Welcome to the Quantum Realm. 

Enjoy today’s breakdown of news, research, & events within quantum.

☀️ New fear unlocked: power grid failure due to intense geomagnetic storms. But, a hybrid classical-quantum neural network is doing the good work to prevent global catastrophe — a movie idea in the making. Plus, quantum computing for cost-effective electric grids, flying photons for a distributed quantum network, and Scotch tape (yes, that tape) for room-temperature computing.

🗓️ UPCOMING

Sunday, July 14 | QTM-X Quantum Education Series 6 of 10: Quantum Hardware

📰 QUANTUM QUICK BYTES

🌐 Grid-scale battery installation using quantum computing: Multiverse Computing, home of the Singularity software platform for integrating quantum and quantum-inspired computing into various workflows, is demonstrating quantum’s potential for positive environmental impact through a pilot project with Iberdrola. Using quantum and quantum-inspired algorithms, they successfully optimized grid-scale battery installations, which translates to grid reliability and voltage control. Part of Iberdrola’s Global Smart Grids Innovation Hub, this initiative is a testament to quantum computing's potential to assist in energy-related technology by creating more resilient and cost-effective electric grids.

💻 EuroQCS-Poland trapped-ion quantum computer has arrived: EuroHPC has just signed a contract with AQT to create EuroQCS-Poland, a trapped-ion quantum computer with over 20 physical qubits, to be located at the Poznan Supercomputing and Networking Center. This quantum computer will serve various European sectors and support applications in quantum optimization, chemistry, material sciences, and machine learning through integration with classical supercomputing systems. The EuroQCS-Poland consortium, led by PSNC and including partners from Poland and Latvia, will oversee its comprehensive hardware and software integration, with installation starting in mid-2025.

🪁 Flying photons may solve the distance problem in the development of quantum networks: A team at the Max Planck Institute of Quantum Optics has achieved near-perfect efficiency in generating atom-photon entanglement using stationary atomic qubits and flying photonic qubits. Utilizing two nearly perfect mirrors and an optical tweezer, they supported up to six entangled atom-photon pairs. This development, if scalable, could significantly advance the creation of a large, distributed quantum network, addressing the challenge of maintaining quantum information transmission over long distances.

✏️ The journey to room-temperature is just a Scotch tape leap away from tetrahertz: The occurrence of random noise at high temperatures necessitates cryo-cooled quantum devices, increasing computational costs. Professor Yoseob Yoon at Northeastern University uses lasers to manipulate materials like graphene, producing atomically thin samples with the Scotch Tape method (legitimately using Scotch tape to pull off layers). Traditionally, thermal transport in thin metallic films has been limited to the gigahertz range due to their heaviness. Yoon’s innovation in combining thermal transport with 2D materials enables control at terahertz frequencies, a thousand times higher. While this is a undoutedly a notable step toward room temperature quantum computing, challenges like faster quantum signal decay at higher temperatures still remain. The next phase of research will focus on pushing the amplitude limits of these high-frequency controls.

🔬 Simulation of electron movement: Led by Pan Jianwei, a Chinese team has built a quantum computer that simulates electron movement in solid-state materials, challenging the world’s fastest supercomputers and demonstrating the power of simulation for tackling scientific problems beyond classical computer capabilities. Using the fermionic Hubbard model, the research explores high-temperature superconductivity, which could lead to innovations in electricity transmission and addresses challenges in optical lattice creation.

🤝Zurich Instruments and Qruise partner to simplify quantum computer tune-up and operation by integrating advanced hardware with machine learning software: Zurich Instruments and Qruise have partnered to expedite quantum computer development by integrating Qruise's Machine Learning Physicist software with Zurich Instruments' Quantum Computing Control System. This collaboration will simplify the tune-up process of quantum processors, essential for minimizing error rates as quantum processing units grow through Qruise's automation technologies like Digital Twin and Rapid Automated Bring-up with Zurich Instruments' scalable SHF+ product line for high-fidelity qubit control.

☕️ FRESHLY BREWED RESEARCH

🖥️ TriQXNet: FORECASTING DST INDEX FROM SOLAR WIND DATA USING AN INTERPRETABLE PARALLEL CLASSICAL-QUANTUM FRAMEWORK WITH UNCERTAINTY CLASSIFICATION

QUICK BYTE: The accurate and scalable prediction of geomagnetic storms is necessary to prevent the collapse of the power grid, as well as blackouts for GPS, compasses, and other relevant technologies. However, previously developed machine learning modules have struggled with handling the high dimensionality of the data. The researchers ideate and experiment with a hybrid classical-quantum neural network with explainable AI that outperforms previous methods while requiring fewer parameters.

PRE-REQS: 

• Geomagnetic storms are disruptions to Earth’s magnetic field caused by solar wind. They can negatively impact our power grids and satellite communications, which has led to great efforts to predict such disturbances for decades.

• The Dst index, also known as the Disturbance Storm-Time index measures the intensity of geomagnetic storms. It is based on data collected from global observatories and relates to the weakening of Earth’s magnetic field. The lower the number, the greater the intensity.

• Conformal prediction is used to provide uncertainty estimates in forecasts. In terms of this use case, it is used to quantify the uncertainty in the Dst index predictions.

SIGNIFICANCE: Geomagnetic storms have the potential to cause profound disruptions to our power grids, GPS navigation, and satellite communications. These storms also compromise the accuracy of devices that rely on magnetic field measurements, such as compasses or magnetometers.

The Dst index is based on data from observatories in San Juan, Honolulu, Kakioka, and Hermanus and is used to describe the intensity of these storms. Lower Dst readings indicate the weakening of Earth’s magnetic field with the numerical decrease associated with an increase in the strength of the storm. While many methods have been attempted and refined over the past several decades, the more advanced ones rely on classical machine learning. While relatively powerful, they suffer under the strain of the high dimensionality and complexity of the datasets.

The innovation lies in the nomenclature — TriQXNet — which stands for a hybrid classical-quantum neural network utilizing three parallel channels for data processing, quantum computing, and explainable AI, to provide enhanced geomagnetic storm forecasting.

RESULTS: 

• TriQXNet outperforms 13 state-of-the-art hybrid deep-learning models, achieving a root mean squared error of 9.27nanoteslas.

• Rigorous evaluation through 10-fold cross-validated paired t-tests confirmed TriQXNet’s improved performance with 95% confidence.

• The model demonstrated exceptional forecasting accuracy for dual hours, particularly during periods of rapid Dst value decline (intensifying storms).

• Despite its advancements, the model does face limitations such as reliance on high-quality real-time solar wind data and substantial computational resource requirements. There is a need to explore additional quantum data encoding techniques to further improve performance.

HONORABLE RESEARCH MENTIONS: 

The Quantum-Train technique is complemented by a Long Short-Term Memory model for flood prediction, using QML to reduce the number of trainable parameters. Classical neural network weights are mapped to a Hilbert space, allowing the quantum-assisted model to process classical data directly and operate independently of quantum resources post-training. Although the model ultimately showed a slight decrease in accuracy compared to classical methods, it demonstrated considerable efficiency and scalability, which could provide enough value to offset the accuracy of classical models in real-world flood prediction applications. —> link to paper

The performance of small-satellite-based quantum key distribution missions under finite-resource constraints is analyzed. Recent advances in finite-key analysis allow missions like CQT-Sat, QUARC-ROKS, and QEYSSat to generate secure keys even with high channel losses. This is significant for satellites in low Earth orbit but remains challenging for higher altitudes. The study provides a detailed assessment of these missions and discusses improvements in finite-key analysis and hardware needed to enhance the performance of small-satellite QKD missions for global quantum communication. —> link to paper

Reductive Quantum Phase Estimation is introduced as a new class of quantum phase estimation protocols that generalize and extend the capabilities of traditional methods like Ramsey Interferometry and Quantum Phase Estimation. The authors present an algorithm to construct RQPE circuits that require fewer qubits and unitary operations while maintaining high phase distinguishability. This approach offers a tradeoff between measurement precision and phase distinguishability, making it adaptable for various quantum applications and improving efficiency over existing methods. —> link to paper

A method for realizing higher-order topological lattices on a quantum computer using NISQ devices is demonstrated by encoding high-dimensional lattices into many-body interactions within a lower-dimensional model and taking advantage of the quantum computer's large Hilbert space to reduce the number of required qubits and operations. This approach is successfully demonstrated on IBM quantum processors, simulating topological states and protected mid-gap spectra in up to four dimensions, showcasing the potential for useful quantum simulations beyond classical capabilities. —> link to paper

UNTIL TOMORROW.