Applications That Require Real Quantum Hardware

Quantum Computers Bring Molecule Simulations Closer to Reality A study published in Nature Physics outlines a breakthrough approach to simulating the behavior of electrons in small molecules, like those in catalysts, using quantum computers. This work represents an important step toward harnessing quantum computing for practical scientific problems, particularly in chemistry and materials science. Quantum Computing for Molecular Simulations • Simulating the behavior of electrons in molecules is inherently a quantum mechanical problem, making it a natural fit for quantum computers. • Catalysts, which accelerate chemical reactions, rely on electron behavior to function. Understanding these dynamics could lead to breakthroughs in fields like energy storage, drug design, and industrial chemistry. The Challenge: Hardware and Errors • Current quantum computers are not yet capable of handling complex simulations due to their limited number of qubits and high error rates. • Researchers estimate that simulations of simple catalysts will require around 100 error-corrected qubits, which are qubits stabilized against computational errors. • The new method optimizes the process, allowing researchers to extract meaningful insights using today’s more limited quantum hardware. Key Findings of the Study • The researchers developed an improved algorithm to simulate electronic structures with higher accuracy while reducing the computational load. • They tested the method on a quantum computer, focusing on specific aspects of molecular behavior, and successfully validated the results against classical simulations. • The findings suggest that quantum computers could become practical for simulating small molecules well before larger, error-corrected systems become widely available. Implications for the Future 1. Accelerated Materials Discovery: • Quantum simulations could identify new catalysts for cleaner energy production, such as hydrogen fuel generation. 2. Hardware Development: • The study provides benchmarks for quantum hardware manufacturers, helping them design systems capable of handling these simulations. 3. Path to Practicality: • By focusing on relatively small molecules, researchers can make quantum computing useful sooner, even before achieving fault-tolerant quantum systems. Conclusion This study demonstrates that with optimized algorithms, quantum computers are inching closer to solving practical problems in chemistry and materials science. While full-scale quantum simulations of large molecules remain a challenge, targeted approaches like this one showcase the potential for near-term applications in catalyst design and beyond.

Interesting approach alert! QUBO-based SVM tested on QPU (Neutral Atoms). A recent study, "QUBO-based SVM for credit card fraud detection on a real QPU," explores the application of a novel quantum approach to a critical cybersecurity challenge: credit card fraud detection. Here are some of the key findings: * QUBO-based SVM model: The study successfully implemented a Support Vector Machine (SVM) model whose training is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach could leverage the capabilities of quantum processors. * Performance: The results demonstrate that a version of the QUBO SVM model, particularly when used in a stacked ensemble configuration, achieves high performance with low error rates. The stacked configuration uses the QUBO SVM as a meta-model, trained on the outputs of other models. * Noise robustness: Surprisingly, the study observed that a certain amount of noise can lead to enhanced results. This is a new phenomenon in quantum machine learning, but it has been seen in other contexts. The models were robust to noise both in simulations and on the real QPU. * Scalability: Experiments were extended up to 24 atoms on the real QPU, and the study showed that performance increases as the size of the training set increases. This suggests that even better results are possible with larger QPUs. Practical implications: This research highlights the potential of quantum machine learning for real-world applications, using a hybrid approach where the training is performed on a QPU and the testing on classical hardware. This approach makes the model applicable on current NISQ devices. The model is also advantageous because it uses the QPU only for training, reducing costs and allowing the trained model to be reused. * Ideal for cybersecurity and regulatory issues: The study also observed that the model preserves data privacy because only the atomic coordinates and laser parameters reach the QPU, and the model test is done locally. Here the article: https://lnkd.in/d5Vfhq2G #quantumcomputing #machinelearning #cybersecurity #frauddetection #neutralatoms #QPU #NISQ #quantumml #fintech #datascience

Is this the first real-world use case for quantum computers? True randomness is hard to come by. And in a world where cryptography and fairness rely on it, “close enough” just doesn’t cut it. A new paper in Nature claims to present a demonstrated, certified application of quantum computing, not in theory or simulation, but in the real world. Led by Quantinuum, JPMorganChase, Argonne National Laboratory, Oak Ridge National Laboratory, and The University of Texas at Austin, the team successfully ran a certified randomness expansion protocol on Quantinuum’s 56-qubit H2 quantum computer, and validated the results using over 1.1 exaflops of classical computing power. TL;DR is certified randomness--the kind of true, verifiable unpredictability that’s essential to cryptography and security--was generated by a quantum computer and validated by the world’s fastest supercomputers. Here’s why that matters: True randomness is anything but trivial. Classical systems can simulate randomness, but they’re still deterministic at the core. And for high-stakes environments such as finance, national security, or fairness in elections, you don’t want pseudo-anything. You want cold, hard entropy that no adversary can predict or reproduce. Quantum mechanics is probabilistic by nature. But just generating randomness with a quantum system isn’t enough; you need to certify that it’s truly random and not spoofed. That’s where this experiment comes in. Using a method called random circuit sampling, the team: ⚇ sent quantum circuits to Quantinuum’s 56-qubit H2 processor, ⚇ had it return outputs fast enough to make classical simulation infeasible, ⚇ verified the randomness mathematically using the Frontier supercomputer ⚇ while the quantum device accessed remotely, proving a future where secure, certifiable entropy doesn’t require trusting the hardware in front of you The result? Over 71,000 certifiably random bits generated in a way that proves they couldn’t have come from a classical machine. And it’s commercially viable. Certified randomness may sound niche—but it’s highly relevant to modern cryptography. This could be the start of the earliest true “quantum advantage” that actually matters in practice. And later this year, Quantinuum plans to make it a product. It’s a shift— from demos to deployment from supremacy claims to measurable utility from the theoretical to the trustworthy read more from Matt Swayne at The Quantum Insider here --> https://lnkd.in/gdkGMVRb peer-reviewed paper --> https://lnkd.in/g96FK7ip #QuantumComputing #CertifiedRandomness #Cryptography

🔐💻 Everyone in #quantum #computing seems to be chasing hundreds or thousands of qubits — to break encryption, simulate chemistry, or power quantum machine learning. But what if just 4 high-quality qubits could already outperform classical systems in a real-world task? 🛰️ A recent preprint (https://lnkd.in/e6m2gcsm) on quantum-processing-assisted classical #communications shows exactly that: Using joint quantum measurements on codewords of weak optical signals, a quantum receiver with only 4 qubits can exceed the performance of any known classical optical decoder — even with realistic gate error rates and losses. ⚡️ This isn’t about quantum supremacy or exotic algorithms. It's about using small-scale quantum logic to unlock real advantages in classical communication — like deep-space links, secure low-power channels, or covert messaging. Pretty cool idea and approach. And it is a potentially really practical application of QC. ‼️ Good job from Harvard and MIT! I'm looking for an experimental demonstration!

Google's quantum computer achieved a measurable advantage over classical computers for molecular analysis. Their Quantum Echoes algorithm represents progress toward practical quantum computing applications in chemistry and materials science. The research details: ↳ Published in Nature with peer review ↳ 13,000x performance improvement on specific calculations ↳ Tested on molecules with 15 and 28 atoms ↳ Results verified against established Nuclear Magnetic Resonance data The algorithm functions as a "molecular ruler" that can measure atomic distances and interactions. It uses quantum interference effects to amplify measurement signals, providing sensitivity that classical computers struggle to achieve efficiently. Current applications being explored include: ↳ Drug development for understanding molecular binding ↳ Materials research for battery and polymer characterization   ↳ Chemical analysis for determining molecular structures ↳ Nuclear Magnetic Resonance enhancement for laboratory use Google worked with UC Berkeley to validate the approach. The quantum computer analyzed molecular structures and provided information that traditional methods either missed or required significantly more computational time to obtain. The research addresses a practical problem in computational chemistry where molecular modeling requires substantial computing resources. Quantum computers may offer efficiency advantages for these specific types of calculations. This work follows Google's established quantum computing research program, building on their previous demonstrations of quantum error correction and computational complexity advantages. Which scientific fields do you think will adopt quantum-enhanced analysis methods first? ♻️ Share this to inspire someone. ➕ Follow me to stay in touch.

🚀 New Paper: Simulating Quantum Materials on Quantum Computers  🚀 In our new scientific article, we use Pauli Path Simulation (PPS) in the BlueQubit SDK as a practical tool for utility-scale quantum state preparation in quantum materials -- from spin models and phase diagrams to topological excitations. Why it matters for materials: 🔹 Predict ground-state energies and order parameters to map phase boundaries and structure–property behavior 🔹 Probe frustration and topology (e.g., Kitaev-type interactions) relevant to spin-liquids and next-gen devices Results (from our latest publication): ⚛️ 48-qubit Kitaev honeycomb on Quantinuum hardware with ~5% relative energy error 📈 PPS outperforms DMRG in select 2D Ising regimes 🌀 First anyon braiding beyond fixed-point models on real quantum hardware Big shoutout to the BlueQubit team – Cheng-Ju Lin and Vincent Su – for driving this forward. Read the full study: https://lnkd.in/d9m9hh87 #QuantumComputing #QuantumMaterials #CondensedMatter #PauliPathSimulation #TopologicalOrder #KitaevModel #IsingModel #MaterialsDiscovery