Report on Current Developments in Quantum Computing and Privacy
General Direction of the Field
The recent advancements in quantum computing and privacy have shown a significant shift towards leveraging quantum technologies to address both computational and privacy challenges. The field is moving towards more practical and scalable solutions, particularly in the context of quantum annealing and hybrid quantum-classical models. There is a growing emphasis on developing privacy-preserving techniques that can be integrated into quantum algorithms, ensuring that sensitive information remains protected even when using third-party quantum services.
One of the key directions is the transformation of classical cryptographic problems into forms that can be solved using quantum annealing. This includes the adaptation of problems like the discrete logarithm problem over binary fields to the Quadratic Unconstrained Binary Optimization (QUBO) format, which is well-suited for quantum annealers. This transformation not only expands the scope of problems that quantum computers can tackle but also opens new avenues for cryptographic research.
Another notable trend is the development of privacy-preserving frameworks for quantum annealing. These frameworks aim to obfuscate the problem data before submission to a quantum annealer, thereby protecting the user's private information from the service provider. Techniques such as digit-wise splitting and matrix permutation are being explored to achieve this obfuscation, with promising results in both theoretical analysis and empirical tests.
In the realm of quantum learning, there is a surge in interest in quantum delegated and federated learning. These approaches utilize quantum homomorphic encryption to ensure that client data remains private during the learning process. The focus is on reducing communication complexity and computational burden on local quantum devices, making these methods more feasible for real-world applications.
Additionally, the integration of quantum computing with cloud services is gaining traction. This includes the development of efficient and privacy-preserving decision tree inference in cloud environments, as well as the implementation of quantum private information retrieval and functional bootstrapping. These advancements aim to enhance both the security and efficiency of cloud-based quantum computations.
Noteworthy Papers
Privacy-Preserving Quantum Annealing for QUBO Problems: Introduces a novel framework using digit-wise splitting and matrix permutation to obfuscate QUBO problems, effectively preserving user privacy.
Quantum delegated and federated learning via quantum homomorphic encryption: Presents a framework that significantly reduces communication complexity and computational burden in quantum learning, ensuring data privacy.
OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud: Offers a practical solution for secure and efficient decision tree inference in cloud environments, enhancing both security and efficiency.
These papers represent significant strides in the integration of quantum computing with privacy-preserving techniques, offering innovative solutions that advance the field.
