Privacy Enhancing Technologies Whitepaper:Developed by Centre of Excellence – Data Sharing and Cloud

This whitepaper provides decision-makers with insights on the benefits of Privacy Enhancing Technologies (PETs) for data collaborations. With recent growth and development of data sharing, public and private organisations can realise new economic and societal value potential. However, data collaboration participants often face barriers for data sharing in form of privacy, commercial and reputational risks. PETs can play a role for reducing these barriers and increasing trust in data collaborations where data cannot be shared directly, since PETs allow to generate insights without disclosing the underlying data. The paper focuses on the most important PETs and their benefits for respective use cases. It also covers challenges that need to be overcome for large-scale adoption of PETs and lastly, shows tangible steps for fostering implementation of these technologies in organisations

Local multi-qubit Clifford equivalence of graph states

In the range of applications opened by quantum technology, often a highly entangled source state is needed as an input for a protocol (target state). The easiest example of such an application is QKD, other examples are quantum secret sharing and measurement based quantum computing. Generating a non-local entangled state is however not trivial. Let us assume that there is an entangled state (source state) present in the network before the start of the protocol. One can ask than whether this source state can be transformed to the target state with only local operations. Local operations usually have a lower error rate compared to non-local operations. As the set of all quantum states increases exponentially, we will restrict ourselves to graph states (quantum states described by a simple graph). This allows one to study the graphs and local operations in a graph theory perspective, instead of in a quantum information perspective. In this thesis we investigate the complexity of deciding equivalence of two graph states under local multi-qubit operations (T-LMQC-equivalent), where T refers to the partition denoting which qubits are on the same device which allows multi-qubit operations.When T consist only of single qubit nodes, this problem reduces to the single qubit Clifford equivalence problem (SQC-EQUIV). It is known that SQC-EQUIV can be solved in O(N^4). On the other side, if all qubits are in the same node, the problem can be solved in O(1) as every source state can be transformed to any graph state on the same vertex set. We will present three algorithms to solve T-LMQC equivalence. The first algorithm solves the problem in general but the running time scales exponentially in the number of multi-qubit nodes and the size of the graph. The other two scale polynomially in the size of the graph, but do not allow for nodes with more than 2 qubits. The remaining question is indeed whether deciding T-LMQC equivalence is an easy (in P) problem or a hard problem (NP-complete).Applied Physics | Quantum Technolog

Privacy Enhancing Technologies Whitepaper:Developed by Centre of Excellence – Data Sharing and Cloud

This whitepaper provides decision-makers with insights on the benefits of Privacy Enhancing Technologies (PETs) for data collaborations. With recent growth and development of data sharing, public and private organisations can realise new economic and societal value potential. However, data collaboration participants often face barriers for data sharing in form of privacy, commercial and reputational risks. PETs can play a role for reducing these barriers and increasing trust in data collaborations where data cannot be shared directly, since PETs allow to generate insights without disclosing the underlying data. The paper focuses on the most important PETs and their benefits for respective use cases. It also covers challenges that need to be overcome for large-scale adoption of PETs and lastly, shows tangible steps for fostering implementation of these technologies in organisations