Exploring Quantum Technology

Woke up today thinking about how atomic particles carry information — a shift that could redefine computing and communication. We typically think of information transfer through wires and circuits. But at the smallest scales, individual particles — photons, electrons, even atoms — are changing how things could work. 1 / Qubits in Quantum Computing In quantum systems, particles like photons and electrons store information as qubits. Unlike traditional bits, qubits use superposition and entanglement to process certain problems exponentially faster, transforming fields like cryptography and complex optimization. 2 / Photonic Communication (bullish here) Photons transmit data in fiber optics, but in quantum communication, single photons enable secure data transfer. Quantum key distribution (QKD) leverages photons to detect interception attempts, creating highly secure networks. 3 / Spintronics for Data Storage Electron spin, rather than charge, is used in spintronics, leading to faster, energy-efficient storage technologies like MRAM. This approach could revolutionize data density and durability, key for next-gen devices. 4 / Atomic Computing At the experimental edge, atoms themselves are being explored as data carriers. Single-atom transistors demonstrate the potential for ultra-compact processing power, hinting at a new frontier in computing miniaturization. Atomic-scale information transfer is reshaping tech—moving us beyond circuits to a new paradigm where particles drive performance. Thoughts?

🚨 New OMB Report on Post-Quantum Cryptography (PQC)🚨 The Office of Management and Budget (OMB) has released a critical report detailing the strategy for migrating federal information systems to Post-Quantum Cryptography. This report is in response to the growing threat posed by the potential future capabilities of quantum computers to break existing cryptographic systems. **Key Points from the Report:** 🔑 **Start Migration Early**: The report emphasizes the need to begin migration to PQC before quantum computers capable of breaking current encryption become operational. This proactive approach is essential to mitigate risks associated with "record-now-decrypt-later" attacks. 🔑 **Focus on High-Impact Systems**: Priority should be given to high-impact systems and high-value assets. Ensuring these critical components are secure is paramount. 🔑 **Identify Early**: It's crucial to identify systems that cannot support PQC early in the process. This allows for timely planning and avoids migration delays. 🔑 **Cost Estimates**: The estimated cost for this transition is approximately $7.1 billion over the period from 2025 to 2035. This significant investment underscores the scale and importance of the task. 🔑 **Cryptographic Module Validation Program (CMVP)**: To ensure the proper implementation of PQC, the CMVP will play a vital role. This program will validate that the new cryptographic modules meet the necessary standards. The full report outlines a comprehensive strategy and underscores the federal government’s commitment to maintaining robust cybersecurity in the quantum computing era. This is a critical step in safeguarding our digital infrastructure against future threats. #Cybersecurity #PQC #QuantumComputing #FederalGovernment #Cryptography #DigitalSecurity #OMB #NIST

World-First Molecular Quantum Entanglement Achieved at Durham University In a groundbreaking achievement, scientists at Durham University in the UK have successfully demonstrated quantum entanglement of molecules with a record-breaking fidelity of 92%. This marks the first time entanglement has been achieved with molecules, advancing quantum mechanics research and opening doors to revolutionary technologies in communication, sensing, and computing. Key Highlights: 1. Quantum Entanglement Basics: Quantum entanglement links particles such that the state of one influences the other, regardless of distance. This phenomenon is a cornerstone for developing next-generation quantum technologies, enabling faster communication and enhanced computational power. 2. ‘Magic-Wavelength’ Optical Tweezers: The team utilized highly precise optical traps known as magic-wavelength optical tweezers to create environments supporting long-lasting molecular entanglement. These advanced tools allowed for stable control and manipulation of molecular states. 3. Applications: • Quantum Networking: Entanglement over existing fiber optic cables could accelerate the real-world deployment of quantum networks without requiring extensive new infrastructure. • Quantum Computing and Sensing: Molecules, with their complex internal structures, offer new dimensions for computation and precision sensing, potentially surpassing the capabilities of entangled atoms. 4. Major Milestone: While entanglement between atoms has been repeatedly demonstrated, molecules bring added complexity due to their additional internal structures. Achieving high-fidelity entanglement with molecules is a significant step forward in the field. Implications for the Future: This breakthrough could lead to advancements in secure communication, more powerful quantum computers, and sophisticated sensing technologies. As quantum entanglement becomes more applicable to real-world systems, innovations like this set the stage for transformative developments in science and technology.

Three weeks ago, our Devsinc security architect, walked into my office with a chilling demonstration. Using quantum simulation software, she showed how RSA-2048 encryption – the same standard protecting billions of transactions daily – could theoretically be cracked in just 24 hours by a sufficiently powerful quantum computer. What took her classical computer billions of years to attempt, quantum algorithms could solve before tomorrow's sunrise. That moment crystallized a truth I've been grappling with: we're not just approaching a technological evolution; we're racing toward a cryptographic apocalypse. The quantum computing market tells a story of inevitable disruption, surging from $1.44 billion in 2025 to an expected $16.22 billion by 2034 – a staggering 30.88% CAGR that signals more than market enthusiasm. Research shows a 17-34% probability that cryptographically relevant quantum computers will exist by 2034, climbing to 79% by 2044. But here's what keeps me awake at night: adversaries are already employing "harvest now, decrypt later" strategies, collecting our encrypted data today to unlock tomorrow. For my fellow CTOs and CIOs: the U.S. National Security Memorandum 10 mandates full migration to post-quantum cryptography by 2035, with some agencies required to transition by 2030. This isn't optional. Ninety-five percent of cybersecurity experts rate quantum's threat to current systems as "very high," yet only 25% of organizations are actively addressing this in their risk management strategies. To the brilliant minds entering our industry: this represents the greatest cybersecurity challenge and opportunity of our generation. While quantum computing promises revolutionary advances in drug discovery, optimization, and AI, it simultaneously threatens the cryptographic foundation of our digital world. The demand for quantum-safe solutions will create entirely new career paths and industries. What moves me most is the democratizing potential of this challenge. Whether you're building solutions in Silicon Valley or Lahore, the quantum threat affects us all equally – and so does the opportunity to solve it. Post-quantum cryptography isn't just about surviving disruption; it's about architecting the secure digital infrastructure that will power humanity's next chapter. The countdown has begun. The question isn't whether quantum will break our current security – it's whether we'll be ready when it does.

Why can’t we scale superconducting qubits like transistors? Qubits, even those on the same chip or wafer, often show big frequency variations. Here’s the thing: qubit frequency is directly tied to the Josephson Junction (JJ), the core circuit component in superconducting qubits. And while we’ve mastered transistor fabrication at nanometer precision, JJs remain a challenge. Why? Turns out, the issue isn’t what you’d expect. It’s something rarely discussed: 𝗚𝗿𝗮𝗶𝗻 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗚𝗿𝗼𝗼𝘃𝗶𝗻𝗴. A Josephson Junction is a trilayer (Al-AlOx-Al), typically made by oxidizing the bottom aluminum layer before depositing the top one. The problem is that aluminum grains form grooves at their boundaries. The oxide layer inherits this roughness, leading to an uneven thickness across the barrier. And that’s where the chaos begins. Because the barrier thickness impacts the critical current, which in turn dictates the qubit frequency. Even tiny variations in the AlOx barrier have a big impact on hitting target frequencies. 𝗦𝗼, 𝗵𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗳𝗶𝘅 𝗶𝘁? We have quite few levers to pull. For instance, • 𝗙𝗹𝘂𝘅 𝗧𝘂𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 We design the qubit as a SQUID loop to tune the frequency using magnetic flux. This has become the state-of-the-art architecture, however it adds to the wiring overhead (one line per qubit). • 𝗣𝗼𝘀𝘁-𝗙𝗮𝗯𝗿𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗶𝗺𝗺𝗶𝗻𝗴 We can use techniques like Laser Annealing to permanently trim the junction resistance 𝘢𝘧𝘵𝘦𝘳 fabrication. This allows us to "edit" qubits to the hit their target frequency.    • 𝗕𝗲𝘁𝘁𝗲𝗿 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 The field is relentlessly trying to improve the hardware stack. One example is growing epitaxial aluminum films. It’s the superior physical solution, but currently expensive and difficult to integrate into standard fabrication workflows. What are you doing to improve qubit reproducibility ? Applied Materials imec Quantum Foundry Copenhagen IQM Quantum Computers Infineon Technologies Intel Foundry TSMC

The most important thing about the U.S. government's $2 billion quantum announcement may not be who received the money. It may be what they were paid to fix. Last month, the U.S. government published one of the clearest maps yet of where quantum computing actually breaks — not through a technical roadmap, but through nine letters of intent proposing $2.013 billion in federal incentives. Read the scope attached to each company, and this stops looking like a list of winners. It starts looking like a government-authored diagnosis of the engineering gaps between a laboratory device and a manufacturable quantum system. Seven of the nine are quantum computing companies. Here is what each was asked to solve: D-Wave: dielectric materials, interface control, and advanced packaging. Rigetti Computing: integrated readout electronics and next-generation cryostat architectures. Atom Computing: the hardware and systems integration required to control tens of thousands of neutral-atom qubits. PsiQuantum: electro-optic materials, single-photon detectors, and ultra-low-loss photonic packaging. Quantinuum: low-loss integrated photonics and reliable optical components at trapped-ion wavelengths. Diraq: scalable, reliable silicon-spin qubit arrays and their manufacturing integration. Infleqtion: high-power optical systems, readout, error correction, and large-scale neutral-atom integration. The pattern matters. These proposed investments are not primarily searching for a new qubit modality or another laboratory demonstration. They are aimed at reproducibility, yield, control, readout, packaging, interconnects, and systems integration. The bottleneck has not moved away from physics. It has expanded beyond physics. The central question is no longer only, "Can a qubit work?" It is, "Can thousands — or eventually millions — of devices be fabricated, connected, controlled, and operated with sufficiently consistent performance?" Taken together, these seven bets map the bottlenecks closest to the processor. The other two recipients — IBM and GlobalFoundries — were paid to build the foundry layer underneath. That layer is where the real structural question lives. Next. Views are my own

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.

Isolating fragile quantum states relies on specific mathematical boundaries. Scaling quantum hardware involves eliminating correlations between a local system and its surrounding environment. When a bipartite quantum state undergoes a unitary operation followed by a decoupling map, the objective is to make the resulting system independent of environmental noise. Past approaches to calculate decoupling error limits relied on approximations and smoothing techniques. A joint research initiative between RWTH Aachen University and National Taiwan University introduces a one-shot decoupling theorem. This study defines the decoupling error bound through exact mathematical structures rather than general estimations. The research was conducted by Mario Berta, Yongsheng Yao, and Hao-Chung Cheng. Consider the technical parameters of this published theorem: → It utilizes quantum relative entropy distance instead of the standard trace distance criteria. → It provides a precise characterisation of one-shot decoupling error without using smoothing techniques or additive terms. → It delivers a single-letter expression for exact error exponents in quantum state merging. → It outlines achievability bounds for entanglement distillation assisted by local operations and classical communication. These mathematical limits apply directly to system performance. For coding rates below the first-order asymptotic capacity, the error decays exponentially for every blocklength. This provides a large-deviation characterisation that is mathematically stronger than conventional first-order approaches. Relative entropy operates as the primary metric for defining the capacity of these operational tasks. The bounds formulated under relative entropy convert directly into purified distance statements via standard entropy-fidelity inequalities. This establishes a strict performance criterion for applications like quantum channel simulation and secure channel coding. The current theorem primarily addresses scenarios involving identical, independently distributed quantum states. The subsequent phase of research requires applying these refined entropy bounds to complex systems featuring correlated noise and memory. This research supplies experimental physicists with a defined mathematical framework for future quantum architecture. How do you evaluate the transition from theoretical limits to functional quantum hardware? Reply in the comments.

To understand real momentum in #quantum, compare the last 2–3 years, not the last 2–3 months. The progress is inspiring, but are we close to any kind of inflection point? 𝗪𝗵𝗲𝗻 Richard Givhan 𝗮𝗻𝗱 𝗜  𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗛𝗮𝗶𝗾𝘂 𝗶𝗻 𝗲𝗮𝗿𝗹𝘆 𝟮𝟬𝟮𝟯 𝗶𝗻 Creative Destruction Lab, 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘄𝗮𝘀 𝗹𝗮𝗿𝗴𝗲𝗹𝘆 𝗮 𝘄𝗼𝗿𝗹𝗱 𝗼𝗳 𝘁𝗼𝘆-𝘀𝗰𝗮𝗹𝗲 𝗽𝗿𝗼𝗼𝗳𝘀 𝗼𝗳 𝗰𝗼𝗻𝗰𝗲𝗽𝘁: few-qubit algorithms on simulators, and “real hardware” demos (often limited by tens-of-qubits devices and unstable performance) where the goal was to confirm the ability to extract any signal in the noise rather than solving anything practical. 𝗔 𝗳𝗲𝘄 𝗿𝗲𝗮𝗹-𝗹𝗶𝗳𝗲 𝗮𝗻𝗲𝗰𝗱𝗼𝘁𝗲𝘀 𝗼𝗳 𝘁𝗵𝗮𝘁 𝘁𝗶𝗺𝗲. In one of our early benchmarks, a publicly available QPU produced nearly random noise with no signs of declared performance specs. As we later learned, the device's cooling system was broken, causing significant thermal noise. On another public device, the algorithm's fidelity fluctuated 2x between calibration cycles. 𝗧𝗵𝗲 𝗼𝘂𝘁𝗹𝗼𝗼𝗸 𝗳𝗼𝗿 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗳𝗲𝗹𝘁... 𝗱𝗶𝘀𝘁𝗮𝗻𝘁. At the same time, some of the one-off “quantum supremacy” experiments were already hinting at a different path. Even on these noisy QPUs, very shallow circuits can create entangled states that are hard to reproduce classically. The obvious question is: can such states be utilised for any useful computation, without the need for handcrafted deep circuits that hardware noise destroys? This reminds me of early #perception #AI systems: millions of lines of handcrafted logic in computer vision or signal processing were replaced by comparatively “shallow” neural nets - once the right training infrastructure and software stack emerged. ⏩ 𝗜𝗻 𝗷𝘂𝘀𝘁 𝗮 𝗰𝗼𝘂𝗽𝗹𝗲 𝗼𝗳 𝘆𝗲𝗮𝗿𝘀: 𝟭𝟬𝟬+ 𝗾𝘂𝗯𝗶𝘁 𝗱𝗲𝘃𝗶𝗰𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗿𝗼𝘂𝘁𝗶𝗻𝗲𝗹𝘆 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 (big thanks to IBM Quantum for this move), and 𝘄𝗲’𝘃𝗲 𝘀𝗲𝗲𝗻 𝗮 𝘀𝘂𝗿𝗴𝗲 𝗼𝗳 𝗹𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝗽𝗵𝘆𝘀𝗶𝗰𝘀, 𝗰𝗵𝗲𝗺𝗶𝘀𝘁𝗿𝘆, 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, etc. Many of these applications are heuristic and shallow-circuit by design. However, running a reliable experiment at utility-scale is still hard. Reproducibility, noise, calibration, and cost still limit quantum runs at that scale. That’s the gap we’re closing at Haiqu - 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗼𝗻 𝗤𝗣𝗨𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲, 𝗯𝘂𝗱𝗴𝗲𝘁𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘄𝗶𝘁𝗵 𝗮 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 𝗵𝗶𝗴𝗵 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. We’re doubling down on making this capability accessible to many more researchers and engineers. Even if reliable quantum hardware appears tomorrow, applications for broad commercial adoption need to be discovered. The inflection point is when prototyping becomes fast and cheap enough to validate practical use cases at scale.