How Qubits Advance Scientific Computing

The last two days have seen two extremely interesting breakthroughs announced in quantum computing. There is a long path ahead, but these both point to the potential for dramatically upscaling ambitions for what's possible in relatively short timeframes. The most prominent advance was Microsoft's announcement of Majorana 1, a chip powered by "topological qubits" using a new material. This enables hardware-protected qubits that are more stable and fault-tolerant. The chip currently contains 8 topologic qubits, but it is designed to house one million. This is many orders of dimension larger than current systems. DARPA has selected the system for its utility-scale quantum computing program. Microsoft believes they can create a fault-tolerant quantum computer prototype in years. The other breakthrough is extraordinary: quantum gate teleportation, linking two quantum processes using quantum teleportation. Instead of packing millions of qubits into a single machine—which is exceptionally challenging—this approach allows smaller quantum devices to be connected via optical fibers, working together as one system. Oxford University researchers proved that distributed quantum computing can perform powerful calculations more efficiently than classical systems. This could not only create a pathway to workable quantum computers, but also a quantum internet, enabling ultra-secure communication and advanced computational capabilities. It certainly seems that the pace of scientific progress is increasing. Some of the applications - such as in quantum computing - could have massive implications, including in turn accelerating science across domains.

Google’s 69-Qubit Quantum Simulator Outperforms Supercomputers in Key Calculations Researchers from Google and the PSI Center for Scientific Computing have developed a 69-qubit quantum simulator that can outperform the fastest classical supercomputers in studying complex quantum systems. This breakthrough brings unprecedented accuracy in modeling quantum processes, unlocking new possibilities in materials science, magnetism, and thermodynamics. Key Features of Google’s Quantum Simulator • Combines Digital & Analog Quantum Computing: The simulator supports both universal quantum gates (digital mode) and high-fidelity analog evolution, providing superior performance in cross-entropy benchmarking experiments. • Beyond Classical Computational Limits: This hybrid approach enables calculations that classical supercomputers cannot efficiently simulate, especially in quantum material and energy research. • Specialized for Quantum Simulations: Unlike general-purpose quantum computers, this simulator is optimized for modeling quantum interactions, making it a powerful tool for scientific discovery. Digital vs. Analog Quantum Computing • Digital Quantum Computing: • Uses quantum gates to manipulate qubits, similar to logic gates in classical computing. • Best suited for algorithms, machine learning, and cryptography applications. • Analog Quantum Computing: • Models physical quantum systems directly, simulating real-world interactions with fewer computational steps. • Ideal for studying material science, condensed matter physics, and quantum thermodynamics. Why This Matters • Accelerating Scientific Research: The simulator could help discover new materials, improve energy storage, and refine magnetism-based technologies. • Advancing Quantum Supremacy: By achieving results beyond classical computation, this simulator cements Google’s lead in quantum research. • Potential for Quantum AI Integration: Combining digital and analog approaches may enhance machine learning models and optimize large-scale computations. What’s Next? • Expanding Qubit Count: Google may scale up its hybrid quantum simulations, pushing closer to full-scale quantum supremacy. • Exploring More Applications: Future research could apply these simulations to biophysics, drug discovery, and nuclear physics. • Potential Industry Collaborations: Google’s breakthrough may lead to partnerships in materials engineering and quantum-enhanced AI systems. This 69-qubit quantum simulator represents a major leap in computational power, proving that quantum systems can now surpass supercomputers in specialized scientific tasks, bringing us closer to practical quantum applications.

Major milestone achieved in new quantum computing architecture "A team led by the U.S. Department of Energy (DOE)’s Argonne National Laboratory has achieved a major milestone toward future quantum computing. They have extended the coherence time for their novel type of qubit to an impressive 0.1 milliseconds — nearly a thousand times better than the previous record." "The team’s qubit is a single electron trapped on an ultraclean solid-neon surface in a vacuum. The neon is important because it resists disturbance from the surrounding environment. Neon is one of a handful of elements that do not react with other elements. The neon platform keeps the electron qubit protected and inherently guarantees a long coherence time." "Yet another important attribute of a qubit is its scalability to link with many other qubits. The team achieved a significant milestone by showing that two-electron qubits can couple to the same superconducting circuit such that information can be transferred between them through the circuit. This marks a pivotal stride toward two-qubit entanglement, a critical aspect of quantum computing." "The team has not yet fully optimized their electron qubit and will continue to work on extending the coherence time even further as well as entangling two or more qubits." This research was published in Nature Physics (https://lnkd.in/d5Y5Dfea) https://lnkd.in/dkXd_Uje

🔗✨ Exploring the Future of Quantum Computing with Physics-Informed Neural Networks (PINNs) ✨🔗 Excited to highlight the pioneering work by Stefano Markidis that dives deep into the potential of Quantum Physics-Informed Neural Networks (Quantum PINNs) for solving differential equations on hybrid CPU-QPU systems! 📘 What’s this about? Physics-Informed Neural Networks (PINNs) have proven their versatility in addressing scientific computing challenges. This study extends PINNs into the quantum realm using Continuous Variable (CV) Quantum Computing, offering a new approach to solving Partial Differential Equations (PDEs) with quantum hardware. Key Highlights: ✅ Quantum Meets Physics: The framework combines CV quantum neural networks with classical methods to tackle PDEs like the 1D Poisson equation. ✅ Optimizer Insights: Traditional optimizers like SGD outperformed adaptive methods in this quantum landscape, highlighting the unique challenges of quantum optimization. ✅ Scalability: Explores batch processing and neural network depth for more effective performance on quantum systems. ✅ Programming Ease: Tools like Strawberry Fields and TensorFlow simplify the integration of quantum and classical computations. 💡 Why it matters: This research doesn't just apply PINNs to quantum computing—it highlights the differences between classical and quantum approaches, paving the way for advancements in quantum PINN solvers and their real-world applications in computational physics, electromagnetics, and more. 📖 Dive deeper: Access the full study here: https://lnkd.in/dZm3F3CR Source code available: https://lnkd.in/dAsXxnbN What are your thoughts on combining quantum computing with AI for scientific breakthroughs? Let’s discuss! 🚀 #QuantumComputing #PhysicsInformedNeuralNetworks #ScientificComputing #HybridAI #PDEsolvers #Innovation

Exciting yet under-the-radar paper (arXiv:2506.10191) from Google Quantum AI on higher-order OTOCs (out-of-time-order correlators) -- a big leap toward practical (scientific) quantum advantage! 🚀 Using their Willow chip with ~100 qubits, they’ve shown remarkable result, yet it’s surprising this hasn’t sparked more buzz -- perhaps because OTOCs are tricky to explain to a wider audience. 🤔   Key Takeaways: 🕒 Quantum Speed: Willow chip solves quantum Hamiltonian properties in ~2.1 hours, using ~40 kWh of energy. 💻 Classical Lag: Best classical method (tensor networks) on Frontier supercomputer estimated to take 3.2 years, 550GWh energy—practically infeasible! 🧪 Real-World Impact: Enables learning properties of quantum materials, with applications in chemistry and quantum control. 10,000x reduction in needed energy for simulation.   This showcases power of NISQ-era quantum devices for quantum simulation. Shall we call it scientific quantum advantage? 📢 #QuantumComputing #QuantumAdvantage #GoogleQuantumAI

Google has made significant strides in quantum computing with the development of its latest quantum chip, Willow. This chip represents a major advancement toward building practical, large-scale quantum computers capable of solving complex problems far beyond the reach of classical supercomputers. Key Features of Willow: (1) Enhanced Qubit Count: Willow boasts 105 qubits, nearly doubling the count from its predecessor, the Sycamore chip. This increase enables more complex computations and improved error correction capabilities. (2) Error Correction Breakthrough: A notable achievement with Willow is its ability to reduce errors exponentially as the system scales. This addresses a fundamental challenge in quantum computing, where qubits are highly sensitive and prone to errors. By effectively managing these errors, Willow paves the way for more reliable quantum computations. (3) Unprecedented Computational Speed: In benchmark tests, Willow completed a complex computation in under five minutes—a task that would take the most advanced classical supercomputers an estimated 10 septillion years. This dramatic speedup underscores the potential of quantum computing to tackle problems currently deemed intractable. Implications and Future Prospects: The advancements demonstrated by Willow have profound implications across various fields: (4) Cryptography: The immense processing power of quantum computers like Willow could potentially break current cryptographic systems, prompting a reevaluation of data security measures. However, experts note that while Willow's 105 qubits are impressive, breaking encryption such as that used by Bitcoin would require a quantum computer with around 13 million qubits. Therefore, while the threat is not immediate, it is a consideration for the future. (5) Scientific Research: Quantum computing can revolutionize fields like drug discovery, materials science, and complex system modeling by performing simulations and calculations at unprecedented speeds. Artificial Intelligence: The ability to process vast datasets and perform complex optimizations rapidly could significantly enhance AI development and deployment. While Willow marks a significant milestone, the journey toward fully functional, large-scale quantum computers continues. Ongoing research focuses on further increasing qubit counts, enhancing error correction methods, and developing practical applications for this transformative technology.

By driving a quantum processor with laser pulses arranged according to the Fibonacci sequence, physicists observed the emergence of an entirely new phase of matter—one that displays extraordinary stability in a domain where fragility is the norm. Quantum computers operate using qubits, which differ radically from classical bits. A qubit can exist in superposition, occupying multiple states at once, and can become entangled with others across space. These properties enable immense computational power, but they come with a cost: quantum states are notoriously short-lived. Environmental noise, microscopic imperfections, and edge effects rapidly degrade coherence, limiting how long quantum information can survive. Seeking a new way to protect fragile quantum states, scientists at the Flatiron Institute, instead of applying laser pulses at regular intervals, they used a rhythm governed by the Fibonacci sequence—an ordered but non-repeating pattern long known to appear in biological growth, crystal structures, and wave interference. The experiment was carried out on a chain of ten trapped-ion qubits, driven by precisely timed laser pulses. The result was the formation of what is described as a time quasicrystal. Unlike ordinary crystals, which repeat periodically in space, a time quasicrystal exhibits structure in time without repeating in a simple cycle. The Fibonacci-based driving created a temporal order that resisted disruption, allowing the quantum system to remain coherent far longer than expected. The improvement was significant. Under standard conditions, the quantum state persisted for roughly 1.5 seconds. When driven by the Fibonacci pulse sequence, coherence times stretched to approximately 5.5 seconds—more than a threefold increase. Even more intriguing was the system’s temporal behavior. Measurements indicated that the quantum dynamics unfolded as if time itself possessed two independent structural directions. This does not imply time flowing backward, but rather that the system’s evolution followed two intertwined temporal pathways—an emergent property arising purely from the Fibonacci drive. The researchers propose that the non-repeating structure of the Fibonacci sequence suppresses errors that typically accumulate at the boundaries of quantum systems. By distributing disturbances in a highly ordered yet aperiodic way, the sequence stabilizes the collective behavior of the qubits. In effect, a mathematical pattern found throughout nature acts as a self-organizing error-management protocol. The findings suggest a powerful new strategy for quantum control. Rather than fighting noise solely with complex correction algorithms, future quantum technologies may harness structured patterns—drawn from mathematics and natural order—to achieve resilience at a fundamental level. https://lnkd.in/dVxp7R8J https://lnkd.in/dDVNRsPk

We’ve all known #Moderna for some years for their role in transforming mRNA science to rapidly develop life-saving vaccines and therapeutics. But what you might not know is that behind almost every mRNA innovation lies an incredibly hard problem: figuring out how each sequence folds. Each mRNA strand can twist and loop into an astronomical number of secondary structures. Only a handful of those make sense, given the physical laws governing molecular behavior. Predicting which ones are biologically plausible? That involves solving a complex combinatorial optimization problem, which turns out to be a sweet spot for quantum computing… exactly where pure classic approaches hit a wall. So the team began creating and testing quantum novel algorithms -like CVaR VQE- and benchmarking them against classical solvers to predict mRNA folding. And the results? The Quantum-enabled pipeline is already matching classic solvers and is expected to augment beyond what’s at reach of classic computers today. ‼️ You can read all details here: https://lnkd.in/ex5gxDCn. You will learn about: 🔹 A 𝐧𝐞𝐚𝐫-𝐭𝐞𝐫𝐦 𝐪𝐮𝐚𝐧𝐭𝐮𝐦-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐛𝐢𝐨𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 🔹 Massive 𝐥𝐚𝐫𝐠𝐞 𝐬𝐜𝐚𝐥𝐞: last year, we ran the largest variational quantum algorithm yet -80 qubits modeling 60-nucleotide mRNA strands (and targeting this year 156 qubits and 950-gate circuits) 🔹A 𝐜𝐥𝐞𝐯𝐞𝐫 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐛𝐨𝐨𝐬𝐭: adding a Conditional Value at Risk (CVaR) lightweight classical post-processing step, to reduce the sensitivity to noisy outliers. 🔹 𝐑𝐞𝐜𝐨𝐫𝐝-𝐦𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: the quantum-enhanced simulations are now reaching the same quality as top classical solvers and aiming at going beyond, proving what a powerful platform Quantum Computing is for Science. To me, this case study perfectly shows 2 vectors we are fully committed at IBM: 1. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦-𝐜𝐥𝐚𝐬𝐬𝐢𝐜 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: the future of computing is going to be full of these hybrid approaches aiming at combining the most efficient use of quantum and classical resources in a 𝐣𝐨𝐢𝐧𝐭 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐡𝐢𝐠𝐡-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 (𝐇𝐏𝐂) 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭. 2. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: when you represent your problem in mathematical terms, abstracting from the domain, it is much easier to borrow ideas from other domains and boost innovation (probably you know that CVaR or Conditional Value at Risk comes from finance). IBM IBM Research IBM Quantum #innovationthatmatters #Science #FutureOfComputing

🔬 Researchers have developed a solution for superconducting quantum processors, addressing the challenge of delivering microwave signals from room-temperature electronics to the cryogenic environment through coaxial cables. This setup is not viable for the millions of qubits required for fault-tolerant quantum computing due to the heat load of cabling and the cost of electronics. 🛠️ The solution: Monolithic integration of control electronics and qubits, which requires a coherent cryogenic microwave pulse generator compatible with superconducting quantum circuits. 🔎 Key advancements: 💡 A signal source driven by digital-like signals. 📡 Pulsed microwave emission with well-controlled phase, intensity, and frequency directly at millikelvin temperatures. 🎯 High-fidelity readout of superconducting qubits with the microwave pulse generator. 🧩 This device has a small footprint, negligible heat load, and great flexibility in operation. It is fully compatible with today’s superconducting quantum circuits, providing an enabling technology for large-scale superconducting quantum computers! 🖥️💫 #QuantumComputing #SuperconductingQubits #Innovation #Technology #Research #FutureOfComputing

Quest - ION Everything Scientists are turning light into multidimensional quantum shapes. Light has always been strange. But scientists are now shaping it in ways that were once pure theory — turning simple photons into powerful tools. A review outlines a rapidly growing field called quantum structured light, where researchers manipulate several properties at once: polarization, spatial patterns, and frequency. By controlling these “degrees of freedom,” they create high‑dimensional quantum states that go beyond the simple on/off bits used in traditional computing. In most quantum systems, information is stored in qubits. These are two‑state quantum objects, like a photon that can be horizontal or vertical in polarization. But structured light uses qudits — quantum states with more than two levels. One qudit can carry far more information than a qubit, and doing this with a single photon means you can send more data without needing more particles. For quantum communication, this expansion means stronger security. Each high‑dimensional photon can carry more information and resist noise and interference better than conventional light signals. That’s critical when data is encrypted or sent across networks where eavesdropping must be minimized. In quantum computing, structured light simplifies circuit designs and makes it easier to build complex quantum states needed for advanced simulations. Instead of stringing together many qubits, researchers can encode more information in fewer, richer quantum objects. Structured light is also opening new doors in imaging and measurement. Holographic quantum microscopes, for example, use these techniques to image delicate biological samples without damaging them. And quantum correlations in light waves are being used to build sensors with extraordinary sensitivity. But challenges remain. Scientists still struggle to maintain these states over long distances. But as on‑chip sources and compact control systems improve, quantum structured light is moving out of the lab and into real‑world applications. Read the study: "Progress in quantum structured light.” Nature Photonics, 2025.