Quantum machine learning (QML) has attracted growing interest for their ability to achieve superior performance with significantly fewer parameters. However, the high cost and scarcity of current hardware push inference to cloud-hosted quantum devices, creating a tension between verifiability and confidentiality. This work proposes a novel framework that converts quantum neural network operations into classical arithmetic circuits that faithfully approximate genuine quantum computations. By encrypting these circuits with zero-knowledge proofs, it ensures computational validity while concealing internal parameters. Experimental results show that our classical circuits achieve fidelity above 0.9996 and total variation distance below 1% compared to actual quantum computations, verifying the practicality of trustworthy and privacy-preserving quantum inference.

The rapid adoption of Verifiable Credentials (VCs) has intensified privacy and security challenges in digital verification, as traditional systems often require full credential disclosure, creating privacy risks and expanding the attack surface. Ensuring end-to-end privacy, security, and verifiability in such systems remains a significant challenge. This paper introducesZK-Sandbox, a Zero-Knowledge Data Sandbox System that integrates zero-knowledge proofs (ZKPs), decentralized identifiers (DIDs), and blockchain anchoring to enable credential validation and verifiable badge issuance without exposing underlying data. ZK-Sandbox supports complex predicate evaluation by securely aggregating multiple VCs from trusted issuers, processing them via JSON Web Token signature verification, Circom-based zk-SNARK circuits, and Docker-isolated execution. The system issues VC-compatible ZK-Badges, cryptographically bound to a Poseidon hash and anchored to a blockchain-registered DID, containing only abstracted verification results. Experimental evaluation confirms 100% validation accuracy, complete detection of tampered submissions, and efficient performance–averaging 326 ms issuance latency, 570 ms off-chain verification, and 4.45 s on-chain verification. These results demonstrate that ZK-Sandbox is a privacy-by-design, scalable, and regulation-aligned solution for self-sovereign digital credential ecosystems.