Quantum Computing Applications

A milestone in quantum physics — rooted in a student project What began as a student's undergraduate thesis at Caltech — later continued as a graduate student at MIT — has grown into a collaborative experiment between researchers from MIT, Caltech, Harvard, Fermilab, and Google Quantum AI. Using Google’s Sycamore quantum processor, the team simulated traversable wormhole dynamics — a quantum system that behaves analogously to how certain wormholes are predicted to work in theoretical physics. Here’s what they did: Implemented two coupled SYK-like quantum systems on the processor that represent black holes in a holographic model. Sent a quantum state into one system. Applied an effective “negative energy” pulse to make the simulated wormhole traversable. Observed the state emerge on the other side — consistent with quantum teleportation. This wasn’t just classical computer modeling — it ran on real qubits, using 164 two-qubit quantum gates across nine qubits. Why it matters: The results are consistent with the ER=EPR conjecture, which suggests a deep link between quantum entanglement and spacetime geometry. In the holographic picture, patterns of entanglement can be interpreted as wormhole-like “bridges.” This experiment shows how quantum processors can begin to probe aspects of quantum gravity in a laboratory setting, complementing astrophysical observations and theoretical work. While no physical wormhole was created, this is a step toward using quantum computers to explore some of the most fundamental questions in physics. What breakthrough in science excites you most? Share your thoughts below — and let’s discuss how quantum computing is reshaping our understanding of reality. ♻️ Repost to help people in your network. And follow me for more posts like this. CC: thebrighterside

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

Interesting research in Quantum Machine Learning addresses key challenges in scalability and data encoding. The GitHub repository is included for further reference. A recent study titled "An Efficient Quantum Classifier Based on Hamiltonian Representations" (Tiblias et al.) proposes a novel approach to quantum classification. The study tackles the limitations of current QML methods that often rely on toy datasets or significant feature reduction due to hardware constraints and the high costs of encoding dense vector representations on quantum devices. The researchers introduce an efficient approach called the Hamiltonian classifier, which circumvents the costs of data encoding by mapping inputs to a finite set of Pauli strings and making predictions based on their expectation values. They also present two classifier variants, PEFF and SIM, with different trade-offs in terms of parameters and sample complexity. Key outcomes of this work include: * A new encoding scheme achieving logarithmic complexity in both qubits and quantum gates relative to the input dimensionality. * The development of classifier variants (PEFF and SIM) offers different performance-cost trade-offs. PEFF reduces model size, while SIM boasts better sample complexity. * The Simplified Hamiltonian (SIM) variant achieves logarithmic scaling in qubit and gate complexity along with a constant sample complexity, making it a strong candidate for practical implementation on Noisy Intermediate-Scale Quantum (NISQ) devices. * Experiments showed that increasing the number of Pauli strings in the SIM model leads to better performance and more stable training dynamics, with models using 500 to 1000 Pauli strings often matching the performance of classical baselines. You can find the GitHub repo here: https://lnkd.in/dN38CFPv. The article here: https://lnkd.in/dG4agXap #quantumcomputing #machinelearning #quantummachinelearning #artificialintelligence #research #nlp #imageclassification #datascience

Most enterprises treat quantum computing as a nerdy R&D curiosity. A mistake. Critical business problems, which are fundamentally constrained by classical computing today, are likely to be solved by 2030. With a hybrid combination of high performance computing and quantum approaches. Three sectors stand out: Pharma, Life & Material Sciences: Drug discovery is essentially a molecular simulation challenge. Classical systems approximate. Quantum systems are designed around quantum mechanics itself. Thus, it is not just about faster research, but the ability to model molecular interactions with higher fidelity. For protein folding, compound optimization, personalized therapeutics. Reaching quantum advantage first in pharma won’t merely accelerate pipelines — it will redefine them. Financial Services: Banks, insurers, stock exchanges operate enormous optimization, transaction or probability engines. E.g., for risk simulations, or fraud detections. Many of these problems scale exponentially in complexity. Quantum algorithms are particularly promising where classical Monte Carlo simulations hit practical limits. And, quantum computing is becoming a cybersecurity challenge. Post-quantum cryptography migration will likely be one of the largest infrastructure transitions the financial sector has seen for decades. Complex Logistics & Supply Chains: Airlines, shipping companies, manufacturers, energy grids, and global retailers all face combinatorial optimization problems. These systems already operate at scales where small efficiency gains create major business impact. Enterprises operating in these segments should get „quantum-ready“ now: • Identify quantum-relevant business problems • Work with quantum partners who advocate an open approach • Build internal quantum literacy • Develop hybrid workflows • Prepare your security stack for the post-quantum era. Additionally we need quantum computing companies delivering at production scale. IQM Quantum Computers calls this Production Quantum. Which is the delivery of a production-ready full stack solution rather than just a scientific solution for a specific problem. This is the same pattern we saw with #AI. The competitive gap formed before the technology fully matured. #Quantum readiness is becoming a strategic capability and critical timing question. For an increasing number of enterprises. Not only for R&D departments.

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.

Today in Science Magazine, work from our IBM team, in collaboration with The University of Manchester, University of Oxford, ETH Zürich, EPFL and the University of Regensburg, shows the creation and simulation of a new molecule with an electronic structure that has never existed before — a half‑Möbius topology: https://lnkd.in/eFU5s9qR. The molecule was assembled using scanning probe microscopy at temperatures just above absolute zero — building it one atom at a time using STM, atom manipulation, and AFM. The electronic orbitals of this half‑Möbius molecule twist by 90 degrees with every loop around the ring, completing a full turn only after four revolutions. Why is this also important for quantum computing? This work demonstrates, for the first time, that quantum computing calculations can provide decisive scientific guidance and powerful characterization capabilities to support the discovery of new complex chemical molecules. In close collaboration with leading experimental laboratories, quantum simulations can now contribute directly to interpreting experimental observations and to guiding the design and understanding of novel molecular systems. The calculations performed in this project go well beyond the regime accessible to brute-force classical simulations, although we do not exclude the possibility that approximate classical methods could also provide valuable insights. Nevertheless, the discovery process itself benefited from quantum simulation, and we chose to employ quantum computing because it offers a natural and scalable framework for tackling problems of this kind. In particular, by comparing Dyson orbitals measured with scanning tunneling microscopy (STM) with images reconstructed from electronic structure calculations performed on a quantum computer using the SqDRIFT algorithm, we were able, for the first time, to contribute directly to the discovery and characterization of a new molecule exhibiting entirely novel electronic structure properties. paper: https://lnkd.in/esg9sHqV

Quantum computing is moving from promise to platform. For years, quantum has lived in research labs and long-term roadmaps. The question was not whether it worked in theory, but whether anyone could turn it into an ecosystem, a product portfolio, and a repeatable strategy. According to a 2025 report from Gartner, IBM is now the company to beat in quantum computing. The recognition points to strength across research, product portfolio, and ecosystem development. A combination that matters. Quantum computing is not an incremental upgrade to classical systems. It is a fundamentally different architecture built on superconducting transmon qubits and quantum circuits. These systems are designed to tackle classes of problems that remain inaccessible to even the most powerful classical supercomputers. The real shift, however, is not about replacing classical computing. It is about integrating it. IBM’s vision centers on a quantum-centric supercomputing model, where quantum processing units work alongside classical hardware such as GPUs. Each architecture handles the part of the problem it is best suited for. Together, they address challenges no single system can solve alone. Execution is where many emerging technologies stall. In quantum, it has required sustained research, disciplined milestones, and early access for developers and enterprises. IBM first put a quantum computer on the cloud in 2016. Since then, it has built an ecosystem that includes hundreds of academic institutions, startups, and industry partners, alongside the open source Qiskit software development kit. Roadmaps are easy to publish. Delivering against them is harder. Since introducing its quantum development roadmap in 2019, IBM has consistently met its stated milestones. Therefore, this recognition is not about a single breakthrough. It reflects a long-term strategy that connects research, hardware, software, and community into a coherent platform. #IBM #IBMiX #Quantum

MIT Sets Quantum Computing Record with 99.998% Fidelity Researchers at MIT have achieved a world-record single-qubit fidelity of 99.998% using a superconducting qubit known as fluxonium. This breakthrough represents a significant step toward practical quantum computing by addressing one of the field’s greatest challenges: mitigating noise and control imperfections that lead to operational errors. Key Highlights: 1. The Problem: Noise and Errors • Qubits, the building blocks of quantum computers, are highly sensitive to noise and imperfections in control mechanisms. • Such disturbances introduce errors that limit the complexity and duration of quantum algorithms. “These errors ultimately cap the performance of quantum systems,” the researchers noted. 2. The Solution: Two New Techniques To overcome these challenges, the MIT team developed two innovative techniques: • Commensurate Pulses: This method involves timing quantum pulses precisely to make counter-rotating errors uniform and correctable. • Circularly Polarized Microwaves: By creating a synthetic version of circularly polarized light, the team improved the control of the qubit’s state, further enhancing fidelity. “Getting rid of these errors was a fun challenge for us,” said David Rower, PhD ’24, one of the study’s lead researchers. 3. Fluxonium Qubits and Their Potential • Fluxonium qubits are superconducting circuits with unique properties that make them more resistant to environmental noise compared to traditional qubits. • By applying the new error-mitigation techniques, the team unlocked the potential of fluxonium to operate at near-perfect fidelity. 4. Implications for Quantum Computing • Achieving 99.998% fidelity significantly reduces errors in quantum operations, paving the way for more complex and reliable quantum algorithms. • This milestone represents a major step toward scalable quantum computing systems capable of solving real-world problems. What’s Next? The team plans to expand its work by exploring multi-qubit systems and integrating the error-mitigation techniques into larger quantum architectures. Such advancements could accelerate progress toward error-corrected, fault-tolerant quantum computers. Conclusion: A Leap Toward Practical Quantum Systems MIT’s achievement underscores the importance of innovation in error correction and control to overcome the fundamental challenges of quantum computing. This breakthrough brings us closer to the realization of large-scale quantum systems that could transform fields such as cryptography, materials science, and complex optimization problems.

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

Microsoft’s Majorana 1 reignited the buzz about our quantum future. Here’s why Quantum is an important step forward for the world: Traditional computers struggle with solving some problems that quantum computing can easily tackle. When it comes to drug discovery, for example, traditional computers must approximate solutions for molecular behavior, often at the expense of time and precision. Quantum computing, leveraging the unique properties of quantum mechanics, promises to simulate these interactions with far greater accuracy and efficiency. This means accelerating the discovery of new drugs and potentially revolutionizing healthcare. Just as AI has sped up our ability to innovate, pairing it with quantum computing could supercharge that acceleration. Unlike AI, Quantum won’t be something that hits consumers with a “Chat GPT moment” right now. The impact of quantum breakthroughs will be felt in improved healthcare, better materials, and smarter technologies that enhance our daily lives in the background. It’s also important to note: Majorana 1 and other breakthroughs are a massive step forward in building a quantum-world, but history reminds us that transformative change is often a journey. Even the loudest proponents agree—real, tangible benefits won't happen instantly. Yet, as with every pioneering technology, the potential is immense, and the iterative process of innovation will get us there.