Quantum Computing Developments

Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. However, there is a significant bottleneck: precise calibration is short-lived, which requires perpetually adapting the control parameters of the computer to the drifting environmental conditions. Just published in Nature, our team showed unified calibration with error-corrected computation on our Willow processor, training a reinforcement learning agent to stabilize a logical qubit and pave the way towards a quantum computer that continuously learns from its errors. Key research findings: -->✨ A New Paradigm: This work enables a future where we have a quantum computer that learns from its errors and never stops computing. Removing the need to take the system offline for calibration. --> Improved Stability: We experimentally demonstrated this framework on our Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. --> Beyond Traditional Limits: RL fine-tuning of an already well-calibrated processor yields an additional 20% suppression of the logical error rate, pushing performance beyond the limits of traditional physics-based calibration and human expert tuning. --> Scalability: Numerical simulations confirm the scalability of our RL framework, revealing that the optimization speed is independent of system size, ensuring this remains just as effective as we scale to much larger systems. This research demonstrates that the path to fault-tolerant quantum computing relies not just on better hardware, but on more intelligent control systems. I am proud of our teams for pioneering this approach, an important step towards solving the challenges in quantum information science. Nature article here: https://lnkd.in/gfuxKYJf

The dirty secret of Quantum Computing… Materials are the limiting factor. Everyone talks about quantum algorithms, error correction, and qubit counts. But the real killer of quantum computing isn’t software, it’s materials. Superconducting qubits don’t decohere because we lack clever code. They decohere because: – Surface oxides introduce two-level system noise. – Impurities and defects act like microscopic time bombs. – Atomic-scale disorder destroys coherence before circuits can compute anything useful. That’s why the biggest breakthroughs aren’t happening in code, they’re happening in materials labs. → Google is building qubits with ultra-clean Al/Si interfaces to suppress noise. → IBM is investing in substrate purification to push coherence times further. → Labs worldwide are chasing epitaxial aluminum films with sub-ppm impurity levels. The “quantum revolution” is being held back by dirt, literally. Until we tame materials noise, scaling qubits is just scaling errors. Quantum doesn’t need another hype cycle. It needs a materials breakthrough. #QuantumComputing #MaterialScience #GrowthAndInnovation #DeepTech

We may be standing at a moment in time for Quantum Computing that mirrors the 2017 breakthrough on transformers – a spark that ignited the generative AI revolution 5 years later. With recent advancements from Google, Microsoft, IBM and Amazon in developing more powerful and stable quantum chips, the trajectory of QC is accelerating faster than many of us expected.   Google’s Sycamore and next gen Willow chips are demonstrating increasing fidelity. Microsoft’s pursuit of topological qubits using Majorana particles promises longer coherence times and IBM’s roadmap is pushing towards modular error corrected systems. These aren’t just incremental steps, they are setting the stage for scalable, fault tolerant quantum machines.   Quantum systems excel at simulating the behavior of molecules and materials at atomic scale, solving optimization problems with exponentially large solution spaces and modeling complex probabilistic systems – tasks that could take classical supercomputers millennia. For example, accurately simulating protein folding or discovering new catalysts for carbon capture are well within quantum’s potential reach.   If scalable QC is just five years away, now is the time to ask : What would you do differently today, if quantum was real tomorrow ?. That question isn’t hypothetical – it’s an invitation to start rethinking foundational problems in chemistry, logistics, finance, AI and cryptography.   Of course building quantum systems is notoriously hard. Fragile qubits, error correction and decoherence remain formidable challenges. But globally public and private institutions are pouring resources into cracking these problems. I was in LA today visiting the famous USC Information Sciences Institute where cutting edge work on QC is underway and the energy is palpable.   This feels like a pivotal moment. One where future shaping ideas are being tested in real labs. Just as with AI, the future belongs to those preparing for it now. QC Is an area of emphasis at Visa Research and I hope it is part of how other organizations are thinking about the future too.

Trying to enter QML in 2026? This is the path I’d take, step by step. A Quantum Machine Learning roadmap should build three pillars in parallel: 1)Mathematics & Classical ML foundations 2)Quantum Computing foundations 3)Hybrid Quantum-Classical ML implementation → Advance QML Models Think of QML as ML + Linear Algebra + Quantum Mechanics + Optimization Step 1: Mathematics, Python, ML Stack, & ML Basics Linear Algebra - vectors, matrices, eigenvalues, tensor products Probability & Statistics - distributions, expectation, variance Optimization - gradient descent, loss functions Python - NumPy, SciPy, Matplotlib PyTorch or TensorFlow, Scikit-learn Supervised, Unsupervised Learning Regression, Classification, Overfitting, Regularization Neural Networks, CNN basics Goal: You should be comfortable building classical ML pipelines Step 2: Quantum Computing Foundations Qubits, superposition, measurement, Bloch sphere Quantum gates, Entanglement and Bell states Quantum circuits, Interference Quantum Algorithms - Deutsch-Jozsa, Grover’s Algorithm, Quantum Fourier Transform, Variational Quantum Algorithms Qiskit, Cirq, Q#(1 of them) Goal: You must think in circuits before doing QML Step 3: Bridge to QML Parameterized Quantum Circuits Variational circuits Classical-quantum feedback loop Cost functions Barren plateaus Expressibility & trainability Difference between: Quantum data → quantum model Classical data → quantum embedding PennyLane, TensorFlow Quantum, Qiskit ML Goal: Understand QML is optimization on quantum parameters Step 4: Core QML Models Quantum Data Encoding Angle embedding Amplitude encoding Basis encoding Quantum Models Variational Quantum Classifier Quantum Neural Networks Quantum Kernel Methods Quantum Support Vector Machines Data re-uploading circuits Compare: Classical NN vs VQC Classical SVM vs Quantum Kernel Goal: Show measurable learning, not just circuit execution Step 5: Advanced QML Concepts Barren Plateaus Noise-aware training Hardware-efficient ansatz Quantum Convolutional Neural Networks Quantum Autoencoders QGANs QML for anomaly detection NISQ Constraints - Noise, Shot statistics, Error mitigation Goal: You understand real-world limitations and research gaps Step 6: Research Grade QML Read Papers Schuld & Killoran (Quantum ML theory) Havlíček et al. (Quantum kernel methods) McClean et al. (Barren plateaus) Cerezo et al. (Variational algorithms) Hybrid classical-quantum architectures Quantum kernels vs classical kernels Data-efficient QML Noise-resilient QML QML benchmarking 5–8 serious QML projects Implement: One paper reproduction One modification or improvement Happy Learning! Save this post for later. Repost ♻️ for Quantum & AI Learners! Check my profile for more resources on Quantum & AI Tech Follow Kiran Kaur Raina here: 📌LinkedIn: https://lnkd.in/gEpKMQ7z 📌YouTube: https://lnkd.in/gTTv2ewB 📌Topmate: https://lnkd.in/gDj-kmYW 📌Medium: https://lnkd.in/gWBppT7G 📌Instagram: https://lnkd.in/g8qZKHe7

IBM to Launch the Largest Quantum Computer Yet in 2025 Overview: IBM plans to build the largest quantum computer to date by linking multiple smaller quantum chips in parallel. The project, set for 2025, aims to shatter existing records for qubit count, marking a significant leap in quantum computing capabilities. IBM’s goal is to more than triple the size of the largest current quantum machine while advancing practical quantum computing applications. Key Details: 1. IBM’s Quantum Roadmap: • IBM’s largest current quantum chip, Condor, contains 1,121 qubits. • By 2025, IBM plans to interconnect multiple chips to exceed this number, ultimately aiming to triple the largest existing system. 2. Milestone Achievements: • The company has successfully demonstrated linking two quantum chips, a key step toward building larger, interconnected systems. • This modular approach allows IBM to scale quantum systems beyond the physical and error-correction limits of single chips. 3. Quantum Computing Use Cases: • IBM provides cloud access to its quantum systems, with most users currently utilizing about 100 qubits for practical tasks. • The expansion to larger systems will enable more complex computations in fields like drug discovery, materials science, and logistics optimization. The Significance of More Qubits: 1. Increased Computational Power: • More qubits enable quantum systems to solve problems exponentially faster than classical computers. 2. Error Correction: • Scaling qubits allows for improved quantum error correction, a critical barrier to achieving reliable quantum computations. 3. Broader Accessibility: • Larger systems will allow more researchers and industries to access practical quantum applications via IBM’s cloud platform. IBM’s Competition in Quantum Computing: 1. Atom Computing Holds Current Record: • Start-up Atom Computing currently holds the record for the largest quantum system, slightly surpassing IBM. 2. Tech Industry Quantum Race: • Competitors like Google, Rigetti, and IonQ are also racing to scale up their quantum systems. 3. IBM’s Modular Strategy: • IBM’s approach focuses on scaling through chip interconnection, which could sidestep the limitations of monolithic single-chip systems. The Takeaway: IBM’s 2025 quantum computer project aims to break new ground by creating the largest quantum system ever built, leveraging interconnected quantum chips to scale qubit counts. While significant technical challenges remain—particularly around error correction and chip interconnectivity—the initiative marks a critical step toward practical, large-scale quantum computing. With competitors like Atom Computing and Google also advancing rapidly, the race for quantum supremacy intensifies, promising transformative impacts across science, industry, and technology in the near future.

Thought you knew which #quantumcomputers were best for #quantum optimization? The latest results from Q-CTRL have reset expectations for what is possible on today's gate-model machines. Q-CTRL today announced newly published results that demonstrate a boost of more than 4X in the size of an optimization problem that can be accurately solved, and show for the first time that a utility-scale IBM quantum computer can outperform competitive annealer and trapped ion technologies. Full, correct solutions at 120+ qubit scale for classically nontrivial optimizations! Quantum optimization is one of the most promising quantum computing applications with the potential to deliver major enhancements to critical problems in transport, logistics, machine learning, and financial fraud detection. McKinsey suggests that quantum applications in logistics alone are worth over $200-500B/y by 2035 – if the quantum sector can successfully solve them. Previous third-party benchmark quantum optimization experiments have indicated that, despite their promise, gate-based quantum computers have struggled to live up to their potential because of hardware errors. In previous tests of optimization algorithms, the outputs of the gate-based quantum computers were little different than random outputs or provided modest benefits under limited circumstances. As a result, an alternative architecture known as a quantum annealer was believed – and shown in experiments – to be the preferred choice for exploring industrially relevant optimization problems. Today’s quantum computers were thought to be far away from being able to solve quantum optimization problems that matter to industry. Q-CTRL’s recent results upend this broadly accepted industry narrative by addressing the error challenge. Our methods combine innovations in the problem’s hardware execution with the company’s performance-management infrastructure software run on IBM’s utility-scale quantum computers. This combination delivered improved performance previously limited by errors with no changes to the hardware. Direct tests showed that using Q-CTRL’s novel technology, a quantum optimization problem run on a 127-qubit IBM quantum computer was up to 1,500 times more likely than an annealer to return the correct result, and over 9 times more likely to achieve the correct result than previously published work using trapped ions These results enable quantum optimization algorithms to more consistently find the correct solution to a range of challenging optimization problems at larger scales than ever before. Check out the technical manuscript! https://lnkd.in/gRYAFsRt

As AI+Science went more mainstream in 2025, our team’s seminal contributions to the field are getting wide recognition. Here are top professional contributions and achievements for 2025. 1. Medical Imaging: We applied Neural Operators as a universal AI scheme that can handle any subsampling scheme and can do zero-shot super-resolution and field of view, without the need for any retraining. We applied it to a range of modalities such as MRI, CT, ultrasound, and photo-acoustic imaging. 2. AI Weather and Climate Models: I led the creation of the first AI-based weather model FourCastNet, built on Neural Operators, back in 2021. This year we announced FourCastNet 3, the fastest AI based model to provide calibrated probabilistic answers, crucial for extreme weather events. This also serves as backbone for state of art AI-based climate models . 3. De-Novo Inverse Design of Physical Devices: We were able to design new devices that were previously out of reach in challenging systems such as gate design in quantum dots, controlling quantum systems and non-linear photonics, using Fourier Neural Operator (FNO). 4. Scientific Modeling: FNOs achieved modeling of bio-realistic neurons, quantum dynamics and black holes with significant speedups while maintaining fidelity. 5. Millennium prize in fluid dynamics: We developed high-precision physics-informed neural networks (PINN) to solve a key step in computer-assisted proofs of singularities. 6. Physics-informed chemistry: Orbitall is the first universal quantum-chemical AI model that can handle at any spin, charge and external fields, and can extrapolate to larger molecules than those in training data. Nucleusdiff improves structure-based drug design by generating physically plausible molecules that maintain proper atomic spacing. We were able to beat previously known natural and engineered enzymes in functionality and versatility using protein-language models (genSLM). 7. Neural Operator foundations: We developed a unified framework to convert many popular neural networks like convolutional and graph neural networks, transformers etc to Neural Operators. We improved generalization to different geometries and scales. We developed FunDPS, a diffusion based inverse problem solver on function spaces. It is a resolution-agnostic unified framework for both forward and inverse PDEs. We also established limitations of hybrid learning that combine numerical solvers with learned closures and superiority of operator learning. 8. Verified Learning in LLMs: We released LeanDojo v2, LeanAgent and LeanProgress for theorem proving. 9. TIME 100 Impact Award and IEEE Kiyo Tomiyasu Award. 10. Group members Zongyi Li and Miguel Liu-Schiaffini winning best graduate and undergraduate research at Caltech commencement for work on Neural Operators and alum Zhuoran Qiao winning the Tianqiao and Chrissy Chen Institute AI+Science prize.

🗞️ Needed report By CyberArk on a burning issue : identity security. A decisive element that will determine our ability to restore digital trust. 🔹 « Identity is now the primary attack surface. » Defenders must secure every identity — human and machine 🔹 with dynamic privilege controls, automation, and AI-enhanced monitoring 🔹and prepare now for LLM abuse and quantum disruption. Machine identities are the fastest-growing attack surface 🔹Growth outpaces human identities 45:1. 🔹Nearly half of machine identities access sensitive data, yet 2/3of organizations don’t treat them as privileged. Quantum readiness is urgent 🔹Quantum computing will break today’s cryptography (RSA, TLS, identity tokens). 🔹Transition planning to quantum-safe algorithms must start now, even before standards are finalized. Large Language Models include prompt injection, data leakage, and misuse of AI agents. So organizations must treat them as a new class of machine identity requiring monitoring, access controls, and secrets management. 🧰 What can we do? ⚒️ 1/ Implement Zero Standing Privileges (ZSP) • Remove always-on entitlements; grant access dynamically and just-in-time. • Minimize lateral movement by revoking privileges once tasks are complete 👥2/ Secure the full spectrum of identities • Differentiate controls for workforce, IT, developers, and machines. • Prioritize machine identities: vault credentials, rotate secrets, and eliminate hard-coded keys. 🛡️ 3/ Embed intelligent privilege controls • Apply session protection, isolation, and monitoring to high-risk access. • Enforce least privilege on endpoints; block or sandbox unknown apps. • Deploy Identity Threat Detection & Response (ITDR) for continuous monitoring. ♻️ 4/ Automate identity lifecycle management • Use orchestration to onboard, provision, rotate, and deprovision identities at scale. • Relieve staff from manual tasks, counter skill shortages, and improve compliance readiness. 5/ Align security with business and regulatory drivers • Build an “identity fabric” across IAM, PAM, cloud, SaaS, and compliance. • Tie metrics (KPIs, ROI, cyber insurance conditions) to board-level priorities. 6/ Prepare for next-generation threats • Establish AI/LLM security policies: control access, monitor usage, audit logs. • Begin phased adoption of post-quantum cryptography to protect long-lived sensitive data. Enjoy the read

Bank for International Settlements – BIS has published "Quantum-readiness for the financial system: a roadmap" The document counts with well-known experts as co-authors: Raphael Auer, Andras Valko (BIS), Angela Dupont (BIS and Banque de France), Maryam Haghighi, Danica Marsden (Bank of Canada), Sarah McCarthy (University of Waterloo) , Donna F. Dodson, and Nicolas Margaine. It provides a comprehensive overview of the #QuantumSafety topic and how it applies to the financial sector systemically and to financial organizations individually. It is useful and insightful, including the most mature thought leadership. Some highlights on general messages: 👉 Trust in the financial system is fundamentally tied to the trust provided by cryptography. 👉 Implementation challenges require coordinated planning and bring an opportunity to build more resilient infrastuctures. 👉 In line with the Canadian roadmap, it emphasizes implementing robust governance structures. 👉 It recommends the implemantation of crypto-agility, understood as per the definition created by the FS-ISAC PQC WG (https://lnkd.in/dgzW_rn8). On "A systemic roadmap": 👉 The document underlines the need for a coordinated and proactive action plan by central banks, supervisory authorities and financial institutions around the world. 👉 Warns about the risk of dual-speed transitions: "In the absence of coordination, actors that are not adequately protected against the quantum threat could become weak links, impacting the security of the entire financial system." 👉 While not suggesting a timeline, the document calls for global alignment: "During the planning phase participants in the financial system translate the jointly agreed priorities and requirements into a system-level migration timeline and a set of common technical choices". 👉 It also recommends protections against the doom of backwards compatibility: "a cut-off date for phasing out legacy cryptographic protocols needs to be approved by all organisations that use those protocols". 👉 And covers the importance of cross-border alignment: "domestic plans need to be aligned with transition plans in other jurisdictions and in cross-border systems, such as multi-currency payment and settlement infrastructures". On organizations' roadmaps: 👉 Underlines the need to appoint an executive leader responsible for driving the programme. 👉 "Forming a dedicated, cross-functional team is essential in this initial phase. This team should include representatives from technology, legal, human resources, finance, operations and security departments". On responsibilities: 👉 "Central banks, as pivotal entities in the global financial system, are well positioned to support and lead the way to increased resilience. [...] Central banks can promote a proactive, systemic approach and help create the alignment necessary for coordinated action across the global financial system". https://lnkd.in/dU4fS4TX