Minimizing classical resources in variational measurement-based quantum computation for generative modeling

arXiv:2604.11578v1 Announce Type: cross Abstract: Measurement-based quantum computation (MBQC) is a framework for quantum information processing in which a computational task is carried out through one-qubit measurements on a highly entangled resource state. Due to the indeterminacy of the outcomes of a quantum measurement, the random outcomes of these operations, if not corrected, yield a variational quantum channel family. Traditionally, this randomness is corrected through classical processing in order to ensure deterministic unitary computations. Recently, variational measurement-based quantum computation (VMBQC) has been introduced to exploit this measurement-induced randomness to gain an advantage in generative modeling. A limitation of this approach is that the corresponding channel model has twice as many parameters compared to the unitary model, scaling as N imes D, where N is the number of logical qubits (width) and D is the depth of the VMBQC model. This can often make optimization more difficult and may lead to poorly trainable models. In this paper, we present a restricted VMBQC model that extends the unitary setting to a channel-based one using only a single additional trainable parameter. We show, both numerically and algebraically, that this minimal extension is sufficient to generate probability distributions that cannot be learned by the corresponding unitary model.

Related

• QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation
• QARIMA: A Quantum Approach To Classical Time Series Analysis
• MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation
• QuanForge: A Mutation Testing Framework for Quantum Neural Networks
• SAQ: Stabilizer-Aware Quantum Error Correction Decoder

Source: arXiv cs.AI | 2026-04-14