Quantum Computing Developments

A couple reflections on the quantum computing breakthrough we just announced... Most of us grew up learning there are three main types of matter that matter: solid, liquid, and gas. Today, that changed. After a nearly 20 year pursuit, we’ve created an entirely new state of matter, unlocked by a new class of materials, topoconductors, that enable a fundamental leap in computing. It powers Majorana 1, the first quantum processing unit built on a topological core. We believe this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years. The qubits created with topoconductors are faster, more reliable, and smaller. They are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor. Imagine a chip that can fit in the palm of your hand yet is capable of solving problems that even all the computers on Earth today combined could not! Sometimes researchers have to work on things for decades to make progress possible. It takes patience and persistence to have big impact in the world. And I am glad we get the opportunity to do just that at Microsoft. This is our focus: When productivity rises, economies grow faster, benefiting every sector and every corner of the globe. It’s not about hyping tech; it’s about building technology that truly serves the world. Read more about our discovery, and why it matters, here: https://aka.ms/AAu76rr

Breaking Quantum News: Real algorithms, real data, real quantum machines HSBC, in partnership with IBM, has delivered the world’s first quantum-enabled algorithmic trading trial. Using live, production-scale data from the European corporate bond market, HSBC integrated IBM’s quantum processors with classical systems—achieving up to a 34% improvement in predicting the probability of winning trades compared with classical methods alone. Why it matters: - Bond trading is one of the most complex, data-heavy challenges in finance. - Classical models struggle to capture hidden pricing signals in noisy markets. - By augmenting workflows with IBM Quantum Heron, HSBC uncovered insights classical systems could not. As Philip Intallura Ph.D, HSBC’s Global Head of Quantum Technologies, put it: “This is a tangible example of how today’s quantum computers could solve a real-world business problem at scale and offer a competitive edge.” And as IBM’s Jay Gambetta emphasized: breakthroughs come from combining deep financial expertise with cutting-edge quantum algorithms—demonstrating what becomes possible as quantum advances. This is not hype. It’s not distant. Quantum is entering the market—today. #QuantumComputing #Finance #Innovation #PQC #QuantumReady

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜, 𝗟𝗟𝗠𝘀, 𝗮𝗻𝗱 𝗚𝗣𝗨𝘀 𝘁𝗵𝗲𝘀𝗲 𝗱𝗮𝘆𝘀! But there’s another technology quietly advancing — one that could make today’s AI systems look primitive: 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴. Last week, IBM revealed its roadmap to build the world’s first large-scale, fault-tolerant quantum computer — IBM Quantum Starling — targeted for delivery by 2029. This system is designed to perform 100 million quantum operations using 200 logical qubits, scaling far beyond current quantum machines. To represent its quantum state would require **more memory than 10⁴⁸ classical supercomputers combined*. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝘀𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀? ⬇️ - Quantum computers use qubits, which can represent multiple states at once — enabling exponential computational power. - They have the potential to transform industries like drug development, materials discovery, and optimization. - At the same time, their power threatens to break current encryption protocols, prompting urgent work on quantum-safe security. - The field is still experimental, requiring extreme conditions like temperatures close to absolute zero — but the trajectory is clear. 𝗜𝗕𝗠’𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗶𝘀 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝗻 𝗿𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: ⬇️ It’s building toward fault-tolerant quantum computing through a stepwise hardware roadmap: 1. Loon (2025) will test new chip components for error correction using quantum LDPC codes — the foundation of scalable quantum computing. 2. Kookaburra (2026) introduces IBM’s first modular quantum processor, combining memory and logic to build systems beyond a single chip. 3.Cockatoo (2027) will entangle multiple Kookaburra modules, connecting chips like nodes in a distributed quantum system. All of this leads to Starling (2029) — IBM’s planned breakthrough system capable of running 100 million quantum operations on 200 logical qubits. These are tightly integrated hardware milestones — solving problems like error correction, interconnects, and scalability — that make large-scale quantum computing actually achievable. 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝘃𝗶𝗱𝗲𝗼 𝗯𝗲𝗹𝗼𝘄 𝘁𝗼 𝘀𝗲𝗲 𝗵𝗼𝘄 𝘁𝗵𝗶𝘀 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝘂𝗻𝗳𝗼𝗹𝗱𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝘆 𝘁𝗵𝗶𝘀 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲𝗰𝗼𝗺𝗲 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗺𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗱𝗲𝗰𝗮𝗱𝗲.

Yesterday, Google announced it had achieved something called a “verifiable quantum advantage”. The announcement might sound like marketing mush but it’s not. It represents one of the most interesting inflection points in the story of computing since the transistor. For decades, the dream of quantum computing has dangled like science fiction: machines that use the strange rules of quantum mechanics to solve problems that would take supercomputers millennia. In 2019, Google claimed quantum supremacy - meaning their quantum computer solved a problem no classical computer could feasibly do in a reasonable timeframe. But that problem was a glorified dice roll: random number sampling. A proof of principle, not of purpose Their latest claim - quantum advantage - goes further. It says a quantum machine has outperformed the best classical algorithms on a task that’s scientifically meaningful. In their experiment, Google’s Willow processor, a 105-qubit superconducting chip, ran an algorithm called Quantum Echoes to model how information spreads and decoheres - essentially, how order unravels into chaos inside quantum systems. That’s the kind of math that underpins chemistry, materials science, and condensed-matter physics. Willow completed the task 13,000x faster than the world’s best supercomputers, while remaining verifiable - that is, its output could be independently checked. In other words, the machine wasn’t playing a party trick anymore; it was doing science. Every era of computing begins with a strange, narrow demo that later looks obvious in hindsight. ➰ The Wright brothers’ first flight lasted 12 seconds - not exactly air travel. ➰ The first transistor amplified a single signal - not exactly an iPhone. ➰ The first webpage looked like a grocery list - not exactly the internet. Google’s quantum milestone feels the same. A narrow, technical victory that, decades later, we’ll point to and say: that’s when the impossible started to feel inevitable. Of course, the hype shouldn’t outrun the hardware. Quantum systems face 3 towering challenges: ▪️ Error correction: Qubits are noisy - one stray photon can flip a bit of reality. ▪️ Scalability: Doubling qubits isn’t like doubling transistors; coherence decays exponentially. ▪️ Integration: Quantum systems must coexist with classical infrastructure - data movement, cooling, algorithms, verification. For now, the near horizon is hybrid quantum-classical computing, where quantum processors handle intractable subproblems inside classical workflows. For the past 80 years, computing has been about logic - zeros and ones manipulating symbols. Quantum computing is about reality itself: entanglement, superposition, uncertainty. It represents a paradigm where the map is the territory - where we use the universe’s own rules to understand the universe. In that sense, the shift from quantum supremacy to advantage mirrors the shift from theory to instrument - from “it works” to “it works for us.”

Many of you will have seen the news about HSBC’s world-first application of quantum computing in algorithmic bond trading. Today, I’d like to highlight the technical paper that explains the research behind this milestone. In collaboration with IBM, our teams investigated how quantum feature maps can enhance statistical learning methods for predicting the likelihood that a trade is filled at a quoted price in the European corporate bond market. Using production-scale, real trading data, we ran quantum circuits on IBM quantum computers to generate transformed data representations. These were then used as inputs to established models including logistic regression, gradient boosting, random forest, and neural networks. The results: • Up to 34% improvement in predictive performance over classical baselines. • Demonstrated on real, production-scale trading data, not synthetic datasets. • Evidence that quantum-enhanced feature representations can capture complex market patterns beyond those typically learned by classical-only methods. This marks the first known application of quantum-enhanced statistical learning in algorithmic trading. For full technical details please see our published paper: 📄 Technical paper: https://lnkd.in/eKBqs3Y7 📰 Press release: https://lnkd.in/euMRbbJG Congratulations to Philip Intallura Ph.D , Joshua Freeland Freeland and all HSBC colleagues involved — and huge thanks to IBM for their partnership.

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

🚨 **CYBERSECURITY ALERT: The Quantum Threat Just Got Real** Chinese researchers have successfully broken RSA encryption using D-Wave's quantum annealing computers - and this should alarm every business leader and cybersecurity professional. **Why this matters:** **1. RSA encryption is EVERYWHERE** - It secures virtually all internet traffic, credit card transactions, online banking, and corporate communications. When researchers say they've cracked it, they're talking about breaking the foundation of digital security as we know it. **2. This wasn't done with theoretical "future" quantum computers** - They used D-Wave's commercially available quantum annealing systems that organizations can actually purchase and deploy TODAY. As the research team noted: "This is the first time that a real quantum computer has posed a substantial threat to multiple full-scale SPN structured algorithms in use today." While they demonstrated this on a 22-bit RSA key (smaller than production systems), the methodology shows a clear path forward. The implications are staggering - if scaled up, this could render current encryption obsolete much sooner than the "10-15 years" timeline experts have been predicting. **The bottom line:** Organizations need to start planning their migration to quantum-resistant encryption NOW, not later. The quantum computing threat to cybersecurity just shifted from "someday" to "today." Post-quantum cryptography has been under development for a while but is rarely implemented at this point. We need to push it much faster. The original paper was published eight months ago but was only verified by peers recently. #Cybersecurity #QuantumComputing #RSAEncryption #InfoSec #DigitalSecurity #QuantumThreat #PQC #cybersecurity #Innovation D-Wave

Quantum computing is no longer speculative—it’s becoming an investment priority. In 2023, European quantum startups outpaced North America, raising $781 million (three times the $240 million raised in the US). Globally, quantum startups raised $2.2 billion, a massive jump from $522 million in 2019. This isn’t happening in a vacuum. Governments are fueling the momentum. The UK has committed $4.3 billion to quantum technologies, while Germany has pledged $3.7 billion. At the same time, VC interest is holding steady, even as funding dries up in other tech sectors.   Quantum technology will have a wide-reaching impact, from cybersecurity and financial modeling to drug discovery and materials science. Pharma will likely see the earliest impact (drug development and molecular simulations using quantum).   In 2022, Finnish startup Algorithmiq raised $4 million for quantum-powered drug discovery, while Paris-based Qubit Pharmaceuticals secured $17 million for molecular simulations. Another European company, Terra Quantum AG, based in Switzerland, raised $75 million to scale its quantum-as-a-service model, which has direct applications in pharma and beyond.   Big Tech is also all-in. Google, IBM, Intel Corporation, and NVIDIA are pouring resources into quantum hardware and software. Meanwhile, publicly traded quantum companies have seen their stocks surge, signaling growing institutional confidence.   At APEX Ventures, we invest in revolutionary quantum startups. We are partnered with kiutra, enabling the second quantum revolution with easy-to-use and sustainable cryogenics, and planqc, building quantum computers that store information in individual atoms.   For founders and investors, the question isn’t whether quantum will matter—it’s when. The trajectory is clear: capital is flowing, enterprise adoption is accelerating, and governments are fully committed. If AI dominated the last decade, quantum may own the next.   #Venturecapital #AI #Deeptech #Startups  Follow us at APEX Ventures and subscribe to our newsletter for exclusive content on groundbreaking Deep Tech startups:   🔗 https://t2m.io/EV2qHQuo

Today marks a historic milestone in quantum computing, as Microsoft and Quantinuum demonstrate the most reliable logical qubits on record. This breakthrough, with a logical error rate 800x better than the physical error rate, signifies a giant leap from the noisy intermediate-scale quantum (NISQ) level (Level 1 – Foundational) to Level 2 – Resilient quantum computing.   This progress is significant as logical qubits are only useful when they have a better error rate than physical qubits themselves. The number of physical qubits is a misleading metric; it’s not how many qubits, it’s how good they are and how resilient the quantum system is to errors.   Using the logical qubits we created, we were able to successfully perform multiple active syndrome extractions, which is when errors are diagnosed and corrected without destroying the logical qubits. Active syndrome extraction helps quantum computers stay reliable even when operations are imperfect.   With the promise of a hybrid supercomputing system powered by these reliable logical qubits, we’re paving the way for scientific and commercial breakthroughs that were once deemed impossible.  This achievement is a testament to the power of collaboration and the collective advancement of quantum hardware and software.   You can learn more from my post on the Official Microsoft Blog https://lnkd.in/gnDfcUV6 and the companion technical post on the Azure Quantum blog by Dennis Tom and Krysta Svore: https://lnkd.in/gMRVPG3s. #quantum #quantumcomputing #azurequantum

A new paper, now published in Nature Computational Science, introduces "Quantum Approximate Multi-Objective Optimization," a breakthrough from researchers at IBM, Los Alamos National Laboratory, and Zuse Institute Berlin. This work represents one of the most promising proposals for near-term demonstrations of quantum advantage in combinatorial optimization, with enormous relevance across industry and science: https://lnkd.in/ew7Pe2K5 Multi-objective optimization is a branch of mathematical optimization that deals with problems involving multiple often conflicting goals—e.g., constructing financial portfolios that minimize risk while maximizing returns. These problems can be extremely challenging for classical methods as the number of objective functions increases, even in cases where the single-objective version of the problem is easily solvable. The study demonstrates how quantum computers can approximate the optimal Pareto front, i.e., the set of all optimal trade-offs between conflicting objectives, showing better scaling than classical algorithms. Sampling good solutions from vast solution spaces is a task at which quantum computers excel, and the researchers take full advantage of that in their work. This marks an important step toward practical quantum advantage in optimization, and shows the value of exploring quantum capabilities beyond conventional problem classes. The paper is the latest outcome from our quantum optimization technical working group, and I encourage you to have a look.