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

How will quantum technologies reshape the defence industry? While the impact of AI and new digital solutions is being discussed every day, I would like to spotlight #quantum technologies ⚛️ Quantum needs to be observed as the next revolutionary leap shaping the #defence industry technically, strategically, and ethically. The possible applications are extraordinary, and @MBDA’s teams are exploring them daily with the support of the European quantum ecosystem. 🚀 #Operation: the improved effectiveness of physical simulations thanks to quantum computers is due to enable MBDA to accelerate in the race toward hypervelocity. Quantum sensors can improve product testing, and Quantum Machine Learning opens the way to enhanced training for operators. Quantum computing can also serve as a lever for optimising troop logistics and system deployment, movements, mission preparation, and real-time resources and mission management in the battlespace. 🛡️ #Cybersecurity and communication: embracing the opportunities of quantum computers for our products and infrastructures, ensuring the safety of systems through post-quantum cryptography, and preparing MBDA for quantum-secured communications through technologies such as quantum key distribution. 📡 #Sensing: navigation units and accurate, compact clocks will allow precise localisation in all environments and contexts, even without positioning satellites. Ultimately, ultra-sensitive radar or seeker capabilities will be embedded into our products to extend engagement opportunities and weapon performance. It is now up to our engineers and future talents to make the most out of this game-changing capability, as part of MBDA’s core mission to anticipate the future needs of our armed forces. The quantum revolution is underway and is set to become – whether tomorrow or the day after – a pivotal asset in strengthening our military superiority. Beyond defence, the remarkable acceleration capacity offered by quantum technologies might change the face of many industries. They already stand out as a major pillar of European #sovereignty. It is our responsibility to make sure we are ready for this new revolution.

A new preprint, “Learning ground state observables from quantum computing experiments,” shows how machine learning can predict properties of interacting many-body systems from data generated on quantum processors, on systems of up to 115 qubits: https://lnkd.in/euAitfbd In collaboration with University of Oxford and STFC Hartree Centre, researchers from IBM developed a basis-optimization technique that combines sample-based quantum diagonalization (SQD) with observable backpropagation, allowing the low-energy subspace to be represented using entangled basis states. Using this workflow, the researchers could study the two-dimensional Heisenberg XXZ model and generate experimental training data across the antiferromagnetic phase. The dataset includes local observables, two-point correlations, and 12-body loop observables, with the same framework extending in principle to global observables. Classical neural networks trained on these data accurately predict spatially resolved observables at Hamiltonian parameters not included during training, including beyond the training range. This work demonstrates an end-to-end workflow combining quantum experiments, high-performance computing, and classical machine learning. More broadly, it shows how quantum computing and AI can complement one another, with quantum processors producing data about complex physical systems and AI using that information to accelerate scientific discovery.

Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. However, there is a significant bottleneck: precise calibration is short-lived, which requires perpetually adapting the control parameters of the computer to the drifting environmental conditions. Just published in Nature, our team showed unified calibration with error-corrected computation on our Willow processor, training a reinforcement learning agent to stabilize a logical qubit and pave the way towards a quantum computer that continuously learns from its errors. Key research findings: -->✨ A New Paradigm: This work enables a future where we have a quantum computer that learns from its errors and never stops computing. Removing the need to take the system offline for calibration. --> Improved Stability: We experimentally demonstrated this framework on our Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. --> Beyond Traditional Limits: RL fine-tuning of an already well-calibrated processor yields an additional 20% suppression of the logical error rate, pushing performance beyond the limits of traditional physics-based calibration and human expert tuning. --> Scalability: Numerical simulations confirm the scalability of our RL framework, revealing that the optimization speed is independent of system size, ensuring this remains just as effective as we scale to much larger systems. This research demonstrates that the path to fault-tolerant quantum computing relies not just on better hardware, but on more intelligent control systems. I am proud of our teams for pioneering this approach, an important step towards solving the challenges in quantum information science. Nature article here: https://lnkd.in/gfuxKYJf

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.

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.

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

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.

Is Quantum Machine Learning useful? When we think about this question, we tend to wonder if quantum computing could accelerate our known ML algorithms. But that could be the wrong way to go about it. A quantum processing unit is a different type of hardware with different computation principles, and as such, it is a great candidate to develop new ML algorithms with purely quantum principles. Quantum ML can actually mean multiple things. There are 2 components to ML: data generation and the data processing device, and each component could be quantum or classical: - If both the data generation process and the data processing device are classical, that would be typical Machine Learning as we know it. - Typically, when people think about QML, they think of the data generation process being classical and the data processing being done on a quantum computer. The data could be text, images, or time series, and we need a quantum-classical interface to convert that data into quantum data. The quantum computer can only process quantum data, and a quantum algorithm would generate outputs that need to be converted into classical data. Converting the data back and forth requires at least linear time complexity in the size of the data, preventing any exponential speed of learning tasks. Many people doubt this process will ever be beneficial. - One interesting avenue for QML is if the data generation is intrinsically quantic. For example, in the Physics, Chemistry, or Biology departments, researchers deal with quantum "data" on a daily basis. Electrons in your CPU or medication molecules abide by quantum mechanical laws. A typical way to study those phenomena is to build numerical simulations using synthetic classical data simulating quantum particles, with those simulations being run on a classical computer. This is very slow, and we can simulate a limited number of particles at once. But if we could use quantum data to simulate quantum particles, we could run quantum ML algorithms directly on those data. There is evidence that this would lead to a quantum speed-up of the process. QML could lead to huge scientific leaps in the near future!  A few hybrid quantum-classical architectures have been proposed where models are spread across classical and quantum processing units. This allows the processing of quantum data with a computer but benefits from the advantage of well-understood computations on classical computers. For example, you can use classical computers as outer loop optimizers for quantum neural networks. An example is Tensorflow Quantum (https://lnkd.in/eziVB4q9), which is mainly intended for applications involving quantic data generation but can also be used for classical data. Here is an example of how to run a ConvNet on quantum data: https://lnkd.in/dx9nmY9n -- 👉 Early-bird deal for my ML Fundamentals Bootcamp: https://lnkd.in/gasbhQSk -- #machinelearning #datascience #artificialintelligence