Quantum Materials & Devices: Hardware Components & Fabrication
Quantum materials news: quantum device fabrication, superconductors, quantum dots, 2D materials. Quantum hardware components & substrates.
Quantum materials and devices form the foundational hardware layer enabling all quantum technologies, requiring specialized materials with precise quantum properties including superconductors, topological insulators, 2D materials like graphene, and semiconductor heterostructures for qubit fabrication.
India's Quantum Materials and Devices Initiatives
India's National Quantum Mission includes Quantum Materials & Devices as the fourth thematic vertical with dedicated funding. The QMD Tech Foundation at IIT Delhi serves as the Thematic Hub on Quantum Materials and Devices, established under the T-Hub framework of NQM. The hub focuses on developing indigenous materials for quantum technologies including substrates for superconducting circuits, quantum dots for spin qubits, and specialized semiconductors.
The ₹720 crore investment for quantum fabrication facilities announced in November 2025 supports this vertical, with facilities at: IISc Bengaluru: Quantum computing fabrication for superconducting, photonic, and spin qubits (3-5 qubits per chip initially, scaling to 20-100 qubits); IIT Bombay: Quantum sensing and device fabrication; IIT Delhi: Quantum materials and packaging; IIT Kanpur: Smaller facility for specialized devices.
The Indian Institute of Technology Madras Centre for Quantum Information, Communication and Computing (CQuICC) houses India's first remotely accessible semiconductor qubit facility, capable of fabricating 3-5 qubit chips per run with 95% device yield.
Research Areas: Superconducting materials: Niobium and aluminum thin films for Josephson junctions; Semiconductor quantum dots: Silicon and III-V materials for spin qubits; 2D materials: Graphene, transition metal dichalcogenides for novel qubit designs; Topological materials: Research into materials exhibiting Majorana zero modes; Photonic materials: Silicon photonics, nonlinear optical crystals for quantum light sources.
The Defence Research and Development Organisation (DRDO) develops quantum materials for defense applications including secure communications and sensing. The Department of Atomic Energy (RRCAT, Indore) provides specialized laser and materials processing capabilities for quantum device fabrication. The NQM targets developing superconductors, novel semiconductor structures, and quantum materials for memory and device fabrication as key deliverables within the 8-year mission timeline.
quantum-computingCould Investing $10,000 in IonQ Make You a Millionaire?
IonQ is one of the most popular quantum computing stocks.Quantum computing is the next big technology that's expected to follow AI. While the AI build-out is in full swing, so is the quantum computing arms race. Every company competing in the quantum computing realm is racing toward one goal: accuracy. That's the one hold-up with quantum technology right now, as its solutions aren't accurate enough to be deployed in a commercial setting. If that's the primary issue that every company is trying to solve, then a logical investment idea is to pick the current leader. Right now, that's IonQ (IONQ 4.58%). IonQ has made some structural decisions that optimize its technology for accuracy, which is why it's a leader in the space. But will those decisions be enough to transform $10,000 into $1 million? Image source: Getty Images. IonQ holds a commanding lead IonQ's latest measure of accuracy was in October 2025, when it delivered 99.99% two-qubit gate fidelity. This metric has become the industry standard in quantum computing and can be utilized when comparing systems from different providers. It measures if a calculation is still correct after passing through two processing "gates," and a 99.99% score indicates one error out of every 10,000 operations. While you and I would be incredibly happy only making one error in every 10,000 decisions we make, that's not good enough for a computer. Basic processes require thousands of operations, and if one error happens, it can propagate to ruin the entire system. That's why accuracy is so important, and with the rest of the quantum computing competition not yet reaching the 99.99% accuracy threshold, IonQ has a decent lead. ExpandNYSE: IONQIonQToday's Change(-4.58%) $-1.53Current Price$31.90Key Data PointsMarket Cap$11BDay's Range$31.38 - $33.8852wk Range$17.88 - $84.64Volume16MAvg Vol20MGross Margin-747.41% But will this be enough to fend off competitors? That's an impossible question to answer. There are several other viable quant
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quantum-computingHow to improve the performance of qubits: Super-fast fluctuation detection achieved
Using commercially available technology and innovative methods, researchers at NBI have pushed the limits of how fast you can detect changes in the sensitive quantum states in the qubit. Their work allows researchers to follow rapid changes in qubit performance that were previously invisible. The study is published in the journal Physical Review X.
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quantum-computingClarification of “academic relevance”
Hi community, I’m reaching out to better understand the removal of my recent post regarding the quantum computer hardware replica I designed and built for a local university. It was removed for "not being related to the academics of quantum computing," and I’m hoping for some clarity on that criteria. To provide context: this wasn’t a fan-art project. This was a commissioned educational tool built specifically for a university’s quantum computing department. The "cooling tower" (dilution refrigerator) architecture is fundamental to how superconducting qubits function; without that specific hardware environment, the "academics" of the math and logic don't translate to reality. My post aimed to show the hardware side of the field, specifically how universities are using physical models to teach students about: Cryogenic environments and the stages of cooling. Signal routing and the physical constraints of wiring a quantum processor. Scaling challenges in hardware design. If a project commissioned by a university for the express purpose of departmental education doesn’t qualify as "academic," could you please clarify what does? Is the sub restricted strictly to theoretical papers, or is there room for the physical engineering and pedagogical tools that make the science accessible? I’d love to find a way to share this that fits your guidelines, as the intersection of hardware engineering and education is a vital part of the field. submitted by /u/StarsapBill [link] [comments]
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quantum-computing2 Quantum Computing Stocks That Could Make a Millionaire
Quantum computing is still a high-risk frontier, but for patient investors, these two tickers could be tomorrow's generational wealth creators.Quantum computing is still early, messy, and wildly speculative, which is exactly why the upside for patient, risk‑tolerant investors is so intriguing. If this technology can cross the chasm from lab curiosity to everyday infrastructure over the next 10–20 years, today's niche players could look like buying early cloud or GPU leaders before the world catches on. Here are two quantum names with very different approaches that could, in a bullish scenario, move the needle on lifetime wealth and eventually produce some millionaire investors. Image source: Getty Images. 1. IonQ IonQ (IONQ 4.52%) remains the poster child for pure‑play, gate‑based quantum hardware. This month, the company reiterated that its systems are already accessible via major public clouds and are being used by customers in pharmaceuticals, materials, finance, logistics, cybersecurity, and government work. What makes IonQ interesting from a millionaire‑maker perspective is the combination of three things: A credible technical roadmap (including industry‑leading error rates on key two‑qubit gates). Distribution through hyperscale clouds that can switch on demand when the economics make sense. Early‑stage real workloads and partnerships rather than purely academic demos. In other words, IonQ looks like a potential millionaire maker because it has a real technical edge, major cloud distribution, and early partnerships, proving it's moving beyond lab demos into real-world use. ExpandNYSE: IONQIonQToday's Change(-4.52%) $-1.51Current Price$31.92Key Data PointsMarket Cap$11BDay's Range$31.37 - $33.8852wk Range$17.88 - $84.64Volume679KAvg Vol20MGross Margin-747.41% 2. Rigetti Computing Where IonQ leans into trapped ions, Rigetti (RGTI 4.07%) is the scrappy superconducting challenger aiming to sell both cloud access and physical systems. In January, the company updat
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quantum-computingGuest Post — Helium-Free Magnetic Refrigeration Supports Continuous Milli-Kelvin Temperatures For Quantum Research
Guest Post by by Jim McMahon Cryogenic characterization is a must to accelerate and enable breakthrough science and quantum technologies. Quantum sensors, quantum communication devices and future quantum computers will rely on scalable and efficient cooling for their operation. Quantum computers rely on qubits, which can exist in multiple states simultaneously. These quantum states are extremely […]
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quantum-computingTraining-free stability metric validated on 445 qubits across 3 IBM backends — 83% error reduction
I'm an independent researcher. I developed a single stability metric Φ = I×ρ - α×S that flags degrading qubits before they fail — no ML training, no per-backend tuning. Tested on ibm_fez, ibm_torino, and ibm_marrakesh: - 445 qubits analyzed, r = 0.9458 correlation with T2/T1 - 83% error reduction using Φ-based qubit selection - 8-18x discrimination between low-Φ and high-Φ qubits across 10-500 gate depths - 20 days early warning before qubit degradation - All 5 dead qubits correctly identified (Φ < 0) - Works across all 3 backends with zero recalibration Same formula also validates on neural networks (660+ architectures), mechanical bearings, turbofan engines, and cardiac arrhythmia — same threshold, same constants. All real hardware data. No synthetic. Code is public. Repo: https://github.com/Wise314/quantum-phi-validation Paper: https://doi.org/10.5281/zenodo.18522745 Cross-domain paper: https://doi.org/10.5281/zenodo.18523292 Happy to answer any questions about the methodology. submitted by /u/Intrepid-Water8672 [link] [comments]
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quantum-computingIll be presenting "A Clean 2D Floquet Logical Qubit from a Purely Imaginary Phase Drive" at QCNC2026, got relegated to a workshop.
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quantum-computingQuantum computing: Tracking qubit fluctuations in real time - Digital Journal
Quantum computing: Tracking qubit fluctuations in real time Digital Journal
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quantum-computingI achieved 97.8% average error recovery on IBM Quantum Torino hardware using classical post-processing. No calibration, no ancilla qubits, no hardware mods. Paper and data inside.
I'm an independent researcher and I've developed a new approach to quantum error mitigation. The core idea: quantum decoherence acts as a diffusion process on measurement probability distributions, and you can reverse it using Richardson-Lucy deconvolution with self-calibrating asymmetric noise estimation. Results on IBM Quantum Torino (real hardware, 20,000 shots per circuit): GHZ 4 qubits: 100% recovery GHZ 8 qubits: 99.7% recovery GHZ 12 qubits: 99.8% recovery W-state 3 qubits: 99.8% recovery Bernstein-Vazirani 5 qubits: 87.6% recovery 3 Bell pairs 6 qubits: 99.6% recovery Average: 97.8% fidelity recovery across all circuits. The method self-calibrates from measurement data alone. Zero calibration circuits. Zero ancilla qubits. Runs in under 1 second on a laptop. Full paper with theory, math, and all experimental results: https://zenodo.org/records/18724718 Patent pending. Happy to answer questions and discuss. Also looking for an arXiv quant-ph endorsement if anyone is willing. Open to feedback and criticism. I want to learn as much as I can from this community. submitted by /u/nicazecenzo [link] [comments]
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quantum-computingMerLin: Framework for Differentiable Photonic Quantum Machine Learning
MerLin: Framework for Differentiable Photonic Quantum Machine Learning MerLin 0.3 is an open-source framework developed by Quandela for the systematic exploration of photonic and hybrid quantum machine learning (QML). Built on the Perceval SDK, it utilizes Strong Linear Optical Simulation (SLOS) to perform exact quantum state computation within a PyTorch-native environment. The architecture is centered on the QuantumLayer, a torch.nn.Module that enables end-to-end differentiable training of linear-optical circuits. By precomputing sparse photon-number transition graphs, the framework accelerates gradient-based optimization of circuit parameters, such as phase shifters and beam-splitters, directly within standard classical AI pipelines. The framework supports multiple data encoding methodologies, including angle encoding for Fourier-like feature mapping and amplitude encoding for state-vector initialization. A QuantumBridge abstraction allows for cross-paradigm architectural comparisons by mapping qubit-based gates into photonic dual-rail or QLOQ encodings. MerLin is designed for hardware-aware execution through the MerlinProcessor interface, which facilitates offloading hybrid model components to physical quantum processing units (QPUs), such as Quandela’s Belenos system. It also integrates noise models and detector-specific semantics—including photon-number-resolving and threshold detectors—allowing researchers to simulate hardware constraints during the training phase. To address reproducibility challenges in QML, MerLin includes a library of 18 reproduced state-of-the-art papers spanning quantum kernels, reservoir computing, and convolutional architectures. These modular experiments provide standardized baselines for comparing photonic and gate-based modalities under unified conditions. Technical insights from these reproductions indicate that expressivity in photonic variational quantum circuits (VQCs) scales linearly with the number of input photons without inc
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quantum-computingFastest Change in Physics Limited by Planck Time
Scientists have long sought to understand the minimum time required for a system to reach local thermal equilibrium. Marvin Qi from the Leinweber Institute for Theoretical Physics & James Franck Institute, University of Chicago, and Alexey Milekhin from the Department of Physics and Astronomy, University of Kentucky, alongside Luca Delacr etaz from the Leinweber Institute for Theoretical Physics & James Franck Institute, University of Chicago, demonstrate a rigorous lower bound on this ‘equilibration time’, conjecturing it is fundamentally limited by the Planckian time. Their research establishes this bound by analysing the emergence of hydrodynamic behaviour in conserved densities, revealing a dimensionless coefficient dependent only on dimensionality and the type of behaviour, irrespective of the underlying thermalisation mechanism. This universally applicable result, achieved through careful consideration of real-time thermal correlators, offers significant insight into the foundations of statistical mechanics and applies to a broad range of physical systems, even those lacking a quasiparticle description or exhibiting inelastic scattering. Within a cryostat chilled to near absolute zero, delicate measurements track how quickly order arises from chaos. This pursuit reveals a fundamental limit to how rapidly any physical system can reach stability. The universal timescale, linked to the very fabric of spacetime, governs the emergence of predictable behaviour in everything from fluids to quantum materials. Scientists have long recognised the importance of the Planckian timescale, ħ/T, in quantum statistical physics — recent attention focuses on a compelling conjecture: that this timescale fundamentally limits how quickly quantum many-body systems reach local equilibrium. With a local equilibration time τeq greater than or equal to the Planckian time, and scientists have now moved beyond theoretical motivation to formally establish this bound. Defining τeq a
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quantum-computingAI Spots New Electron Crystal Within Graphene Layers
Scientists have uncovered a novel ground state of matter within artificial graphene, revealing a paired Wigner crystal formed through an unexpected self-assembly process. Conor Smith from the Center for Computational Quantum Physics at the Flatiron Institute and the Department of Electrical and Computer Engineering at the University of New Mexico, alongside Yubo Yang from the Center for Computational Quantum Physics, Flatiron Institute and the Department of Physics and Astronomy at Hofstra University, Zhou-Quan Wan, Yixiao Chen from ByteDance, Miguel A. Morales from the Center for Computational Quantum Physics, Flatiron Institute and the Department of Physics at the University of Toronto, and Shiwei Zhang utilised a neural-network-based Monte Carlo approach to identify this state in a two-dimensional electron gas subjected to a honeycomb moiré potential. This research demonstrates the spontaneous formation of molecules comprising paired electrons, which then organise into a Wigner crystal without any external guiding potential or attractive forces, offering a compelling example of emergent collective behaviour and opening avenues for the design of materials with unique electronic characteristics. For decades, physicists have sought to understand how electrons arrange themselves in complex materials. Now, an artificial graphene system reveals an unexpected, self-organised pattern where electrons pair up and form crystalline structures, offering a fresh perspective on collective electron behaviour and potential control over material properties. Scientists are increasingly focused on moiré systems as tunable platforms for investigating quantum matter — these artificially created structures, arising from the interference of two overlaid lattices, have already exhibited a range of exotic states. Prompting considerable research across experimental and theoretical physics. This new state emerges at a specific filling factor, where one electron occupies every four minima wi
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quantum-computingCryogenic Chip Calibrates Microwave Signals in Milliseconds
Researchers have developed a novel method for the accurate calibration of microwave attenuation and gain, crucial for the performance of sensitive superconducting circuits. Thomas Descamps and Linus Andersson, leading the work at Chalmers University of Technology alongside colleagues Vittorio Buccheri, Simon Sundelin, Mohammed Ali Aamir, and Simone Gasparinetti, have demonstrated a compact, self-calibrating cryogenic noise source. This device, integrating an on-chip chromium attenuator directly into a coaxial microwave line, allows for in situ determination of attenuation and gain without prior knowledge of the attenuator temperature. The significance of this research lies in its ability to provide a simple and accurate characterisation technique for near quantum-limited parametric amplifiers, vital components in superconducting-qubit readout systems. Until recently, verifying the delicate balance of signals in quantum processors proved exceptionally difficult. Now, a miniature, self-checking device allows precise tuning of the components that read information from these systems, promising more reliable and powerful quantum computers. Scientists developing advanced superconducting quantum circuits require precise calibration of microwave signals at extremely low temperatures. Accurate measurement of both attenuation and amplification chain noise is essential for interpreting experimental results and characterising amplifier performance. Scientists have created a compact, self-calibrating cryogenic noise source integrated directly into the microwave line at the mixing-chamber stage of a dilution refrigerator. This new device utilizes an on-chip chromium attenuator. This can be heated with remarkably low power levels, on the order of nanowatts. By comparing the Johnson-Nyquist noise generated through both direct current (Joule) heating and microwave power dissipation within the attenuator. The attenuation of the input microwave line is determined. Crucially, this meth
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quantum-computingQuantum Systems Linked with Near-Perfect Data Transfer
Scientists are continually striving to improve the efficiency of quantum teleportation, a process vital for secure quantum communication and computation. Ravi Kamal Pandey from the Department of Physics, Institute of Science, Banaras Hindu University, and Shraddha Singh from Nehru Gram Bharti (Deemed to be University), working with Dhiraj Yadav from IILM University and Devendra Kumar Mishra from Banaras Hindu University, have demonstrated a significant advance in this field. Their research details a method for achieving near-perfect quantum teleportation between distinct types of quantum encoding, discrete and continuous variables, utilising a hybrid entangled resource. This is particularly noteworthy as teleportation from discrete to continuous variables has historically been less efficient than the reverse process, and this new approach, employing cross-Kerr nonlinearity and linear optical components, overcomes this limitation, potentially paving the way for more robust and versatile quantum networks. For decades, fully realising the potential of quantum communication has been hampered by the difficulty of transferring information between different types of quantum systems. Now, a method achieving near-perfect teleportation between distinct quantum encodings offers a major step forward, potentially unlocking more flexible and powerful quantum networks. Scientists are increasingly focused on the reliable transmission of quantum information, a field with implications for secure communication and advanced computation. Quantum teleportation, a process of transferring quantum states, offers a potential solution, yet achieving perfect state transfer remains a significant challenge. A qubit, the basic unit of quantum information, can be encoded in the polarization of a single photon (discrete-variable or DV) or in the superposition of phase-opposite coherent states of an optical field (continuous-variable or CV). DV systems, while convenient, are more susceptible to sign
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quantum-computingQuantum Computers Tackle Complex Drone Delivery Schedules
Scientists are increasingly exploring quantum computing to solve complex logistical challenges, and this research details a novel approach to the drone delivery packing problem. Sara Tarquini from Gran Sasso Science Institute, Matteo Vandelli and Francesco Ferrari from Quantum Computing Solutions, Leonardo S.p.A., alongside Daniele Dragoni working with colleagues at both Quantum Computing Solutions, Leonardo S.p.A. and the Hypercomputing Continuum Unit, Leonardo S.p.A., and Francesco Tudisco from Gran Sasso Science Institute and University of Edinburgh, present a hybrid quantum-classical framework utilising a neutral-atom quantum processing unit. They reformulate the delivery problem as a graph-partitioning task, leveraging the unique capabilities of neutral-atom quantum computers to encode constraints and efficiently explore potential solutions. This work is significant because it demonstrates the potential for quantum algorithms to optimise real-world delivery schedules, offering a pathway towards more efficient and scalable drone delivery networks, and showcases promising results from experiments conducted on up to 100 atoms on the Fresnel QPU. Solving complex delivery problems, such as optimising drone routes, could become far more efficient with this technology. This demonstration offers a practical application for emerging quantum processors, moving beyond theoretical possibilities. Researchers are applying the principles of quantum computing to a practical logistical challenge: optimising drone delivery routes. This work details a hybrid quantum-classical approach to the Drone Delivery Packing Problem, a complex task involving assigning deliveries to drones with limited battery life and time windows. By reformulating the problem as a graph partitioning exercise based on independent sets, the team successfully demonstrated a method for finding efficient delivery schedules. The core innovation lies in using a neutral-atom quantum processing unit (QPU) to genera
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quantum-computingNew Method Reveals Hidden Order in Complex Systems
Scientists have developed a novel spectroscopic technique, termed dissipative spectroscopy, to extract spectral information from complex systems by harnessing controlled dissipation. Xudong He and Yu Chen, from the University of Science and Technology of China, present this framework, establishing a general dissipative response applicable to both Markovian and non-Markovian environments. Their research details a protocol to access the dissipative spectrum through driven oscillation-dissipation resonance, revealing previously hidden signatures of critical behaviour and macroscopic order. This work is significant because it identifies two-particle soft modes near critical points and predicts power-law growth following a dissipation quench, even in quasiparticle-dominant regimes often dismissed as trivial. By introducing extended dissipative susceptibilities and demonstrating their utility in a fermionic model, the authors offer a versatile tool for probing both equilibrium properties and predicting non-equilibrium dissipative dynamics. Scientists have devised a novel technique for understanding complex materials by carefully controlling how energy fades away within them. This method reveals hidden details about a material’s behaviour, even when traditional approaches fail to detect changes, and promises a fresh perspective on predicting how systems evolve and respond to external stimuli. This work introduces dissipative spectroscopy, a technique that extracts spectral information from quantum materials through controlled dissipation, opening avenues to study phenomena previously hidden from view. The research details how this approach can identify subtle changes within materials near critical points, moments of dramatic transformation, and even predict the emergence of order in seemingly disordered systems. Probing quantum dynamics often requires distinguishing between external influences and inherent noise. Equipped with recent advances in dissipation engineering, re
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quantum-computingMachine Learning Clarifies Elusive Quantum States in Material
Scientists continue to pursue the definitive identification of Majorana zero modes (MZMs) within topological superconductors, a pursuit complicated by overlapping spectral features that mimic genuine MZM signals. Jewook Park and Hoyeon Jeon, both from the Center for Nanophase Materials Science at Oak Ridge National Laboratory, alongside Dongwon Shin from the Materials Sciences and Technology Division at the same institution, have led a study employing a novel machine-learning approach to address this challenge. Working with colleagues including Guannan Zhang from the Computer Science and Mathematics Division, Michael A McGuire and Brian C Sales from the Materials Sciences and Technology Division, and An-Ping Li, the team developed a data-driven workflow for analysing tunneling spectroscopy data from the intrinsic topological superconductor FeTe0.55Se0.45. This research is significant because it introduces an objective and reproducible method for distinguishing true MZMs from trivial in-gap states, offering a crucial step towards reliable detection and eventual manipulation of these exotic states for potential quantum computation applications. Scientists are edging closer to realising the potential of quantum computing with a new technique for identifying elusive quantum particles. The method overcomes a major hurdle in materials science by reliably distinguishing genuine quantum signals from misleading background noise, promising to accelerate the development of stable and scalable quantum technologies. Researchers are developing a new method to reliably identify Majorana zero modes within topological superconductors, a critical step towards building more stable quantum computers. Identifying these quasiparticles has proven difficult because their signatures, zero-bias conductance peaks, can be mimicked by other, non-topological phenomena within the material. The team demonstrated a data-driven workflow integrating detailed spectral analysis with machine learning to
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quantum-computingTwisted Material Hosts Topological Superconductivity and Vortices
Researchers are increasingly focused on understanding the interplay between superconductivity and the fractional quantum anomalous Hall (FQAH) effect in twisted materials. Daniele Guerci, Ahmed Abouelkomsan, and Liang Fu, all from the Department of Physics at the Massachusetts Institute of Technology, demonstrate that the superconducting state observed in twisted MoTe₂ is a chiral p-wave superconductor hosting an array of vortices. These vortices are induced by an emergent magnetic field within the moiré superlattice, resulting in a topological superconducting vortex lattice state with a Chern number of one. This work offers a unified understanding of both FQAH and topological superconductivity, potentially paving the way for novel electronic devices and a deeper comprehension of correlated electron systems. Recent observations in twisted molybdenum ditelluride (MoTe₂) revealed the simultaneous presence of superconductivity and the fractional quantum anomalous Hall effect (FQAH), prompting a detailed theoretical investigation into their underlying connection. The arrangement of electrons within the material creates a unique, ordered structure with implications for future electronic devices. Scientists have uncovered a surprising link between these two distinct quantum phenomena. This work demonstrates that the superconducting state emerging in these materials is not conventional, but a chiral f-wave superconductor hosting a unique array of vortices, each carrying twice the usual quantum of magnetic flux. These vortices, induced by an emergent magnetic field arising from the material’s layered structure, form a topological vortex lattice with a Chern number of -1/2, directly resulting in a half-integer thermal Hall conductance. The research establishes a unified framework explaining both phenomena, controlled by the spatial variation of this emergent magnetic field. Unlike traditional superconductivity induced by external magnetic fields, this system’s superconductiv
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quantum-computingPhoenix and Quantum Technology: Arizona’s Industrial Bet on the Quantum Economy
Insider Brief Officials, investors, manufacturers and researchers met in Phoenix to assess how the region could build a manufacturing-centered quantum ecosystem, signaling a shift in focus from research breakthroughs to long-term system production. Discussions highlighted Arizona’s expanding semiconductor and advanced materials base — including epitaxial wafer manufacturing and photonic chip fabrication at ASU Research Park — as foundational infrastructure for future quantum hardware supply chains. Participants framed Phoenix as entering a preparatory phase similar to early aerospace and semiconductor hubs, positioning the region to support large-scale deployment and trusted manufacturing once quantum technologies mature. Image: Lawrence Semiconductor process engineer inspecting an isotopically enriched silicon-28 epitaxial wafer produced at the company’s Tempe, Arizona facility. The company’s capabilities support low-defect, spin-coherent materials platforms for silicon spin-qubit research and quantum device development. Over two days in Phoenix this week, local officials, manufacturers, researchers, international partners and representatives from the U.S. Air Force met across a series of roundtables and meetings to discuss what it would take to build a regional quantum ecosystem. The visit, led by Matt Cimaglia, founder and managing partner of Quantum Coast Capital, and senior advisor Dan Hart, included discussions at the Greater Phoenix Economic Council and concluded with remarks at the Phoenix Sister Cities annual Global Links Business Luncheon. The conversations frequently returned to a comparison that has begun surfacing in policy circles: the early space industry and the emerging quantum technology sector may follow similar geographic patterns. Matt Cimaglia, left, and Dan Hart, right, speak during the Phoenix Sister Cities Global Links Business Luncheon at Monroe Street Abbey on Feb. 19, 2026, in downtown Phoenix. The implication is less about where breakthr
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quantum-computingSuperconductor Effect Lost in Stages, Not All at Once
Researchers are investigating the behaviour of superconductivity in bilayer materials, revealing a surprising sequence of events leading to the loss of key quantum properties. F. Yang, C. Y. Dong, and Joshua A. Robinson from the Department of Materials Science and Engineering and Materials Research Institute at The Pennsylvania State University, working with L. Q. Chen, demonstrate that the Josephson diode effect, a form of nonreciprocal current flow, disappears at a lower temperature than complete superconducting coherence. This challenges the established understanding that both effects vanish simultaneously. Their self-consistent microscopic theory, incorporating phase fluctuations, shows a hierarchy of thermal crossovers, progressing from a nonreciprocal to a reciprocal and finally an incoherent Josephson regime before the superconducting gap closes. Significantly, this research highlights the sensitivity of these transitions to factors like interlayer coupling, in-plane disorder, and carrier density, offering insights relevant to layered superconductors such as cuprates and nickelates, and potentially advancing the development of superconducting devices. Imagine building a delicate house of cards, where even the slightest tremor can cause it to collapse. Similarly, maintaining the flow of supercurrent in advanced materials requires shielding it from disruptive thermal vibrations. New work reveals how this delicate balance breaks down in layered superconductors, with specific components failing at different temperatures before complete loss of conductivity. Scientists have long understood that superconductivity, the lossless flow of electricity, relies on the delicate coherence of electrons forming Cooper pairs. Recent investigations into superconducting diodes, devices exhibiting a directional preference for current flow, have revealed a surprising complexity in how this coherence breaks down within layered superconductors. Contrary to expectations of a simultan
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