Paper 2026/332 Original: eprint.iacr.org/2026/332 ↗

Cost-Layer–Blind Hybrid QAOA for MAX K-CUT via Native MBQC and Selective Graph Masking

Abstract

Delegating the Quantum Approximate Optimization Algorithm (QAOA) to an untrusted quantum cloud can leak sensitive instance structure: for graph objectives, the connectivity of the cost unitary directly reveals which edges are present. We propose a selectively blind protocol that hides only the instance-dependent cost Hamiltonian while keeping the mixer public and unmodified. Our approach combines (i) the native measurement-based implementation of the MAX $K$-CUT cost layer from Proietti \emph{et al.} (MBQC-QAOA) and (ii) selective masking techniques inspired by Selectively Blind Quantum Computation (SBQC). The client pads the private graph into a public candidate supergraph by adding dummy edges/vertices. During the measurement-based cost evolution, the server prepares a fixed public MBQC resource over the candidate edges and streams the corresponding cost ancillas to the client for measurement (measurement-only delegation). By choosing either the intended interaction angle (real edge) or the identity angle (dummy edge) \emph{locally}, the client privately prunes dummy edges while revealing no cost-layer angles to the server; one-time-padded correction bits preserve a leakage-free Pauli-frame interface to a standard gate-model mixer. We prove correctness and selective edge blindness, show that padding does not alter the QAOA optimization landscape (hence does not worsen barren plateaus), and provide proof-of-concept numerical validations for MAX-CUT ($K=2$) (exact state-vector equivalence tests and shot-based circuit emulation with feed-forward), together with an asymptotic resource analysis for general MAX $K$-CUT and an explicit dummy-vertex invariance check under full-register mixers.