Welcome to The Daily Qubit!

Get the latest in top quantum news and research Monday through Friday, summarized for quick reading so you stay informed without missing a qubit.

Have questions, feedback, or ideas? Fill out the survey at the end of the issue or email me directly at [email protected].

And remember—friends don’t let friends miss out on the quantum era. If you enjoy The Daily Qubit, pass it along to others who’d appreciate it too.

Happy reading and onward!

Cierra

Today’s issue includes:

• A hybrid quantum and complex-valued neural network is used for predicting phases in multi-principal element alloys.

• High-dynamic-range quantum sensing of magnons is demonstrated using superconducting qubits to precisely measure magnon properties and dynamics.

• A hybrid quantum-classical reinforcement learning model that combines quantum computing and classical neural networks to solve high-dimensional reinforcement learning problems, such as Atari games like Pong and Breakout.

QUANTUM APPLICATION HEADLINES

⚛️ Quantum-Assisted AI Applied to Accurate Phase Predictions for Advanced Alloys

APPLICATION: A team from Zhengzhou University, Chinese Academy of Sciences, and others introduce a hybrid quantum and complex-valued neural network for predicting phases in multi-principal element alloys. By integrating quantum networks and complex-valued neural networks, it may address challenges in accurately predicting alloy phases using only compositional data.

SIGNIFICANCE: Multi-principal element alloys, sometimes called high-entropy alloys, are advanced materials made from five or more main elements combined in nearly equal proportions. This unique structure gives them properties such as high strength, durability, and thermal stability, making them useful for aerospace, energy, and other demanding industries. However, determining the phase—the specific arrangement of atoms that gives the material its structure and properties—has been a major challenge. This study simplifies the process by using only the raw elemental composition as input, skipping the need for descriptors or time-consuming simulations. The model can also identify critical phase transition thresholds, the precise conditions (like composition ratios) where materials shift from one phase to another—an essential step in designing alloys with specific performance characteristics.

HOW: The QCVNN hybrid model uses quantum networks to process sparse compositional data by encoding it into a higher-dimensional complex space using amplitude encoding, which increases the amount of information carried in the input. This encoded data is then analyzed using complex-valued neural networks, which operate natively in the complex domain to minimize the loss of information often seen when transitioning between real and complex numbers. By combining these quantum and neural network techniques, the model efficiently learns relationships within compositional data without relying on manually derived descriptors. During testing, the QCVNN outperformed classical machine learning models and standalone quantum networks, demonstrating its ability to accurately map elemental compositions to specific alloy phases and robustly predict phase transitions in AlxCoCrFeNi alloys.

BY THE NUMBERS:

• 94.93% – Overall accuracy achieved by QCVNN in predicting alloy phases, outperforming the best classical machine learning model (92.64%) and pure quantum models (86.26%).

• 2.29% – Improvement in accuracy over the leading classical model, showcasing the benefit of combining quantum encoding with CVNN processing.

• 5 phases – Categories accurately predicted by the QCVNN: amorphous (AM), body-centered cubic (BCC), face-centered cubic (FCC), mixed BCC+FCC, and intermetallic (IM)—all phases which dictate the material's structural and mechanical properties.

🧲 Superconducting Qubits are Used for High-Precision Sensing of Magnon Dynamics Across Thousands of Excitations

APPLICATION: Researchers from the University of Illinois demonstrates high-dynamic-range quantum sensing of magnons—quantized spin-wave excitations in magnetic materials—using superconducting qubits. The method precisely measures magnon properties and dynamics, enabling advanced quantum sensing applications.

SIGNIFICANCE: Magnons are collective spin excitations in magnetic materials that can carry and manipulate information in hybrid quantum devices. They are promising for applications like quantum transduction (converting quantum information between systems) and nonreciprocal devices, which allow one-way quantum signal transfer. However, to harness magnons effectively, it’s critical to characterize their properties, such as decay rates, across a broad range of excitations. This study showcases the use of superconducting qubits as high-sensitivity probes capable of detecting magnons with high accuracy and range. This opens new possibilities for understanding nonlinear magnon dynamics (behavior beyond simple harmonic motion) and integrating magnons into future quantum technologies.

HOW: The researchers coupled a superconducting transmon qubit to magnons within a ferrimagnetic yttrium-iron-garnet sphere placed inside a microwave cavity. The qubit acted as a sensor, detecting weak interactions with the magnons through shifts in its operating frequency—a phenomenon known as dispersive interaction. By precisely controlling the qubit with microwave signals, they measured the qubit’s frequency shifts and dephasing to infer the magnon population. To resolve magnon decay (the rate at which the excitations dissipate), they applied parametric pumping allowing energy exchange between the two systems. This energy exchange effectively “mapped” magnon decay onto the qubit’s relaxation time, enabling accurate measurement of magnon lifetimes even when they decay too quickly for traditional methods. Together, these techniques allowed the team to measure magnon populations with extreme sensitivity and observe their behavior over a dynamic range of thousands of excitations.

BY THE NUMBERS

• 2,000 excitations – The largest number of magnons detected in this experiment. By resolving a high dynamic range, the method can capture both microscopic (few magnons) and macroscopic (many magnons) behaviors.

• 67 kHz per magnon – The dispersive shift detected, where each magnon causes a slight frequency shift in the qubit; this shift is necessary for determining magnon population size.

• 34–40 nanoseconds – The observed magnon decay time, or how quickly magnons dissipate energy—the precision that enables deeper analysis of material performance and energy loss mechanisms.

🎮️ Quantum-Classical Model Brings Reinforcement Learning to High-Dimensional Challenges

APPLICATION: A team from Technische Universität in Austria and Freie Universität Berlin developed a hybrid quantum-classical reinforcement learning model that combines quantum computing and classical neural networks to solve high-dimensional reinforcement learning problems, such as Atari games like Pong and Breakout.

SIGNIFICANCE: Reinforcement learning, a method where agents learn decision-making by interacting with an environment, has achieved notable results using deep neural networks but remains computationally expensive. Quantum computing has the potential to speed up certain tasks within RL. However, early quantum RL models struggled with high-dimensional input spaces, limiting their practical application. This study addresses that challenge by integrating classical convolutional layers for dimensionality reduction with parameterized quantum circuits for processing features. The hybrid model successfully learns Atari games—complex tasks with visual inputs—and achieves comparable performance to classical baselines.

HOW: The proposed model uses a hybrid architecture where classical convolutional layers first extract informative features from high-dimensional game images, reducing complexity before inputting them into a PQC. The PQC processes these latent features and the output is post-processed by a classical fully connected layer, which translates the results into Q-values (scores for actions). To optimize learning, the researchers adjusted reward scaling and learning rates, ensuring that the quantum model efficiently explored the environment and learned effective policies. Tests on Pong showed the hybrid model matched classical performance while learning more consistently. In Breakout, it achieved non-trivial scores, with optimizations narrowing the performance gap to 13% compared to classical models.

BY THE NUMBERS: 

• 2 games – Atari games Pong and Breakout used to evaluate the hybrid model’s learning capabilities.

• ~2 million steps – Training steps required for the hybrid model to reach a mean score of 84 in Breakout, demonstrating its capacity to learn complex tasks.

• 13% gap – Performance difference between the optimized hybrid model and classical baseline in Breakout, significantly reduced from earlier attempts.

• 4 out of 5 – Successful learning runs in Pong for the hybrid model, showing greater consistency compared to classical baselines. 

RESEARCH HIGHLIGHTS

✨ Scientists from the University of Science and Technology of China, QuatumCTek Co., the National Institute of Metrology and others introduce Zuchongzhi 3.0, a superconducting quantum processor with 105 qubits and high operational fidelities (99.90% single-qubit, 99.62% two-qubit, 99.18% readout fidelity). According to the study, the researchers conducted 83-qubit, 32-cycle random circuit sampling, achieving one million samples in just a few hundred seconds.

🖥️ A study from the National University of Singapore, Nanyang Technological University, and others demonstrates enhanced quantum tomography using quantum reservoir processing on a bosonic quantum electrodynamics platform. By treating the system's dynamics as a “black box,” QRP accurately reconstructs quantum states and processes while accounting for real-world imperfections like decoherence and systematic errors. The results achieve state reconstruction fidelities exceeding 91%, an improvement over standard methods (59%).

✅ IBM Quantum, Harvard, Caltech, and others demonstrate the creation of long-range Ising order—spins remain highly correlated—using a constant-depth, measurement-based protocol on a 127-qubit superconducting processor. By using mid-circuit measurements and classical decoding, the researchers stabilize this order against both coherent and incoherent errors, tuning the system to observe a Nishimori phase transition, a behavior typically requiring fine-tuning in classical systems.

NEWS QUICK BYTES

💰️ SEALSQ Corp has secured $25 million through a securities purchase agreement with institutional investors, offering 13.16 million shares at $1.90 per share. The proceeds will fund the deployment of next-generation post-quantum semiconductor technology and ASIC capabilities in the United States, alongside supporting working capital and general corporate purposes. The offering, facilitated by Maxim Group LLC, is expected to close on December 17, 2024, pending standard closing conditions.

👩‍🔬 The NSF has selected six new pilot projects under the National Quantum Virtual Laboratory initiative, awarding $1 million each to accelerate quantum technology development. The projects include advancing analog quantum hardware (Q-BLUE), building a high-performance quantum network (ASPEN-Net), developing quantum error correction platforms (ERASE), creating a 60-logical-qubit fault-tolerant computer (FTL), exploring quantum-enhanced chemical sensing (DQS-CP), and enabling quantum photonic integrated circuits for precision measurements (QuPID).

💡 SDT and Pusan National University are developing a compact, rack-mountable quantum entangled photon pair light source, a critical technology for quantum communication, cryptography, and information processing. The system, featuring a photon spectral width 1/1000th narrower and more stable than traditional nonlinear crystal-based sources, enables applications such as quantum LiDAR, microscopy, and long-distance quantum networks. The team plans to commercialize the world’s first atomic-based quantum light source, verifying its performance and conducting fiber network tests up to 100 km.

🔐 Banco Sabadell, in collaboration with Accenture and QuSecure, successfully completed a four-month pilot integrating post-quantum cryptography technologies to protect financial data against quantum threats. The project utilized QuSecure's encryption agility software and open-source libraries to modernize cryptographic systems without requiring major infrastructure changes. Accenture provided a quantum-resilient roadmap, positioning Banco Sabadell as an early adopter of PQC.

🔢 Synergy Quantum has successfully deployed its advanced Quantum Random Number Generator at India's Centre for Development of Advanced Computing (C-DAC). This collaboration strengthens India's cryptographic resilience by providing hardware-independent, secure randomness essential for Post-Quantum Encryption (PQE), ensuring protection against quantum computing threats.

🤝 SDT, LG Electronics, KRISS, and Korea University have signed an MOU to advance neutral atom-based quantum computing technology through industry-academia-research collaboration. KRISS and Korea University will develop neutral atom-based QPUs, LG Electronics will oversee middleware development and integration, and SDT will focus on hardware development, cloud services, and commercialization strategies.

✨ Israel has announced its first domestically built 20-qubit quantum computer, developed using superconducting technology through a collaboration between the Israel Innovation Authority, Israel Aerospace Industries (IAI), the Hebrew University, and Yissum. This milestone establishes Israel’s first superconductor-based quantum infrastructure, enabling advancements in defense and civilian applications while positioning the country among the global leaders in quantum technology. IAI’s Quantum QHIPU lab will focus on designing practical applications, further solidifying Israel’s role in quantum research and innovation.

🔒️ Australia’s cybersecurity agency, the Australian Signals Directorate, has announced plans to disallow widely used cryptographic algorithms—including SHA-256, RSA, ECDSA, and ECDH—by 2030 over concerns that advances in quantum computing could render them insecure. This timeline is notably more aggressive than the U.S. National Institute for Standards and Technology (NIST) and NSA, which aim for a transition by 2035.

🤝 SEALSQ, in collaboration with WISeID.COM, has integrated advanced post-quantum cryptography to secure electronic identities and documents, ensuring long-term protection against the risks posed by quantum computing. By enabling crypto-agility—systems capable of supporting multiple cryptographic algorithms—SEALSQ may facilitate a smooth transition to post-quantum security while maintaining compatibility with existing infrastructure.

QUANTUM MEDIA

LISTEN

Tobias Lindstrom, Head of Science for the Department of Quantum Technologies at the National Physical Laboratory (NPL) in the UK, is interviewed by Yuval Boger. They discuss the critical role of national measurement institutes in the quantum ecosystem. Tobias explains how NPL bridges the gap between academia and industry, delves into the complexities of quantum benchmarking and standardization, and explores advancements in quantum communications, sensing, and computing. They also touch on the evolving quantum supply chain, the role of neutrality in measurement services, and what the future holds for quantum technologies.

THAT’S A WRAP.