The Daily Qubit

Semi-classical light creates a secure protocol for delegated quantum computing, quantum cognition machine learning, and new quantum energy teleportation protocol.

Cierra Choucair
September 20, 2024 • Estimated Reading Time: 9 minutes

Friday, September 20th, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️

Today’s issue includes:

Scientists from Quandela introduce a secure protocol for delegated quantum computing using semi-classical light, which allows clients with limited quantum resources to perform secure quantum computations.
Flapmax and Intel launched the Quantum AI Challenge to encourage HBCU students, faculty, and researchers to solve real-world problems using quantum computing and AI technologies.
A new quantum energy teleportation protocol extracts and stores quantum energy from a quasi-vacuum state, overcoming the previous limitation of energy loss.
Plus Krylov complexity analysis informs quantum reservoir computing, D-Wave's partnership to accelerate quantum annealing in the Middle East, quantum cognitive machine learning, and more.

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

💡 Quandela Scientists Introduce Practical and Secure Protocol for Delegated Quantum Computing Using Semi-Classical Light

UBQC, BDQC, and SDQC protocol. 📸: “Towards practical secure delegated quantum computing with semi-classical light“

QUICK BYTE: Scientists from Quandela introduce a secure protocol for delegated quantum computing using semi-classical light, which allows clients with limited quantum resources to perform secure quantum computations.

DETAILS:

A new protocol for secure delegated quantum computing, reduces the technical demands on both clients and quantum servers while maintaining strong security.
This protocol allows clients to use simple attenuated laser pulses instead of complex quantum devices, and servers handle quantum emitters that generate spin-photon entanglement, making the setup more practical for real-world applications.
The researchers proved that the protocol ensures information-theoretic and composable security, using techniques such as measurement-based quantum computing and GHZ privacy amplification to ensure the security of quantum computations.
The significance of this lies in its practical readiness for experimental implementation, providing a secure method for clients to outsource quantum computations without revealing sensitive data.

🤖 Flapmax and Intel Launch Quantum AI Challenge to Support HBCU Students in Solving Real-World Problems

📸: Flapmax

QUICK BYTE: Flapmax, in collaboration with Intel, launched the Quantum AI Challenge to encourage HBCU students, faculty, and researchers to solve real-world problems using quantum computing and AI technologies.

DETAILS:

Flapmax and Intel’s Quantum AI Challenge is an initiative intended to encourage innovation and collaboration among Historically Black Colleges and Universities (HBCUs) by engaging students, faculty, and researchers in solving real-world problems using quantum computing and AI technologies.
Participants will work in teams to define and solve optimization problems in industries such as healthcare, energy, and material science using quantum algorithms and circuits, with selected teams gaining access to Intel AI PCs for quantum simulations and the chance to present their solutions to a global audience, earning prizes and mentorship.
This initiative supports the inclusion and advancement of HBCU students in the fields of AI and quantum computing, encouraging technological growth and preparing the next generation of researchers with hands-on experience in solving practical, real-world problems.

🔋 Quantum Energy Teleportation Protocol Extracts and Stores Energy from Quasi-Vacuum Using IBM Quantum Computer

Schematic of proposed QET protocol. 📸: “Extracting and Storing Energy From a Quasi-Vacuum on a Quantum Computer“

QUICK BYTE: A new quantum energy teleportation protocol extracts and stores quantum energy from a quasi-vacuum state, overcoming the previous limitation of energy loss.

DETAILS:

A research team from Purdue University and North Carolina State University has developed an enhanced quantum energy teleportation (QET) protocol that extracts energy from a quasi-vacuum state and stores it for future use in a quantum register, addressing the limitations of traditional QET protocols.
The new protocol introduces an additional qubit to the system, allowing the stored energy to remain within a quantum state, making it usable in quantum computing tasks, which was validated using IBM's superconducting quantum computers.
The team demonstrated that by leveraging entanglement between qubits, they could extract previously inaccessible quantum energy, transferring it from one qubit to another and storing it without energy loss into classical devices.
This not only has potential for practical utility in quantum energy storage but also for applications in manipulating and storing vacuum energy for quantum computing and related fields.

Scientists from the Ilmenau Institute of Physics in Germany explore the use of Krylov complexity to analyze and improve the performance of quantum machine learning tasks, especially in quantum reservoir computing and quantum extreme learning machines. It introduces the effective dimension as a new and computationally accessible measure of expressivity, showing that increased effective dimension correlates with improved task performance, such as in the Lorenz prediction tasks, providing a tool for better understanding quantum machine learning systems.

D-Wave announced a partnership with Staque, an AI, blockchain, and quantum computing consulting firm, to accelerate the adoption of annealing quantum computing across the Middle East. The partnership was recently revealed at Qubits UAE in Dubai, and will use D-Wave's quantum computers and hybrid solvers through the Leap™ quantum cloud service to develop solutions for complex enterprise challenges related to supply chain management and portfolio optimization, aligning with the Middle East's economic diversification efforts.

Researchers from Tennessee Tech University, Alexandria University, and Virginia Tech propose a space-air-ground quantum (SPARQ) network architecture for distributing entanglement in quantum communications. It introduces a deep reinforcement learning framework for solving entanglement routing challenges, using deep Q-networks to optimize routes dynamically. The results show a 39% increase in resolved teleportation requests and a 23.5% improvement in entanglement fidelity over existing space-ground networks. The proposed third-party entanglement distribution policy further enhances fidelity and reduces memory consumption.

PensionDanmark has invested DKK 70 million ($10 million) in Atom Computing, following a similar investment from the Export and Investment Fund of Denmark (EIFO), which led the U.S.-based quantum computing startup to establish its European headquarters in Denmark. Atom Computing, known for developing the first gate-based quantum platform with over 1,000 qubits, recently partnered with Microsoft with hopes to create the world’s most powerful quantum machine.

Researchers from Qognitive, Stony Brook University , MIT and others introduce a method for estimating the intrinsic dimension of data sets using quantum cognition machine learning. This approach models each data point as a quantum state and creates a quantum metric to capture the geometry of the data manifold. The key advantage of this method lies in its resilience to noise, as demonstrated through experiments on synthetic and real-world data sets like MNIST and the Wisconsin Breast Cancer Dataset. The results show that this quantum-based approach outperforms traditional intrinsic dimension estimation techniques, particularly in terms of handling noisy data.