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:

• Scientists developed a quantum version of Gaussian processes to estimate electrical grid line parameters using quantum algorithms.

• Researchers explore how quantum computing may improve biomarker discovery in healthcare by addressing the limitations of classical machine learning.

• A proposed shortcut to chemically accurate quantum computing integrates density-functional theory minimizes the qubit resources required.

QUANTUM APPLICATION HEADLINES

🔌 Quantum Gaussian Processes Power Up Electrical Grid Optimization

APPLICATION: Scientists from E.ON have developed a quantum version of Gaussian processes, applying it to estimate electrical grid line parameters using quantum algorithms like Harrow-Hassidim-Lloyd and approximate quantum compiling to perform computationally demanding kernel matrix inversions efficiently.

SIGNIFICANCE: As electrical grids grow increasingly complex, accurately estimating line parameters such as resistance and inductance is highly relevant. Traditional methods are challenging to scale due to the computational burden of Gaussian processes. QGP addresses this faster matrix inversions, which may provide enhanced modeling and predictions for grid maintenance, real-time monitoring, and optimization.

HOW: The quantum Gaussian processes approach integrates quantum algorithms to overcome the limitations of classical Gaussian processes in processing large datasets. By replacing classical matrix inversions with the Harrow-Hassidim-Lloyd quantum algorithm, QGP reduces computational requirements. Additionally, approximate quantum compiling optimizes the implementation of HHL by reducing circuit depth, making the method practical for current, limited devices. To validate, QGP was used to estimate resistance and inductance parameters of a transmission line using 32 samples of simulated electrical measurements. These computations were performed on both classical and quantum platforms.

BY THE NUMBERS:

• 32x32 – Size of the kernel matrix inverted using the QGP method.

• 13 qubits – Number of qubits used for the largest HHL circuit implemented in this study on IBM Auckland.

• 100 iterations – Optimization cycles conducted on quantum hardware for parameter estimation.

• 39% & 113% – Absolute errors in estimating R and L, respectively, compared to classical GPs.

❤️‍🩹 Quantum Computing for Precision Medicine May Assist Biomarker Discovery

APPLICATION: Researchers from QuantumBasel, Cleveland Clinic, DESY, IBM and others explore how quantum computing may improve biomarker discovery in healthcare by addressing the limitations of classical machine learning, specifically in areas like genomics, electronic health records, and medical imaging.

SIGNIFICANCE: As precision medicine advances, identifying reliable biomarkers is important for diagnosing and managing diseases. Classical methods struggle with large-scale, multi-modal data due to scalability and accuracy issues. Quantum algorithms may be able to more efficiently handle these challenges and provide faster, more accurate insights into disease progression, treatment responses, and patient stratification. This could assist biomarker discovery, enabling earlier diagnoses and personalized treatments for multifactorial diseases such as cancer and Alzheimer's.

HOW: Quantum computing for biomarker discovery uses advanced algorithms to address the challenges of high-dimensional, noisy, and multi-modal datasets. Techniques like quantum principal component analysis and quantum isomap help with dimensionality reduction for easier interpretation of complex biomarker datasets. For pattern recognition in omics and electronic health records, algorithms such as quantum k-means and quantum neural networks are used to identify meaningful correlations. Quantum reservoir computing improves the analysis of longitudinal medical studies by capturing intricate temporal patterns in patient data. Additionally, quantum error mitigation techniques improve the reliability of noisy datasets, providing more accurate analyses in fields like genomics and medical imaging.

BY THE NUMBERS:

• 100+ – Minimum number of use case related to healthcare currently being explored across academia and industry using quantum computing.

• 3 modalities – Key healthcare data types targeted: EHRs, omics, and medical imaging.

• 10–100 billion parameters – The number of parameters required by classical neural networks to generalize on datasets like ImageNet, highlighting the need for a solution, such as quantum algorithms, to achieve similar results with fewer data samples and model parameters.

⚛️ Researchers Achieve Chemically Accurate Simulations with Fewer Qubits

APPLICATION:  Researchers from Sorbonne Université, Qubit Pharmaceuticals, NVIDIA, and others have proposed a shortcut to chemically accurate quantum computing by integrating density-functional theory with quantum computing methods. Specifically, density-based basis-set corrections enhance the accuracy of quantum chemistry calculations while minimizing the qubit resources required.

SIGNIFICANCE: Achieving chemically accurate results in quantum chemistry is challenging due to the high computational demands of traditional methods, especially when using large basis sets. This research presents a hybrid quantum-classical method that significantly reduces these demands so that quantum computers may simulate molecular properties, ground-state energies, and dipole moments. This is highly relevant for advances in areas such as drug discovery, sustainable energy, and nanotechnology.

HOW: The proposed method combines the variational quantum eigensolver with density-based basis-set corrections to achieve near-complete basis-set accuracy in quantum chemistry calculations. Two strategies are used to improve the efficiency of these computations. The first, a posteriori corrections, applies basis-set corrections to quantum calculations after they are performed on a classical computer. The second, self-consistent corrections, iteratively integrates basis-set corrections directly into the quantum computation process. Additionally, system-adapted basis sets are introduced, dynamically optimizing the basis sets for specific molecules to further reduce computational costs. This hybrid quantum-classical approach was validated using simulations on GPU-accelerated emulators, demonstrating accuracy improvements for molecular systems such as H₂, LiH, and N₂ while requiring fewer qubits.

BY THE NUMBERS:

• Up to 32 qubits – Maximum qubit count used in simulations, as compared to the 100+ qubits typically required for similar accuracy.

• ~1.6 mHa error – Accuracy achieved in energy calculations, meeting chemical precision.

• 2 strategies – Basis-set correction methods tailored for efficiency and compatibility with current quantum devices.

RESEARCH HIGHLIGHTS

🌊 Quantinuum and bp used quantum circuits to simulate the one-dimensional acoustic wave equation on NISQ devices. The study focuses on optimizing circuit design to minimize computational errors while maintaining accuracy, providing a benchmark for evaluating quantum hardware. Results show that the quantum approach achieves scalable and efficient wave equation simulations, with accuracy improving as the number of qubits increases, highlighting its potential for applications in scientific computing and seismic imaging.

💡 Researchers from Sun Yat-sen University and QUDOOR introduce a partially connected quantum neural network (PCQNN) algorithm to solve the unit commitment problem, an optimization task in power systems. PCQNN reduces circuit depth and improves computational efficiency compared to conventional fully connected quantum neural networks. Simulation results demonstrate that the PCQNN achieves exact solutions with fewer layers and better precision, highlighting its potential for scalable quantum optimization in distributed computing environments.

✨ Scientists from Hewlett Packard, Quantum Machines, Fermilab, Qolab, and many others explore the challenges and opportunities in scaling quantum computing to utility-scale applications. They identify bottlenecks in hardware, software, and system integration, emphasizing the need for high-quality qubits, advanced error correction, and hybrid quantum-classical architectures. The study argues that leaning into semiconductor manufacturing techniques and system engineering could improve scalability and performance

NEWS QUICK BYTES

🖥️ Yonsei University and IBM have deployed Korea's first IBM Quantum System One at Yonsei’s Songdo International Campus to support research, education, and ecosystem development. Powered by a 127-qubit IBM Quantum Eagle processor, the system will enable Yonsei and its collaborators across academia, industry, and government to advance quantum algorithms, explore quantum-bio convergence, and foster quantum talent.

👩‍💻 QuEra Computing has launched a full-stack quantum algorithm co-design program to optimize the potential of neutral-atom quantum computing for businesses and research institutions. The initiative integrates hardware, software, and application development, offering tailored solutions, early simulations, and priority hardware access to accelerate quantum innovation.

💫 Microsoft and Atom Computing have announced a commercial quantum system featuring 24 logical qubits, the largest number of entangled logical qubits to date, available for delivery in 2025. Built on Atom Computing’s neutral-atom qubit technology and integrated with Microsoft’s Azure Quantum platform, the system reduces error rates compared to physical qubits and supports advanced applications in chemistry, materials science, and artificial intelligence.

⌛️ NVIDIA is collaborating with Google Quantum AI to accelerate the design of next-generation quantum processors using the CUDA-Q platform and the NVIDIA Eos supercomputer. By simulating the physics of quantum devices with 1,024 H100 Tensor Core GPUs, Google can analyze noise implications in quantum hardware designs, achieving simulations of 40-qubit systems in minutes instead of weeks.

🔒️ Quantinuum, Mitsui & Co., and NEC have successfully demonstrated the first delivery of quantum tokens over a 10km fibre-optic network in Japan using commercial quantum key distribution hardware. Quantum tokens, designed to prevent forgery, ensure privacy, and enable near-instant transaction settlement, represent an advancement in quantum-enhanced financial security.

✨ Quantum Circuits has announced the Aqumen Seeker, a quantum computing system featuring Dual-Rail Cavity Qubit technology that integrates error detection directly into its hardware. With eight DRQs and a nearest-neighbor topology, the Seeker enables high-fidelity quantum applications and provides a platform to develop and test error correction protocols.

💡 Lightsynq Technologies has emerged from stealth with $18M in Series A funding to develop quantum interconnect solutions that link quantum processors, addressing the critical challenge of scaling quantum computing networks. Founded by former Harvard and AWS quantum networking experts, Lightsynq uses diamond-based photonic devices to create scalable optical interconnects..

🧪 Infineon Technologies and Quantinuum have announced a strategic partnership to develop next-generation ion traps, specifically to advance quantum computing capabilities in areas such as generative chemistry, material science, and AI. Leaning into Infineon's work in high-volume processing and integrated photonics alongside Quantinuum's ion-trap design, the collaboration seeks to enhance scalability, fidelity, and coherence times for trapped-ion quantum processors.

🤝 SDT and Anyon Technologies have announced a joint venture to develop a 20-qubit superconducting quantum computer integrated with NVIDIA’s Grace Hopper Superchip, with production slated for early next year. With SDT’s manufacturing facilities and Anyon’s proprietary dilution refrigerator and QPU technologies, the collaboration intends to deliver advanced quantum systems across Asia, targeting industries such as financial services and AI.

💾 Alice & Bob has launched Dynamiqs, an open-source quantum simulation library that uses NVIDIA accelerated computing to achieve up to 60x faster simulations of quantum systems. Built with JAX and Diffrax, Dynamiqs addresses challenges in simulating large Hilbert spaces, open systems, and time-dependent dynamics, enabling research on quantum optimization and system control. This tool expands the capabilities of quantum simulations, supporting applications like quantum state tomography and optimal control.

⏩️ Algorithmiq has partnered with NVIDIA to develop error mitigation techniques for near-term quantum devices, using NVIDIA-accelerated supercomputing to enhance the performance and reliability of quantum systems. Early tests show a 300x speedup in Algorithmiq’s Tensor Network Error Mitigation solution.

💎 Oak Ridge National Laboratory has integrated IQM Quantum Computers' Resonance quantum cloud service into its Quantum Computing User Program, granting global researchers access to IQM's Crystal and Star quantum processing units. The Crystal QPU features a high-fidelity square-lattice topology, while the Star QPU offers a unique star topology with a central resonator for enhanced error correction.

🚗 Classiq, NVIDIA, and the BMW Group have partnered to optimize mechatronic systems using advanced quantum computing. The project uses Classiq’s quantum software platform and NVIDIA’s CUDA-Q technology to implement and simulate complex quantum programs, including QAOA and HHL algorithms, to optimize electric vehicle components for improved efficiency and reduced energy waste.

QUANTUM MEDIA

LISTEN

Yianni Gamvros and Iordanis Kerenidis, co-founders of Quantum Signals, a new startup focusing on B2B software for financial services, are interviewed by Yuval Boger. Yianni and Iordanis discuss their strategy of building classical AI pipelines to deliver immediate value to customers while planning to integrate quantum enhancements as the technology matures. They explore the challenges of optimizing large financial transactions using predictive signals powered by transformers and quantum-inspired methods, contrasting their approach with traditional trading algorithms. They discuss their initial successes, a quantum hackathon they are organizing, and much more.

THAT’S A WRAP.