Quantum Correlation Use Cases in Technology

❓ Ever wondered how Neural Networks (NNs) could revolutionize #quantum research? #NeuralNetworks aren't just transforming #AI —they're also pivotal in the quantum realm! In the work entitled "Parameter Estimation by Learning Quantum Correlations in Continuous Photon-Counting Data Using Neural Networks." Quantinuum proudly collaborated with global partners, such as the Universidad Autónoma de Madrid, Chalmers University of Technology, and the University of Michigan, uniting expertise from every corner of the world. 🌍 https://lnkd.in/gj8qttdN 🔍 Key Findings: 1️⃣ The study introduces a novel inference method employing artificial neural networks for quantum probe parameter estimation. 2️⃣ This method leverages quantum correlations in discrete photon-counting data, offering a fresh perspective compared to existing techniques focusing on diffusive signals. 3️⃣ The approach achieves performance on par with Bayesian inference - renowned for its optimal information retrieval capability - yet does so at a fraction of the computational cost. 4️⃣ Beyond efficiency, the method stands robust against imperfections in measurement and training data. 5️⃣ Potential applications span from quantum sensing and imaging to precise calibration tasks in laboratory setups. 🤔 Curious About the Unknowns? The authors are sharing EVERYTHING on Zenodo! 🎉 The codes used to generate these results, including the proposed NN architectures as TensorFlow models, are available here https://lnkd.in/gVdzJycM as well as all the data necessary to reproduce the results openly available here: https://lnkd.in/gVdzJycM Enrico Rinaldi, Manuel González Lastre, Sergio Garcia Herreros, Shahnawaz Ahmed, Maryam Khanahmadi, Franco Nori, and Carlos Sánchez Muñoz

“We make cars. What could quantum possibly do for us?” a representative from a major car company asked me this week. “And besides,” they added, “we already use AI — so we’re probably covered.” Fair question. And no, quantum won’t make trucks teleport (ever). But it will reshape how cars are designed, produced, powered, and maintained — often together with #AI. In fact, companies like Volkswagen Group, Mercedes-Benz AG, and Porsche AG are already exploring quantum use cases today: ⚡ Battery breakthroughs - car manufacturers are working with companies developing quantum hardware to simulate lithium-sulfur battery materials using #QuantumComputing. The idea is to improve charge capacity, energy density, and battery life for electric vehicles. ⚡ ⚡ Production optimization - another use case is to apply quantum to simulate welding and other processes, identifying potential defects before they happen on the factory floor. And this is just the beginning. Let’s unpack how quantum will act as a force multiplier for AI — especially in industrial sectors like automotive, logistics, and mobility: 🔹 Faster training of AI models Training large models for autonomous driving or fleet management takes serious compute. Quantum computing could speed up complex math operations in deep learning — shaving training time from months to days. 🔹 Smarter supply chain optimization Quantum algorithms like QAOA could help AI find faster, better solutions to complex problems like routing, scheduling, and resource allocation — critical in global automotive supply chains. 🔹 Next-gen R&D simulations AI + quantum chemistry = a leap in simulating materials, structures, and battery components, before building anything physical. That means faster, smarter innovation. 🔹 Safer autonomy through better NLP Vehicle perception systems rely on understanding nuance and context. Quantum-enhanced NLP may help AI interpret rare edge cases more accurately — a big win for autonomous driving safety. 🔹 Richer data analytics Quantum machine learning could unlock insights from massive, high-dimensional datasets — from predictive maintenance to customer behavior modeling. Bottom line? Quantum won’t replace AI. But it will unlock a new scale of possibility. We’re moving from “maybe someday” to “what can we pilot now?” And those who start early — even with hybrid quantum-classical approaches — will build real strategic advantage. Curious what you think: 👉 Where do you see quantum enhancing AI in your industry? Let’s exchange ideas, in comments below!

The Future of MRI: What Happens When Quantum Computing Meets Medical Imaging? Google’s launch of its first quantum computer chip opens up a completely new frontier for MRI technology. Imagine combining quantum mechanics with advanced imaging—what we could achieve is nothing short of revolutionary. Let’s explore how quantum computing could reshape MRI as we know it, pushing boundaries in resolution, speed, and accessibility. Quantum-Enhanced MRI: A Concept Picture an MRI sequence designed with quantum principles like entanglement and superposition at its core: Entangled Spin States: Instead of traditional RF pulses, quantum algorithms would entangle nuclear spins in tissue, creating a shared quantum state. This massively amplifies signal sensitivity, especially for detecting rare biomarkers or low-concentration metabolites. Superposition for Encoding: Quantum superposition could encode spatial information (X, Y, Z) simultaneously, slashing scan times by reducing the need for multiple gradient applications. Spin Squeezing: By manipulating quantum uncertainty, we could reduce noise in one dimension while enhancing signal precision in another—perfect for ultra-high-resolution imaging. Quantum Feedback Loops: Real-time quantum computation could dynamically optimize the magnetic field, compensating for patient motion or scanner imperfections on the fly. Possible Scenarios for the Future of MRI Ultra-High-Resolution Imaging: Quantum computing could refine MRI to image at the cellular or molecular level, potentially visualizing structures like individual proteins or mapping brain networks in unprecedented detail. Use Case: Detecting diseases like Alzheimer’s years before symptoms appear. Faster, Real-Time Scans: With quantum-enhanced processing, MRIs could achieve real-time imaging. Motion artifacts would become irrelevant, and scanning entire organs could take seconds instead of minutes. Use Case: Emergency cardiac imaging or dynamic tracking of blood flow. Improved Sensitivity for Early Detection: Quantum sensors could enable detection of weak magnetic resonance signals, helping diagnose early-stage cancers or rare diseases. Non-proton imaging (e.g., sodium or phosphorus) might even become routine. Use Case: Identifying cancers or metabolic changes long before they’re visible in conventional scans. Portable, Affordable MRI Systems: Quantum computing could lead to more compact hardware designs and cheaper magnets, enabling portable systems for underserved areas. Use Case: Scalable solutions for remote or low-resource settings. Hybrid Imaging: Quantum computing could make it easier to integrate MRI with other modalities like PET or spectroscopy, creating multi-functional devices capable of both structural and metabolic imaging. Use Case: Simultaneously visualizing tumor structure and activity in cancer research. #QuantumComputing #MRI #MedicalImaging #HealthcareInnovation #FutureTech 4o

No, We Cannot Use Quantum To Communicate Faster Than Light. Quantum entanglement is the most seductive ghost story in our industry. The logic seems obvious: two particles measured far apart show instant correlations, so surely we can use them to send data instantly? We cannot. The physics is absolute: Entanglement offers correlation, not communication. You cannot control the measurement, so you cannot encode a message. Einstein’s speed limit remains unbroken. (In short: You can teleport a state, but not a Captain’s body, so Spock stays on the Ship.) But this limitation is precisely why the technology matters for Telecom! The fact that these measurement outcomes cannot be controlled is what makes them unhackable. This impossibility is the foundation of Quantum Key Distribution. If a hacker tries to read the quantum states carrying the encryption key, the state collapses, and the intrusion is physically exposed. Similarly, Quantum Random Number Generation provides security entropy that no supercomputer can predict or spoof. These are not lab research things. They are the new trust layer of the internet. For instance, BT Group uses Quantum Key Distribution to secure backbone routes between research sites, and SK Telecom has integrated dedicated chips for Quantum Random Number Generation into its authentication modules. Furthermore, China Mobile International Limited has built a metropolitan quantum network in Hefei for government and financial institutions. Telcos are testing entanglement in noisy metro rings and old fiber ducts for one reason: to prove that Trust can scale. So, Quantum will never carry user data. It will anchor security. The ultimate lesson of quantum physics is that intentional communication cannot outrun the speed of light. Our focus must therefore shift from speed to verifiable truth. https://lnkd.in/gp_kkNRY

Quantum Sensing Breakthrough: Tunable System Surpasses Standard Quantum Limit Introduction: A Leap Toward Ultra-Precise Measurements Physicists at the Niels Bohr Institute, University of Copenhagen, have developed a tunable quantum sensing system that could revolutionize everything from medical diagnostics to gravitational wave detection. Published in Nature, this breakthrough enables measurements that go beyond the standard quantum limit, a long-standing barrier caused by quantum noise in ultra-sensitive optical systems. ⸻ Key Advances in the New Quantum Sensing System 1. Tunability Enhances Versatility and Precision • The system can be dynamically adjusted to adapt to different sensing environments and measurement types. • This makes it ideal for a wide range of applications, from detecting biological fluctuations to measuring cosmic phenomena like gravitational waves. 2. Overcoming the Standard Quantum Limit • The standard quantum limit arises from two key sources of noise: • Back-action noise: the disturbance caused by the act of measurement itself. • Detection noise: the fundamental uncertainty in reading out a quantum signal. • The new system reduces both types of noise using quantum-enhanced techniques. 3. Quantum Tools: Squeezing, Entanglement, and Back-Action Evasion • Techniques such as squeezed light (narrowing quantum uncertainty in one variable at the expense of another) and entanglement (quantum correlations between particles) are employed to boost signal clarity. • These tools allow the system to sense changes below classical noise thresholds, a feat not possible with traditional optics. 4. Broad Applications Across Scales • Biomedical diagnostics: Could detect tiny electrical or molecular signals for early disease detection. • Gravitational wave detection: Improved precision in space observatories like LIGO or future quantum-based detectors. • Quantum computing and communications: Potential for more accurate readouts in qubits and secure transmission systems. ⸻ Why This Matters This tunable quantum sensing platform isn’t just a lab curiosity—it’s a practical advancement with enormous implications. By overcoming fundamental noise limits, it enables next-generation sensing technologies that are faster, more precise, and widely adaptable. From observing the universe’s most massive events to decoding the body’s smallest signals, this innovation opens the door to smarter diagnostics, more secure communications, and deeper scientific discovery. https://lnkd.in/gEmHdXZy

Quantum computing just made Wall Street history IBM & HSBC used IBM’s Heron-powered quantum systems for bond trading — the first applied case with high ROI. Markets noticed: IBM stock jumped 5% today. This is the moment quantum stops being theory and starts delivering value. HSBC showed early evidence that quantum computing can aid algorithmic bond trading. Working with IBM team, their researchers improved predictions of trade fill likelihood in the European corporate bond market by up to 34%, using real production-scale data from HSBC.

Quantum Computing (QC) 1/2 What is it? Quantum machines encode data using quantum bits or #qubits that can store either a zero or a one like computers today but also a weighted combination of zero and one at the same time. Principles used include #Superposition - quantum particle can represent multiple possibilities, #Entanglement - multiple particles become correlated more strongly than regular probability allows, #Decoherence - particles decay, collapse or change converting into single states measurable by physics, and #Interference - entangled particles can interact and produce more and less likely probabilities. QC can scale exponentially - 2 qubits can compute 4 pieces of information, 3 can compute 8 etc.    Today's computer v. QC - Instead of computing every step of a complicated calculation, QC can process enormous datasets simultaneously with different operators resulting in massive scale and efficiency to solve problems. Also instead of providing a single answer which is very precise, QC provide ranges of possible answers. See image.   Use cases -  #Pharmaceuticals - Molecular formulations which are the basis of drug discovery are actually quantum systems (molecules) based on quantum physics. Exact methods are computationally intractable for today's computers and approximations are often not accurate when interactions at the atomic level are critical. So in theory, the inability of an average computer today re: the limitations of basic calculations predicting molecule behavior using tools such as molecular Dynamics or Density Function Theory could be significantly improved using QC as it can now increase the scope of biological mechanism (protein folding), shorten screening time and reduce the number of iterations that result in no significant outcome. #Cybersecurity - QC allows you to take the leap from pseudo-random number generators - limitation being you cannot really generate random encryption because of the code they are built on can never be truly random and always follows a pattern to post-quantum cryptography - where given the enormous computing power and quantum physics, quantum algorithms can truly generate random numbers. So we'll move on from symmetric (AES) and asymmetric (RSA) cryptography. But on the flip side, this computational power of QC could be enough to crack AES and RSA encryptions.  I'll share what's the hold up and future in the next post.    Further Reading -  https://lnkd.in/eUMumUgp https://lnkd.in/eTVy4DnW   #quantumcomputing  Carpe Diem

🚀 New research from Amazon Quantum Solutions Lab addressing hard combinatorial optimization problems using algorithms well-suited to quantum computers. In this blog, the team takes a look at a quantum-guided cluster algorithm (QGCA) to addresses a key limitation in traditional approaches of getting trapped in local minima when solving complex combinatorial problems. By utilizing low-energy correlations, they enable collective moves that remain effective even in highly constrained and frustrated settings, where standard methods struggle. The approach is relevant for scheduling, routing, portfolio optimization, and network design problems where constraint satisfaction is challenging. ✍ Nice work by Peter Eder, Aron Kerschbaumer, Christian Mendl, Jernej Rudi Finžgar, Helmut G. Katzgraber, Martin Schuetz, Raimel A. Medina, and Sarah Braun #QuantumComputing #Optimization #Research #AWS https://lnkd.in/gchXgHgs

☕ Freshly Brewed Quantum News from The Daily Qubit x The Quantum Insider ⚆ Researchers from Prince Sattam Bin Abdulaziz University used a hybrid quantum-classical neural network to analyze speech signals to improve early Parkinson’s detection accuracy and processing speed. ⚆ A team from Sookmyung Women's University and Ajou University developed adaptive quantum federated learning for drone surveillance, enhancing license plate recognition accuracy in complex wireless environments. ⚆ IPFN - Instituto de Plasmas e Fusão Nuclear researchers created a quantum algorithm to model electron behavior in intense electromagnetic fields, advancing laser-plasma physics studies and aligning with upcoming petawatt laser facilities. ⚆ Researchers at Tsinghua University transmitted quantum correlations over 12 km of fiber using multiplexing for 280 quantum states, achieving fast transmission rates, relevant for quantum repeater development. ⚆ A team from the University of Science and Technology of China and Xiamen University improved quantum algorithms for electron microscopy by reconstructing phase-shifting circuits, making them more efficient for real hardware. ⚆ Scientists at Universidad de Chile and U.S. Army DEVCOM Army Research Laboratory developed quantum methods for detecting spoofed radar signals, achieving near-optimal detection probabilities. ⚆ The U.S. Senate Committee on Energy & Natural Resources advanced a $2.5 billion bill to fund quantum research over five years, emphasizing quantum networking and industry partnerships. ⚆ Italian startup Ephos, backed by NATO, is producing glass-based photonic quantum chips that operate at room temperature, offering an energy-efficient alternative to silicon-based chips. ⚆ Japan's New Energy and Industrial Technology Development Organization (NEDO) will launch a quantum computing competition in March 2025, offering substantial prizes to tackle societal challenges. Open to all fields, the initiative includes training to attract diverse participants and fresh perspectives. ⚆ QuEra Computing Inc.’s Yuval Boger highlighted quantum computing’s potential for automotive challenges, from battery design to manufacturing optimization. Major automakers like BMW and Volkswagen are already exploring these applications. ⚆ European physics educators demonstrated that teaching quantum concepts through qubits improves student comprehension compared to traditional methods, making modern quantum technologies more accessible to young learners. ⚆ On the most recent episode of the Bloch Sphere Quantum podcast, host Jay Shah sits down with D-Wave Vice President Murray Thom to discuss real-world quantum computing applications in production. Don't miss a single qubit. Read more at https://lnkd.in/gAj2uJJ2 #quantumcomputing #quantumtechnology #innovation #quantumscience #quantumphysics

I get at least two inbound pings every month from life-sciences companies asking some version of the same question: “So what can #quantumcomputing actually do for drug discovery and pharmaceuticals?” These used to be cocktail-napkin conversations. Now they sound more like budget discussions. What’s changed isn’t the curiosity—it’s the intent. The tone has shifted from fiction to spreadsheet. These are no longer blue-sky debates about some distant quantum future. The focus is on near-term, hybrid quantum-classical use cases that can deliver measurable advantage well before fault-tolerant quantum machines arrive. Over lunch during JPM with our resident quantum whisperer and Global Quantum leader for healthcare and life sciences at IBM Research , Gopal Karemore, PhD, I got the clearest framing yet: quantum computing today is roughly where AI was a decade ago—promising, awkward, and not quite ready to replace classical HPC. Pharma leaders don’t see it as a substitute for today’s supercomputers; they see it as a future-defining capability for molecular science. Right now, the real value is in learning, experimentation, and building hybrid workflows that blend quantum with classical simulation—especially for molecular problems where classical methods rely on approximations like DFT and force fields that eventually hit physical limits. Gopal and team came back from JPM with a consistent message from pharma and biotech: #quantum is viewed as a long-term R&D accelerator, not a near-term cost-cutting tool. The smartest organizations are running targeted pilots, building internal quantum literacy, and partnering with technology providers to become “quantum-ready.” The goal isn’t speed for its own sake—it’s better science: improved decision-making, reduced uncertainty, and new chemical and biological insight when quantum complements AI and classical simulation. So far, the use cases cluster into three main buckets: -->Molecular and electronic structure simulation – improving the accuracy of binding energies and reaction mechanisms. -->Drug discovery optimization – particularly hit-to-lead and lead optimization. -->Protein structure and dynamics – longer term, to better understand folding and functional conformations. The metrics are refreshingly practical: prediction accuracy, fewer experimental cycles, and faster (and cheaper) advancement of candidates. The hoped-for outcome is equally old-fashioned: higher-quality leads, lower attrition, and better R&D productivity. In other words, #quantum isn’t here to fire your chemists. It’s here to give them fewer wrong answers—eventually. Thank you Gopal and was lovely catching up with you and Shervin Ayati. #quantumcomputing #quantum #drugdiscovery