New method tackles continuous-variable problems on qubit-based machines

Researchers at the Veneto Institute of Oncology IOV-IRCCS in Padova, Italy, are addressing a core challenge in quantum optimization: efficiently tackling continuous-variable problems without being limited by either predefined search ranges or the need for complex Hamiltonians. The team’s new framework combines adaptive discretization with an iterative mapping onto an effective spin-glass Hamiltonian, offering a flexible approach for qubit-based quantum architectures. The method was assessed using the n-dimensional Gramacy-Lee benchmark and applied to an inverse-planning problem for intensity-modulated radiotherapy, demonstrating the potential for competitive solutions in both quantum and quantum-inspired pipelines. Constructing a usable Hamiltonian for continuous-variable quantum optimization often demands either dense polynomial models or complex interpolation schemes, limiting practical application. The framework detailed by researchers avoids these requirements by employing an adaptive discretization technique, where the resolution of each continuous variable’s encoding adjusts dynamically during the optimization process. This approach differs from standard binary discretization, which confines encoded values to a fixed domain and resolution, and is a departure from methods that relax constraints at the…