling complex optimization problems on near-term quantum hardware. Here, we demonstrate that hybrid qubit-qumode quantum devices offer an efficient route to solving Quadratic Unconstrained Binary Optimization (QUBO) problems using the Echoed Conditional Displacement Variational Quantum Eigensolver (ECD-VQE). Leveraging circuit quantum electrodynamics (cQED) architectures, we encode QUBO instances across multiple qumodes weakly coupled to a single qubit and extract binary solutions directly from photon-number measurements. We apply ECD-VQE to the Binary Knapsack Problem and show that it outperforms the Quantum Approximate Optimization Algorithm (QAOA) implemented on conventional qubit circuits, achieving higher-quality solutions with dramatically fewer resources. We also demonstrate that ECD-VQE can be extended to chemically motivated tasks such as active-space selection for multireference electronic structure methods. These results highlight the utility of hybrid qubit-qumode platforms for a broad class of NP-hard and chemistry-related optimization problems and demonstrate that variational ECD ansatz can realize expressive state preparation with significantly shallower circuits than qubit-only architectures, positioning qubit-qumode gates as compelling candidates for constrained optimization in early fault-tolerant quantum computing.

Keywords:

Algorithms

Circuits

Hamiltonians

Mathematical methods

Optimization

Solving Constrained Optimization Problems Using Hybrid Qubit-Qumode Quantum Devices

Abstract

Journal

Organization