Quantum Computing Applications

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

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

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.

History was made this week in financial markets. HSBC, Europe’s largest bank, has proven that quantum isn’t just theory...it’s a powerful competitive advantage. In partnership with IBM, HSBC’s quantum pilot delivered a 34% improvement in predicting bond trade fill rates at quoted prices. In markets where milliseconds move billions, that edge is transformative. By combining quantum and classical computing, HSBC tackled complex pricing algorithms that factor in real-time market conditions and risks. Philip Intallura, HSBC’s Group Head of Quantum Technologies, explained: “It means we now have a tangible example of how today’s quantum computers could solve a real-world business problem at scale.” Why it matters: • Quantum computing is projected to become a $100B market within a decade (McKinsey). • Finance is the proving ground where nanoseconds and probabilities drive outcomes. • HSBC just demonstrated how quantum can deliver measurable results today. Quantum is still in its early stages, but breakthroughs like this set the benchmarks for what comes next. Which industry do you think will unlock the first trillion-dollar quantum advantage? #QuantumComputing #FinancialMarkets #BondTrading #FinTech #InnovationLeadership #HSBC #IBM

Quantum Teleportation Achieved Over Internet for the First Time Researchers in the U.S. have successfully teleported a quantum state of light through over 30 kilometers (18 miles) of fiber optic cable while coexisting with regular internet traffic. This achievement marks a monumental step toward integrating quantum communication systems into existing telecommunications infrastructure, paving the way for future quantum internet networks. Key Highlights: • Teleportation Explained: Quantum teleportation involves transferring the quantum state of one particle to another distant particle, effectively replicating its state without physically moving the particle itself. • Overcoming Challenges: The experiment succeeded despite the interference from traditional internet data flowing through the same cables, showcasing an unprecedented level of stability and accuracy in a real-world environment. • Infrastructure Integration: The ability to teleport quantum states using existing fiber optic networks suggests that quantum and classical communication systems can share infrastructure, greatly reducing costs and accelerating deployment timelines. Why This Matters: • Quantum Internet Potential: Quantum networks promise ultra-secure encryption, seamless quantum computer connections, and advanced distributed sensing systems. • Real-World Feasibility: Demonstrating quantum teleportation in active fiber optic networks proves the technology can be scaled and deployed in real-world conditions. • Data Security: Quantum encryption methods, leveraging principles such as quantum key distribution (QKD), could make communications virtually unhackable. Researcher Insights: “This is incredibly exciting because nobody thought it was possible,” said Prem Kumar, a computing engineer at Northwestern University who led the study. “Our work shows a path towards next-generation quantum and classical networks sharing a unified fiber optic infrastructure. Basically, it opens the door to pushing quantum communications to the next level.” Implications for the Future: • Secure Communications: Enhanced encryption and ultra-secure networks could revolutionize cybersecurity. • Quantum Cloud Computing: Seamless connectivity between quantum computers across long distances could unlock unprecedented computational capabilities. • Scalable Deployment: Utilizing existing infrastructure minimizes costs and accelerates integration into global communication networks. While we’re still far from the Star Trek-style teleportation of physical objects, this achievement represents a profound advancement in quantum network engineering, bringing the vision of a global quantum internet significantly closer to reality.

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

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

🗞️ 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

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