Experimental Methods for Studying Quantum Complexity

In an international collaboration, researchers from BasQ, CERN, UAM–CSIC, the Wigner Research Centre for Physics, and IBM have simulated the real-time dynamics of confining strings in a (2+1)-dimensional Z2-Higgs gauge theory with dynamical matter, leveraging a superconducting quantum processor with up to 144 qubits and 192 two-qubit layers (totaling 7,872 two-qubit gates). This work tackles a longstanding challenge in high-energy physics: understanding the real-time dynamics of confinement in gauge theories with dynamical matter—a crucial aspect of non-perturbative quantum field theory, including quantum chromodynamics (QCD). Classical methods face fundamental limitations in simulating these dynamics, often requiring indirect approaches such as asymptotic in-out probes in collider experiments. Quantum processors, by contrast, now offer the opportunity to observe the microscopic evolution of confining strings directly, opening new pathways for studying these complex phenomena in real time. To accomplish this, matter and gauge fields were encoded into superconducting qubits through an optimized mapping onto IBM’s heavy-hex architecture. By exploiting local gauge symmetries, the team applied a robust combination of error suppression, mitigation, and correction techniques—including novel methods such as gauge dynamical decoupling (GDD) and Gauss sector correction (GSC)—enabling high-fidelity observations of string dynamics, supported by 600,000 measurement shots per time step. The results reveal both longitudinal and transverse string dynamics—including yo-yo oscillations and endpoint bending—as well as more complex processes such as string fragmentation and recombination, which are essential to understanding hadronization and rotational meson spectra from first principles. To predict large-scale real-time behavior and benchmark the experimental results, the study integrates state-of-the-art tensor network simulations using the basis update and Galerkin methods. Altogether, this paper marks a significant milestone in the quantum simulation of non-perturbative gauge dynamics, showcasing how current quantum hardware can be used to explore real-time phenomena in fundamental physics. paper is here https://lnkd.in/eD89BKqi

❓ 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

In 2013, researchers at Lund University achieved the first direct visualization of hydrogen electron orbitals using photoionization microscopy—a technique that transforms quantum probability distributions into observable patterns. The experimental approach involved exciting hydrogen atoms with precisely tuned laser pulses, ionizing electrons from specific quantum states. As electrons escaped, position-sensitive detectors recorded their trajectories thousands of times. Since quantum mechanics dictates that measurement outcomes follow probability distributions defined by the wave function, accumulating many measurements reconstructs that underlying distribution—effectively imaging the orbital's shape. The resulting data confirms quantum mechanical predictions with striking precision. The concentric ring patterns correspond to nodes and antinodes in the electron wave function for particular quantum states. This isn't imaging the electron itself—which has no definite position before measurement—but rather mapping the probability amplitude governing where measurements will find it. The technique validates a cornerstone of quantum theory: particles are described by wave functions that determine statistical measurement outcomes rather than deterministic trajectories. Beyond hydrogen, this methodology offers insights into atomic structure, chemical bonding, and quantum state engineering. Visualizing orbitals helps bridge the gap between abstract mathematical formalism and physical intuition, making quantum mechanics more tangible for researchers developing quantum technologies. #life #news #science

I've been tackling the "barren plateaus" problem in QML, where training stalls inside vast search spaces. My latest experiment in fraud detection revealed a fascinating, counterintuitive solution. I discovered that increasing my quantum circuit's entanglement didn't smooth the path to a solution, but it created a more complex and rugged loss landscape (using a dressed quantum circuit scheme). Taking advantage of the hyvis library, I visualized this effect (thanks to the colleagues of JoS QUANTUM for putting this together), as shown in the first image of the post. The landscape evolves from a simple valley to a rich, expressive terrain (but potentially more complex for an optimizer). But did this complexity hurt performance? Usually that should be the case, but the exact opposite happened. The image shows the model with the most complex landscape (8 CNOTs by layer) not only learned faster (lower loss) but also achieved the highest accuracy (AUC) on the validation set and later in the test set. There is no free lunch on this. We can't generalize from these examples. This added complexity, or "expressivity," is precisely what allowed the model to find a superior solution in this case and avoid getting stuck, but it is not the norm. My biggest conclusion here It seems that for QML, the key to real-world performance isn't avoiding complexity, but leveraging it. To be able to extract permanent benefits, we should follow approaches like what Dra. Eva Andres Nuñez is researching by finding the way to use the extra complexity of entanglement to be able to find the global minima and not get stuck in our quantum optimization procedures using the theory behind SNNs. Here details about the hyvis library in GitHub: https://lnkd.in/dzqcFvDE An insightful paper from Eva about mixing SNNs and quantum: https://lnkd.in/dXDiuCBH Same subject from Jiechen Chen: https://lnkd.in/d-Uyngef #quantumcomputing #machinelearning #ai #datascience #frauddetection #ml #qml

Physicists Measure Quantum Geometry for the First Time MIT researchers achieve a breakthrough in measuring the quantum shape of electrons in solids, unlocking new possibilities for quantum materials research. For the first time, physicists at MIT and their collaborators have directly measured the quantum geometry of electrons in solids—a property that was previously only inferred through theoretical calculations. The findings, published in the November 25 issue of Nature Physics, mark a significant leap in our understanding of the quantum behavior of materials. Why Quantum Geometry Matters: • Beyond Energy and Velocity: Scientists have long been able to measure the energy levels and velocities of electrons in crystalline materials. However, quantum geometry, which describes the shape of electron wave functions, remained elusive. • A New Measurement Blueprint: This study provides a methodology for directly probing quantum geometry, offering insights into electron behavior at the most fundamental level. Key Insights from the Study: 1. Direct Measurement Achieved: • Researchers successfully mapped the quantum geometry of electrons in a solid for the first time. 2. Broader Applicability: • The methods developed can be applied to any type of quantum material, not just the specific material studied in this research. 3. Technological Implications: • Understanding quantum geometry could lead to advancements in quantum computing, superconductivity, and other emerging technologies. Significance of the Breakthrough: • New Avenues for Research: The findings open the door for scientists to better understand and manipulate quantum materials. • Enhanced Material Design: Engineers can now design materials with precisely controlled quantum properties, optimizing them for specific applications. Quotes from the Researchers: • Riccardo Comin, MIT Associate Professor of Physics: “We’ve essentially developed a blueprint for obtaining some completely new information that couldn’t be obtained before.” • Mingu Kang, First Author and Kavli Postdoctoral Fellow: “This work could be applied to any kind of quantum material, not just the one we worked with.” The Future of Quantum Geometry: 1. Applications in Quantum Technologies: • Improved understanding of quantum geometry could enhance quantum computing platforms, topological insulators, and advanced superconductors. The Takeaway: The ability to measure the quantum geometry of electrons in solids represents a groundbreaking advance in quantum physics. With the methodology established in this research, scientists can now directly explore the quantum landscape of materials, leading to potential breakthroughs in quantum computing, energy storage, and next-generation electronic devices. This milestone sets the stage for a new era in quantum materials research, where the geometry of electrons is no longer hidden but a measurable and actionable property.

I'm excited to share our latest work, Demonstration of robust and efficient quantum property learning with shallow shadows, published in Nature Communications! 🎉 📝 Authors: Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren, Yipei Zhang, Derek S. Wang, Yi-Zhuang You, Zlatko Minev, Susanne F. Yelin, Alireza Seif 🔍 Context: Extracting information efficiently from quantum systems is crucial for advancing quantum information processing. Classical shadow tomography offers a powerful technique, but it struggles with noisy, high-dimensional quantum states and complex observables. 🤔 Key Question: Can we overcome noise limitations and improve sample efficiency in quantum state learning, especially for high-weight and non-local observables, using shallow quantum circuits? 💡 Our Findings: We introduce robust shallow shadows—a protocol designed to mitigate noise using Bayesian inference, enabling highly efficient learning of quantum state properties, even in the presence of noise. Our experiments on a 127-qubit superconducting quantum processor confirm the protocol’s practical use, showing up to 5x reduction in sample complexity compared to traditional methods. ✨ Key Takeaways: 1. Noise-resilience: Accurate predictions across diverse quantum state properties. 2. Sample Efficiency: Substantial reduction in sample complexity for high-weight and non-local observables. 3. Scalability: The protocol is well-suited for near-term quantum devices, even with noise. Paper: https://lnkd.in/dW4NJ23Q

Last week, we shared exciting new results studying operator dynamics on structured circuits designed by our collaborators at Algorithmiq. Our experiments on up to 70 qubit, high-fidelity, heavy-hex layouts, with heuristic error mitigation methods, produced accurate results at short depths that were verified with classical simulation. At larger circuit depths (up to 1872 CZ gates), the circuits were seen to be challenging for Belief propagation-based tensor network methods in the Schrödinger picture, even at fairly large bond dimensions, while the experiments produced data points that were within theoretical bounds. These experiments were enabled, in part, by a 10x reduction in median 2Q error rates from the utility experiment — now at 0.101% in simultaneous operation across the layout! Thanks to our collaborators at Algorithmiq, Simons Foundation Flatiron Institute. We shared these results in the new open community Quantum advantage tracker (https://lnkd.in/eG6Ue3sg), that includes the theoretical background for the experiment, classical simulation and experimental details, run-times, open-source code, etc. This tracks progress towards observable estimation with rigorous error bounds, ground state problems with variational solutions, and problems with efficient classical verification, and also invites proposals for new advantage candidates! Looking forward to sharing upcoming results from experiments and simulations, as they roll in, in this new open "lab notebook". I hope this accelerates the feedback loop between quantum experiments and classical simulation, without boundaries, and ultimately advances the pace of scientific discovery.

⚛️ Sequential Quantum Computing 📑 We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome the limitations of each type of quantum computer by combining their complementary strengths. Current quantum devices, including analog quantum annealers and digital quantum processors, offer distinct advantages, yet face significant practical constraints when individually used. SQC addresses this by efficient inter-processor transfer of information through bias fields. Consequently, measurement outcomes from one quantum processor are encoded in the initial-state preparation of the subsequent quantum computer. We experimentally validate SQC by solving a combinatorial optimization problem with interactions up to three-body terms. A D-Wave quantum annealer utilizing 678 qubits approximately solves the problem, and an IBM’s 156-qubit digital quantum processor subsequently refines the obtained solutions. This is possible via the digital introduction of non-stoquastic counterdiabatic terms unavailable to the analog quantum annealer. The experiment shows a substantial reduction in computational resources and improvement in the quality of the solution compared to the standalone operations of the individual quantum processors. These results highlight SQC as a powerful and versatile approach for addressing complex combinatorial optimization problems, with potential applications in quantum simulation of many-body systems, quantum chemistry, among others. ℹ️ Romero et al - 2025

By driving a quantum processor with laser pulses arranged according to the Fibonacci sequence, physicists observed the emergence of an entirely new phase of matter—one that displays extraordinary stability in a domain where fragility is the norm. Quantum computers operate using qubits, which differ radically from classical bits. A qubit can exist in superposition, occupying multiple states at once, and can become entangled with others across space. These properties enable immense computational power, but they come with a cost: quantum states are notoriously short-lived. Environmental noise, microscopic imperfections, and edge effects rapidly degrade coherence, limiting how long quantum information can survive. Seeking a new way to protect fragile quantum states, scientists at the Flatiron Institute, instead of applying laser pulses at regular intervals, they used a rhythm governed by the Fibonacci sequence—an ordered but non-repeating pattern long known to appear in biological growth, crystal structures, and wave interference. The experiment was carried out on a chain of ten trapped-ion qubits, driven by precisely timed laser pulses. The result was the formation of what is described as a time quasicrystal. Unlike ordinary crystals, which repeat periodically in space, a time quasicrystal exhibits structure in time without repeating in a simple cycle. The Fibonacci-based driving created a temporal order that resisted disruption, allowing the quantum system to remain coherent far longer than expected. The improvement was significant. Under standard conditions, the quantum state persisted for roughly 1.5 seconds. When driven by the Fibonacci pulse sequence, coherence times stretched to approximately 5.5 seconds—more than a threefold increase. Even more intriguing was the system’s temporal behavior. Measurements indicated that the quantum dynamics unfolded as if time itself possessed two independent structural directions. This does not imply time flowing backward, but rather that the system’s evolution followed two intertwined temporal pathways—an emergent property arising purely from the Fibonacci drive. The researchers propose that the non-repeating structure of the Fibonacci sequence suppresses errors that typically accumulate at the boundaries of quantum systems. By distributing disturbances in a highly ordered yet aperiodic way, the sequence stabilizes the collective behavior of the qubits. In effect, a mathematical pattern found throughout nature acts as a self-organizing error-management protocol. The findings suggest a powerful new strategy for quantum control. Rather than fighting noise solely with complex correction algorithms, future quantum technologies may harness structured patterns—drawn from mathematics and natural order—to achieve resilience at a fundamental level. https://lnkd.in/dVxp7R8J https://lnkd.in/dDVNRsPk

GEOMETRY OF MATTER CONTROLS THE QUANTUM TIMESCALE The longstanding question of how long a quantum transition actually takes has moved to experimentally accessible physics. In the attosecond regime, the transition from an initial bound state to a final photoelectron state is governed not by an external clock but by the internal phase evolution of the electronic wavefunction. The EPFL study demonstrates that this timescale is not universal: it is a symmetry‑dependent property of the material’s electronic structure. The key advance is the use of spin‑ and angle‑resolved photoemission (SARPES) to extract the EWS delay directly from the spin texture of the emitted electrons. In systems with strong spin–orbit coupling, multiple partial waves contribute to the photoemission amplitude. Their interference generates a measurable spin polarization even in nonmagnetic crystals under linearly polarized excitation. Because the spin vector is locked to the relative phase between these channels, it becomes an intrinsic probe of the accumulated phase—and therefore of the transition time—without perturbing the system with an external streaking field. Applying this method across materials of different dimensionality reveals a robust inverse relationship between spatial symmetry and quantum transition time. In 3D Cu with high cubic symmetry, the EWS delay approaches the lower theoretical bound (~26 as). In quasi‑2D TiSe₂ and TiTe₂, the delay increases to ~150 as, independent of correlation strength. This monotonic increase cannot be attributed to electron–electron interactions; instead, it reflects the reduction in the number of symmetry‑allowed propagation channels. The physical picture is that the excited electron occupies a quasi‑stationary state whose lifetime is determined by the density and symmetry of available decay pathways. High‑symmetry 3D lattices support many equivalent channels for constructive interference, enabling rapid phase accumulation and fast emission. As dimensionality is reduced, the Hilbert space of allowed momenta contracts, forcing the electron to undergo a more complex internal phase evolution before escape. The result is a geometry‑induced temporal bottleneck. These findings have several implications for condensed‑matter physics. First, they establish time as a symmetry‑controlled material parameter, not a universal constant of the photoemission process. Second, they impose fundamental constraints on petahertz‑scale electronics, where low‑dimensional nanostructures—despite their technological appeal—will exhibit intrinsically longer response times. Third, the spin‑interference method provides a new route to attosecond‑scale dynamics in systems where strong external fields would destroy fragile quantum phases, including correlated materials and topological states. The results show that spatial symmetry and temporal evolution are deeply entangled. In quantum materials, the structure of space dictates the flow of time.