Quantum Error Correction: Surface Code & Fault-Tolerant Computing
Quantum error correction news: logical qubits, surface code, fault-tolerant quantum computing, QEC. Error mitigation & suppression.
Quantum error correction (QEC) is the critical enabler for fault-tolerant quantum computing, protecting quantum information from environmental noise through redundant encoding across multiple physical qubits. Recent breakthroughs demonstrated below-threshold error correction where logical qubit error rates fall below physical qubit rates.
The 2D surface code is the leading QEC approach due to high error threshold (~1%), local nearest-neighbor interactions, and compatibility with superconducting chip designs. Recent breakthroughs include Google's Willow demonstrating below-threshold surface code scaling, and IBM's Heavy Hex optimizing qubit connectivity for surface code implementation.
India's Quantum Error Correction Research
India's National Quantum Mission includes quantum error correction in its basic science research component. The Foundation for QC Innovation at IISc Bengaluru addresses error correction as part of its quantum computing development. The Harish-Chandra Research Institute (HRI) and Institute of Mathematical Sciences (IMSc) conduct theoretical research on quantum error correction codes.
The NQM targets developing intermediate-scale quantum computers with 50-1000 physical qubits, requiring error mitigation and eventually error correction to achieve quantum advantage. The mission includes development of indigenous control electronics and error mitigation techniques.
quantum-computingAustralian Government Allocates $12.7M AUD ($9M USD) to Industrialize Quantum Prototypes
Australian Government Allocates $12.7M AUD ($9M USD) to Industrialize Quantum Prototypes The Australian Government has finalized $12.7 million AUD ($9 million USD) in funding for eight projects under Stage 2 of the Critical Technologies Challenge Program (CTCP). This funding round supports the transition of quantum-based solutions from feasibility studies to proof-of-concept demonstrations. Aligned with the National Quantum Strategy, these grants aim to catalyze the industrialization of quantum hardware and software across sectors of national significance, including energy, resources, and healthcare. The program follows a dual-stage structure where participants receive up to $5 million to build working prototypes capable of operating in relevant environments. In the resource exploration sector, Loughan Technology Group Pty Limited received $2.4 million to develop a quantum optical sensor for the real-time detection of rare-earth elements in clay-hosted deposits. This project, partnered with ABx Group Limited, Australian Rare Earths Limited, and The University of Adelaide, utilizes Quantum Novel Fluorescence Analysis (Q-NFA) to quantify economically recoverable minerals. Simultaneously, Orica Australia Pty Ltd was awarded $2.3 million to integrate quantum opto-mechanical sensors into through-earth communications. Collaborating with the Department of Defence, Syndetic Pty Ltd, and The University of Queensland, the initiative focuses on detecting weak magnetic signals to enhance wireless initiating systems in harsh mining environments. Energy optimization projects include a $1.1 million grant to La Trobe University for the development of a hybrid quantum-classical optimization system for data center cooling. The system utilizes the Quantum Walk-Assisted Optimisation Algorithm (QWOA) in partnership with AQ Intelligence Pty Ltd, Fujitsu Australia Ltd, NEXTDC Limited, and the University of Western Australia to reduce operational energy consumption. Additionally, Flinders
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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-computingWhat happens during a 1 week lab visit before a short research contract and possible longer contract?
I recently graduated with Bachelor's in Electrical Engineering and have been invited to visit a professor’s semiconductor quantum computing lab for 1 week. This may lead to a 3 month research contract and possible separate 1 year if things go well. I want to understand what to expect during this. Is a 1 week visit usually an evaluation or just orientation/mutual fit? What do professors typically expect from you during such a short visit? Any tips to make a good impression. Would appreciate any insights. Thanks. submitted by /u/whyami_2025 [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-computingGoogle Gave Amazing News to Nvidia and Broadcom Stock Investors
By Jose Najarro – Feb 21, 2026 at 8:15PM ESTNASDAQ: GOOGLAlphabetMarket Cap$3.8TToday's Changeangle-down(3.95%) $11.96Current Price$314.81Price as of February 20, 2026 at 3:58 PM ETAlphabet reported massive Capex growth driven by AI demand.In today's video, I discuss recent updates affecting Alphabet (GOOGL +3.95%) (GOOG +3.66%) and other AI stocks. To learn more, check out the short video, consider subscribing, and click the special offer link below. *Stock prices used were the after-market prices of Feb. 4, 2026. The video was published on Feb. 4, 2026. Read NextFeb 20, 2026 •By Eric TrieStock Market Today, Feb. 20: Alphabet Jumps as Gemini Rollout Bolsters $185B AI BuildoutFeb 20, 2026 •By Patrick SandersBetter Artificial Intelligence Stock: Alphabet vs. AmazonFeb 20, 2026 •By Anders BylundPrediction: 2 Stocks That Should Be Worth More Than Nvidia 10 Years From NowFeb 20, 2026 •By Sean WilliamsBillionaire Stanley Druckenmiller Dumped 4 of the Hottest AI Stocks and Nearly Quadrupled His Fund's Stake in Another Trillion-Dollar CompanyFeb 19, 2026 •By Johnny RiceHere's the Quantum Computing Stock Wall Street Loves the Most (Hint: It's Not IonQ or Rigetti)Feb 18, 2026 •By Daniel SparksAmazon vs. Alphabet: Which Is the Better AI Stock to Buy Now?About the AuthorJose Najarro enjoys investing in the tech market, more importantly, the semiconductor sector. Before partnering with the Fool, Jose worked as a Senior Electrical Engineer for General Dynamics, where he had first-hand experience seeing how emerging technology can change the world. Jose Najarro went to NJIT, receiving his Bachelor's and Master's degree in Electrical Engineering.TMFJoseNajarroX@_JoseNajarroStocks MentionedAlphabetNASDAQ: GOOGL$314.81 (+3.95%) $+11.96BroadcomNASDAQ: AVGO$332.44 (0.46%) $1.55NvidiaNASDAQ: NVDA$189.82 (+1.02%) $+1.92AlphabetNASDAQ: GOOG$314.67 (+3.66%) $+11.11*Average returns of all recommendations since inception. Cost basis and return based on previous market day close.
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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-computingNew Quantum Algorithms Deliver Speed-Ups Without Sacrificing Predictability
Researchers have begun to systematically investigate pseudo-deterministic quantum algorithms, a novel class of quantum computation that consistently yields a canonical solution with high probability. Hugo Aaronson and Tom Gur from the University of Cambridge, working with Jiawei Li from UT Austin, present compelling evidence of their potential and limitations within the query complexity model. Their findings, detailed in a new paper, demonstrate significant complexity separations, including a problem where pseudo-deterministic quantum algorithms require substantially more queries than their classical randomised counterparts. This work is particularly significant as it establishes both the advantages, an exponential speed-up for certain problems, and the boundaries, a quintic advantage over deterministic algorithms, of this emerging computational paradigm, potentially reshaping our understanding of quantum algorithmic power. Problems currently intractable for even the most powerful computers could yield to a new class of quantum algorithms. These ‘pseudo-deterministic’ quantum methods find correct answers with high probability, offering speed-ups for specific calculations. Initial results demonstrate an exponential advantage over classical approaches for certain problems, such as Quantum-Locked Estimation. Meanwhile, remaining within a quintic limit for general computations. Scientists have begun a systematic investigation into pseudo-deterministic quantum algorithms, representing a unique intersection between the power of quantum mechanics and the reliability of deterministic computation. Recent work has focused on the query complexity model, revealing surprising separations in what these algorithms can achieve compared to classical counterparts. Understanding these differences has implications for the development of more dependable quantum technologies and a deeper understanding of the fundamental limits of computation. To establish clear boundaries between what qu
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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-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-computingQuantum Computing Weekly Round-Up: Week Ending February 21, 2026
Week Ending 2 | 21 | 2026. Packaging scales, networking demos go metro, and post-quantum risk gets legal and financial teeth. Add fresh funding moves, national programs, and a few “read this before it’s on every slide deck” lab results. Take a read in this week's round-up. The post Quantum Computing Weekly Round-Up: Week Ending February 21, 2026 appeared first on The Qubit Report.
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quantum-computingQuandela Unveils MerLin, Reproducing 18 State-of-the-Art Photonic QML Models
Quandela Quantique Inc. has unveiled MerLin, a new open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. Available as of February 11, 2026, MerLin integrates optimized quantum simulation into standard machine learning workflows, enabling the training of quantum layers and systematic benchmarking. As an initial demonstration, the framework successfully reproduces eighteen state-of-the-art photonic and hybrid QML models, spanning diverse architectures like kernel methods and convolutional networks. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for comparisons and hybrid workflows, “establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence.” This positions MerLin as a tool for linking algorithms, benchmarks, and future quantum hardware. Photonic Quantum Computing Advantages for Machine Learning Photonic quantum computing is proving particularly promising due to its scalability, robustness, compatibility with optical communication technologies, and energy efficiency. This convergence of quantum computing and machine learning is accelerating advances in both fields, with quantum machine learning (QML) offering the potential to extend the capabilities of classical algorithms. Unlike many approaches, photonic QML “exploits the bosonic nature of light and high-dimensional multi-mode interference to implement and train machine learning models directly on this unconventional photonic quantum computation model, enabling intrinsic parallelism and efficient exploration of large Hilbert spaces.” Realizing this potential necessitates software frameworks that bridge abstract QML models with execution on emerging quantum hardware. The need for such tools is highlighted by the current fragmented software landscape, where frameworks like Qiskit, Cirq, Puls
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