Practical Applications of Quantum Physics Models

When it comes to quantum applications, I have a new favorite. One you can actually follow. It's not finance. It's not logistics. It's a cancer therapy called photodynamic therapy. You give the patient a light-sensitive molecule. You shine a specific wavelength on the tumor. The molecule passes its energy to the oxygen nearby, and that oxygen turns into singlet oxygen, which kills the cell. It's already used in the clinic. So where does quantum come in? The whole thing depends on the excited states of that light-sensitive molecule. It absorbs light, jumps into a triplet state, and hands its energy to oxygen. If you want to design a better drug, you have to simulate those excited states. And that is exactly what classical computers are bad at. Triplet states, near-degeneracies, strong correlation. DFT gets shaky. The accurate methods blow up as the molecule gets bigger. So Algorithmiq, together with IBM Quantum and the Cleveland Clinic, put a quantum computer on the problem. They simulated the excited states of a real photosensitizer, that is already in a Phase II clinical trial, on IBM hardware. To quote Sabrina Maniscalco: "It's not hydrogen. It's a real molecule." Up to 100 qubits. Quantum-boosted DMRG (a classical algorithm for simulating strongly correlated quantum systems) with tensor network error mitigation. No fault tolerance needed. To be clear, this is not proven quantum advantage yet. It is a trajectory. But an exciting one. 📸 Credits: Algorithmiq Thanks to Stefan Knecht for telling me about this application in the first place!

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering

Lockheed and IBM Use Quantum Computing to Solve Chemistry Puzzle Once Thought Impossible Introduction: Cracking a Chemical Code with Quantum Power In a breakthrough for quantum chemistry, Lockheed Martin and IBM have successfully used quantum computing to model the complex electronic structure of an “open-shell” molecule—a challenge that has defied classical computing for years. This marks the first application of the sample-based quantum diagonalization (SQD) method to such systems and signals a significant advance in the practical application of quantum computing for scientific research. Key Highlights from the Collaboration • The Molecule: Methylene (CH₂): • Methylene is an open-shell molecule, meaning it has unpaired electrons that lead to complex quantum behavior. • These molecules are notoriously difficult to simulate accurately because electron correlations create exponentially growing complexity for classical algorithms. • The Innovation: Sample-Based Quantum Diagonalization (SQD): • The team used IBM’s quantum processor to implement SQD for the first time in an open-shell system. • SQD is a hybrid algorithm that leverages quantum sampling to solve eigenvalue problems in quantum chemistry, reducing computational burdens. • Why Classical Methods Fall Short: • Traditional high-performance computing (HPC) platforms struggle with electron correlation in multi-electron systems. • Approximation techniques become prohibitively expensive as system size increases, especially for reactive or radical species like methylene. • Quantum Advantage in Practice: • Quantum processors can represent electron configurations using entangled qubits, offering more scalable solutions. • By simulating the electronic structure directly, quantum methods could help scientists design new materials, catalysts, and pharmaceuticals faster and more efficiently. Why It Matters: Pushing Past the Limits of Classical Chemistry • Industrial and Scientific Impact: • Simulating open-shell systems is vital for battery design, combustion processes, and metalloprotein modeling. • The success of SQD opens the door to accurate modeling of previously inaccessible molecules, potentially accelerating innovations in energy, health, and aerospace. • Defense and Aerospace Relevance: • Lockheed Martin’s involvement reflects strategic interest in applying quantum computing to defense-grade materials and mission-critical chemistry. • Quantum Chemistry as a Flagship Use Case: • This achievement underscores how quantum computing is beginning to deliver real results in scientific domains where classical methods hit their ceiling. • As quantum hardware improves, the number of solvable molecular systems will expand exponentially. Quantum computing just helped humanity take a critical step into the chemical unknown, proving its value not just in theory—but in practice. Keith King https://lnkd.in/gHPvUttw

Exponential quantum advantage in processing massive classical data by John Preskill https://lnkd.in/eUTvGHaX Abstract Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real-world applications, including single-cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP = BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.

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.

⚛️ Two quantum breakthroughs this week just moved us significantly closer to practical quantum computers that could solve real-world problems. Alice & Bob in Paris achieved something remarkable: their "Galvanic Cat" qubits can now resist errors for over an hour - that's millions of times longer than standard qubits that typically last only microseconds. This solves quantum computing's biggest challenge: keeping information stable long enough to perform meaningful calculations. Meanwhile, Caltech physicists assembled the largest qubit array ever built: 6,100 neutral atoms trapped by 12,000 laser "optical tweezers" with 99.98% accuracy. Think of it as building a quantum city where every atom is perfectly positioned and controlled. 🏗️ Here's why this matters for every industry: 💊 Pharmaceutical companies could simulate molecular interactions in hours instead of years, accelerating drug discovery 🔋 Materials scientists could design better batteries and solar panels by understanding quantum behavior 🧬 Medical researchers could unlock new treatments by modeling complex biological systems 🏦 Financial institutions could optimize portfolios and detect fraud with unprecedented precision These cat qubits could reduce quantum computer hardware requirements by up to 200 times compared to competing approaches - making quantum computers not just more powerful, but dramatically cheaper and more accessible. 💰 The actionable insight: Start preparing your teams now. Companies that understand quantum applications in their field will have a massive competitive advantage when these systems become commercially available in the next 5-7 years. What quantum applications could transform your industry? Share your thoughts below! 👇 https://lnkd.in/ea4p9Sby https://lnkd.in/e8Urf97w

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.