Tuesday, September 24th, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• The Measurement-Circuit-Driven neural network improves the reliability of quantum cross-platform verification.

• New graph state-based protocols optimize the routing of Greenberger-Horne-Zeilinger states across quantum networks by using tree structures.

• LatentQGAN is a hybrid quantum-classical model integrating a convolutional autoencoder with quantum GANs.

• Plus quantum reinforcement learning for drone fleets, the latest in research funding, entanglement at the hadron collider, and more.

And even more research, news, & events within quantum.

❎ MC-Net: A Multimodal Deep Learning Framework for Quantum Cross-Platform Verification

QUICK BYTE: Researchers from the University of Sydney, Nanyang Technological University, and others developed the Measurement-Circuit-Driven neural network for quantum cross-platform verification.

DETAILS

• MC-Net was introduced to address the challenges of verifying the similarity between outputs from two quantum devices running identical quantum algorithms. It incorporates a multimodal learning approach, using both measurement outcomes and classical circuit descriptions to create a comprehensive representation of quantum states.

• MC-Net uses a permutation-invariant neural network and a graph neural network to process measurement data and circuit layouts separately, then fuses the information to form a unified, low-dimensional representation that predicts cross-platform fidelity. The model reduces computational demands, especially for systems with up to 50 qubits, and improves prediction accuracy by three orders of magnitude compared to conventional random measurement methods.

• This model may be used to improve the reliability of quantum cross-platform verification, enabling scalable and efficient verification for large quantum systems, with potential applications in entanglement estimation and state purity assessment.

🌐 Optimizing Quantum Networks: Tree-Based Protocols Enhance GHZ State Distribution for Scalable and Secure Communication

QUICK BYTE: In a recent arXiv preprint, scientists from the Indian Statistical Institute propose new graph state-based protocols to optimize the routing of Greenberger-Horne-Zeilinger states across quantum networks by using tree structures, achieving better scalability and efficiency compared to linear configurations.

DETAILS

• Efficient routing of multiparty entanglement across quantum networks are necessary for advanced quantum communication protocols but are difficult to maintain over long distances due to the fragility of quantum states.

• Researchers from the Indian Statistical Institute introduce two graph state-based protocols that use tree structures instead of linear configurations to distribute GHZ states more effectively. This allows for larger GHZ states to be shared, especially in grid networks, by using local operations, which reduce the need for long-distance entanglement routing.

• The paper analyzes how tree structures, particularly balanced trees, outperform unbalanced ones in terms of maximizing the number of users that can participate in the GHZ state sharing. For grid networks, they propose a specific construction method to extract the GHZ state optimally by utilizing a tree-based repeater structure.

• The proposed methods demonstrated the potential for improved the scalability and efficiency of quantum communication networks, which may contribute to more practical implementations of quantum internet infrastructure, which is critical for future secure communication technologies. Their algorithm also surpasses previous protocols in terms of efficiency and the size of the GHZ state that can be distributed over a network.

🤖 LatentQGAN: A Hybrid Quantum-Classical Model Enhancing Scalability and Efficiency in Quantum Machine Learning for Image Generation

QUICK BYTE: Researchers from Université de Sherbrooke, Polytechnique Montréal, and Thales Digital Solutions propose LatentQGAN, a hybrid quantum-classical model integrating a convolutional autoencoder with quantum GANs.

DETAILS: 

• LatentQGAN integrates a quantum-classical generative adversarial network with a classical autoencoder to improve the scalability and efficiency of quantum machine learning models for image generation.

• The LatentQGAN model compresses image data into a latent space using an autoencoder, making the representation more compatible with quantum circuits, thus reducing the computational complexity and overcoming quantum hardware limitations such as decoherence and limited qubit connectivity.

• The was shown to outperform existing quantum and classical GAN models by improving training efficiency and generating higher-quality data using fewer quantum resources, which may be especially applicable in quantum machine learning applications involving data generation tasks.

BTQ Technologies has successfully acquired Radical Semiconductor's portfolio, including its CASH cryptographic accelerator architecture, to strengthen its cryptographic capabilities in post-quantum cryptography and secure communications. Radical's technology, designed for PQC algorithms like Kyber and Dilithium, will enhance BTQ's offerings for industries such as IoT, finance, and government sectors. The acquisition positions BTQ as a leader in quantum-secure solutions, as it integrates Radical’s technology with its own PQC technology, such as the Preon quantum signature algorithm, in line with evolving global standards from NIST.

Researchers from Korea University and Sookmyung Women’s University introduced a hybrid quantum-classical framework, quantum multi-drone reinforcement learning, to optimize the mobility control of drone fleets in dynamic environments. By combining quantum computing with classical methods, the QMDRL framework improves scalability and decision-making efficiency in multi-agent systems, addressing challenges like non-stationarity. Although still limited by current quantum hardware constraints, this approach shows promise for future applications in autonomous systems beyond drones, including smart city infrastructures and autonomous vehicles.

Martin Mosquera, an assistant professor in Montana State University’s Department of Chemistry and Biochemistry, received an Early Career Research Program Award from the U.S. Department of Energy, accompanied by a $875,000 grant to support his quantum computing research. Mosquera will use the grant to expand his computational platform, which models the behavior of quantum systems, specifically neutral atoms, as potential building blocks for advanced analog quantum computing. This system can bypass multi-step processing, increasing computational power for tasks such as simulating complex natural systems.

Infleqtion has secured a $1.15M DOE SBIR Phase IIB grant to advance its quantum software platform, Superstaq, which optimizes quantum computing performance by integrating hardware-specific features. The grant will support further development of quantum software for applications in industries like defense, energy, and healthcare, with a focus on optimizing compilation, error mitigation, and resource state distillation.

Q-CTRL’s Fire Opal has integrated with QCentroid's platform to improve quantum readiness by offering no-code and API tools, real-time monitoring, and AI-driven error suppression for optimizing quantum algorithm performance. This partnership may assist businesses and developers in deploying quantum solutions more efficiently, with Fire Opal’s optimization capabilities improving algorithm success on real quantum hardware by up to 1000x.

LISTEN

On the most recent episode of the Superposition Guy’s pocast, Yuval Boger, CMO of QuEra, interviews Renaud Béchade, founder of Anzaetek, a quantum software company based in Korea. Renaud shares insights about the company’s work in quantum machine learning for hospitals, focusing on managing limited medical data, federated learning, and potential quantum solutions for personalized medicine. They also discuss hardware-software co-design, the quantum ecosystem in Korea, and the future of quantum applications beyond healthcare, including in finance and optimization. Renaud reflects on key learnings from recent conferences, potential breakthroughs in quantum error correction, and much more.

ENJOY

Scientists at the Large Hadron Collider studied the entanglement phenomenon using entangled top quarks, showing that when the spin of one quark is measured, the other immediately adopts the complementary spin. This instantaneous behavior puzzled Albert Einstein, who proposed hidden variables might explain it, but later experiments, including those inspired by physicist John Bell in 1964, have consistently confirmed the predictions of quantum mechanics with no evidence of hidden variables. Read more in this recent article from Symmetry magazine.

WATCH

Overview of Hamiltonian dynamics simulation on quantum computers and its applications in quantum algorithms: