In this work, we propose a novel architecture (and several variants thereof) based on quantum cryptographic primitives with provable privacy and security guarantees regarding membership inference attacks on generative models. Our architecture can be used on top of any existing classical or quantum generative models. We argue that the use of quantum gates associated with unitary operators provides inherent advantages compared to standard Differential Privacy based techniques for establishing guaranteed security from all polynomial-time adversaries.

This paper demonstrates and proves that the coordination of actions in a distributed swarm can be enhanced by using quantum entanglement. In particular, we focus on - Global and local simultaneous random walks, using entangled qubits that collapse into the same (or opposite) direction, either random direction or totally controlled simultaneous movements. - Identifying eavesdropping from malicious eavesdroppers aimed at disturbing the simultaneous random walks by using entangled qubits that were sent at random or with predefined bases. - Identifying Byzantine robots or malicious robots that are trying to gain secret information or are attacking the system using entangled qubits. - The use of Pseudo Telepathy to coordinate robots' actions.

Mphasis accelerates the world-leading Quantum Computing Ecosystem in partnership with the University of Calgary and the Government of Alberta. The Quantum Lab is set to accelerate the development of quantum skills in the city to enable job creation. CALGARY, AB, June 9, 2022 – Mphasis, (BSE: 526299; NSE: MPHASIS), an Information Technology (IT) solutions provider specializing in cloud and cognitive services, today joined the Government of Alberta and the University of Calgary to announce the launch of the world-leading Quantum City – Canada. Quantum city will further establish Alberta as a leading technology hub and will accelerate the development of the quantum ecosystem in Calgary. The partnership aims to utilize the synergy between academia, industry, and government to put the process of ideation to market at the forefront.

Qu&Co and Pasqal are merging their businesses, combining Qu&Co's robust portfolio of algorithms with Pasqal's full-stack neutral-atom system to accelerate the quantum path to commercial applications. The united business, known as Pasqal and located in Paris, will offer a 1,000-qubit quantum solution in 2023, according to the disclosed roadmaps of the most sophisticated quantum platforms. Qu&Co's portfolio of quantum algorithms will be tightly integrated with Pasqal's advanced quantum hardware, providing added value to customers such as Johnson & Johnson, LG, Airbus, and BMW Group. The combined company will offer a wide range of quantum solutions in chemistry, life sciences, automotive, electronics, utilities, aerospace, defense, finance, and other sectors. In an interview with EE Times Europe, Georges-Olivier Reymond, CEO of Pasqal, said the merger enables the combined company to fast-track the implementation of its R&D roadmap, recruit top talent, achieve an industry-relevant quantum advantage much sooner, and serve more clients with new, unique, and proprietary quantum solutions.

Fully convolutional networks are robust in performing semantic segmentation, with many applications from signal processing to computer vision. From the fundamental principles of variational quantum algorithms, we propose a feasible pure quantum architecture that can be operated on noisy intermediate-scale quantum devices. In this work, a parameterized quantum circuit consisting of three layers, convolutional, pooling, and upsampling, is characterized by generative one-qubit and two-qubit gates and driven by a classical optimizer. This architecture supplies a solution for realizing the dynamical programming on a one-way quantum computer and maximally taking advantage of quantum computing throughout the calculation. Moreover, our algorithm works on many physical platforms, and particularly the upsampling layer can use either conventional qubits or multiple-level systems. Through numerical simulations, our study represents the successful training of a pure quantum fully convolutional network and discusses advantages by comparing it with the hybrid solution.