# Blind quantum machine learning with quantum bipartite correlator

arXiv: https://arxiv.org/abs/2310.12893 · HTML: https://mlsanity.com/paper/2310.12893/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [Quantum federated learning through blind quantum computing](https://mlsanity.com/paper/2103.08403/) (2021)
- [Chiron: Privacy-preserving Machine Learning as a Service](https://mlsanity.com/paper/1803.05961/) (2018)
- [Sublinear quantum algorithms for training linear and kernel-based classifiers](https://mlsanity.com/paper/1904.02276/) (2019)
- [Towards a Distributed Quantum Computing Ecosystem](https://mlsanity.com/paper/2002.11808/) (2020)
- [Private quantum computation: An introduction to blind quantum computing and related protocols](https://mlsanity.com/paper/1611.10107/) (2016)
- [Distributed Quantum Computing: a Survey](https://mlsanity.com/paper/2212.10609/) (2022)
- [Expressive variational quantum circuits provide inherent privacy in federated learning](https://mlsanity.com/paper/2309.13002/) (2023)
- [Quantum Internet: Networking Challenges in Distributed Quantum Computing](https://mlsanity.com/paper/1810.08421/) (2018)

## Built on by (most cited first, 8 of 8)

- [Privacy-preserving quantum federated learning via gradient hiding](https://mlsanity.com/paper/2312.04447/) (2023)
- [Quantum delegated and federated learning via quantum homomorphic encryption](https://mlsanity.com/paper/2409.19359/) (2024)
- [Experimentally validated quantum-secure federated learning over a multi-user quantum network](https://mlsanity.com/paper/2501.12709/) (2025)
- [Prospects of Privacy Advantage in Quantum Machine Learning](https://mlsanity.com/paper/2405.08801/) (2024)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [Geometric quantum machine learning of BQPA protocols and latent graph classifiers](https://mlsanity.com/paper/2402.03871/) (2024)
- [Emerging Paradigms for Securing Federated Learning Systems](https://mlsanity.com/paper/2509.21147/) (2025)
- [Quantum ring all-reduce: communication and privacy advantages for distributed learning](https://mlsanity.com/paper/2606.20344/) (2026)

## Abstract (arXiv)

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this work, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.