Quantum Machine Learning: QML Algorithms & Quantum AI Applications
Quantum machine learning news: QML algorithms, quantum AI, quantum neural networks. Hybrid quantum-classical ML & quantum advantage research.
Quantum machine learning (QML) explores intersections between quantum computing and artificial intelligence, investigating whether quantum algorithms can accelerate data analysis, pattern recognition, and model training beyond classical capabilities.
Theoretical foundations include quantum advantages for linear algebra subroutines central to machine learning—matrix inversion, principal component analysis, and vector inner products. The HHL algorithm promises exponential speedup for specific sparse, well-conditioned systems.
India's Quantum Machine Learning Landscape
India's National Quantum Mission supports quantum machine learning research through its Quantum Computing Thematic Hub at IISc Bengaluru. The Indian Institute of Science offers a Certificate Programme in Quantum Computing and Artificial Intelligence through its Centre for Continuing Education, providing comprehensive training in quantum AI applications with hands-on coding using Qiskit and PennyLane.
Tata Consultancy Services (TCS) develops quantum machine learning algorithms for enterprise applications. Infosys explores quantum AI through its Quantum Living Labs. IIT Delhi offers certification programs in quantum computing and machine learning in collaboration with industry partners.
The NQM targets developing quantum algorithms for optimization, simulation, and machine learning, with human resource development including training programs for quantum professionals.
Current NISQ-era QML relies on hybrid quantum-classical approaches including variational quantum algorithms, quantum neural networks, and quantum kernel methods. Challenges include "barren plateaus" in optimization landscapes limiting trainability, and limited qubit counts restricting model complexity.
quantum-computingStudy Finds Exponential Quantum Advantage in Machine Learning Tasks
Insider Brief Small quantum computers could process massive datasets more efficiently than far larger classical systems, according to a study recently posted on arXiv that outlines a path to exponential gains in machine learning and data analysis. The study, conducted by researchers from Caltech, Google Quantum AI, MIT and and Oratomic, reports that quantum systems […]
Quantum DailyLoading...0
quantum-computingClassical Data Limits Quantum Computing’s Broad Impact
Haimeng Zhao is addressing a fundamental hurdle preventing widespread adoption of quantum computing: efficiently integrating classical data into quantum algorithms. Despite advances in experimental capabilities, demonstrating broad societal impact beyond niche areas like quantum materials simulation and cryptanalysis remains a significant challenge, largely due to the difficulty of accessing real-world, classically-generated data in a quantum format, a problem known as the data loading problem. Their new framework, termed quantum oracle sketching, offers a solution by processing data as a continuous stream and applying small quantum rotations to incrementally build an accurate quantum oracle. “We live in an effectively classical world, dammit, and maybe classical computers and AI already suffice for most of our problems,” Zhao playfully suggests, adapting a famous quote from Richard Feynman, highlighting the need to bridge the gap between classical data and quantum processing. Data Loading Bottleneck Hinders Broad Quantum Advantage While quantum computers excel at simulating quantum materials and certain cryptographic tasks, these applications are inherently quantum or possess mathematical structures easily exploited by quantum algorithms; extending this advantage to everyday problems proves far more difficult. The core issue stems from the fact that most modern computation relies on processing vast amounts of noisy, classical data, the very fuel powering the success of machine learning and artificial intelligence. This data, originating from the macroscopic classical world, doesn’t naturally lend itself to the delicate, specialized structures quantum computers require. Imagine attempting to simultaneously read a million movie reviews; the conventional, sequential access of classical computers presents a bottleneck for quantum systems. To address this, Haimeng Zhao has developed a framework called “quantum oracle sketching,” which allows for optimal access to classi
Quantum ZeitgeistLoading...0
quantum-computingUniversity of Houston Hosts Quantum Symposium with Industry and IonQ
Insider Brief The University of Houston hosted a quantum symposium with IonQ and industry leaders as part of its Quantum Initiative to align research, workforce development, and industry collaboration. The initiative builds on a statewide effort to advance quantum computing, materials, networks, and workforce development while positioning UH as a regional innovation hub. Speakers highlighted a projected global shortage of quantum talent and emphasized the need for universities to scale education and training to meet industry demand. PRESS RELEASE — As part of its Quantum Initiative, the University of Houston convened global industry leader IonQ, national laboratory partners and energy executives for the symposium, “Powering the Future: Quantum Technologies in the Energy Economy,” advancing its efforts to align research, talent and industry collaboration in quantum technologies. The initiative builds on momentum from the Texas Quantum Summit, a statewide alliance where UH and seven other universities identified four strategic pillars shaping the field: quantum computing, quantum materials and devices, quantum networks and workforce development. UH’s Quantum Initiative aligns its expertise with these statewide and national priorities, positioning the institution as a primary engine for innovation in the region. “The University of Houston has long been recognized for its leadership in energy research and its deep partnerships with industry,” said Claudia Neuhauser, vice president and vice chancellor for research at UH. “As energy systems evolve to incorporate advanced computation, new materials and digital infrastructure, quantum technologies will become part of that future landscape.” Building a Workforce for a Rapidly Expanding Industry Industry leaders at the symposium emphasized the urgency of preparing talent at scale. Industry data from IonQ suggests the global quantum sector could require as many as 850,000 workers within the next decade; however, current projec
Quantum DailyLoading...0
quantum-computingA streamlined quantum algorithm for topological data analysis with exponentially fewer qubits
AbstractTopological invariants of a dataset, such as the number of holes that survive from one length scale to another (persistent Betti numbers) can be used to analyze and classify data in machine learning applications. We present an improved quantum algorithm for computing persistent Betti numbers, and provide an end-to-end complexity analysis. Our approach provides large polynomial time improvements, and an exponential space saving, over existing quantum algorithms. Subject to gap dependencies, our algorithm obtains an almost quintic speedup in the number of datapoints over previously known rigorous classical algorithms for computing the persistent Betti numbers to constant additive error – the salient task for applications. However, we also introduce a quantum-inspired classical power method with provable scaling only quadratically worse than the quantum algorithm. This gives a provable classical algorithm with scaling comparable to existing classical heuristics. We discuss whether quantum algorithms can achieve an exponential speedup for tasks of practical interest, as claimed previously. We conclude that there is currently no evidence for this being the case.► BibTeX data@article{McArdle2026streamlinedquantum, doi = {10.22331/q-2026-04-10-2058}, url = {https://doi.org/10.22331/q-2026-04-10-2058}, title = {A streamlined quantum algorithm for topological data analysis with exponentially fewer qubits}, author = {McArdle, Sam and Gily{\'{e}}n,, Andr{\'{a}}s and Berta, Mario}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2058}, month = apr, year = {2026} }► References [1] Gunnar Carlsson. Topological methods for data modelling. Nature Reviews Physics, 2 (12): 697–708, 2020. 10.1038/s42254-020-00249-3. https://doi.org/10.1038/s42254-020-00249-3 [2] Vin De Silva and Robert Ghrist. Coverage in sensor networks via persistent homology. Algebra
Quantum JournalLoading...0
quantum-computingOpen-source Python toolkit for quantum machine learning (variational classifiers, quantum kernels, reproducible workflows)
submitted by /u/sidiwinkle [link] [comments]
Reddit r/QuantumComputing (RSS)Loading...0
quantum-computingThinking About Selling Your Bitcoin? Nearly 50% of Holders Might Be Too.
By Alex Carchidi – Apr 10, 2026 at 1:30AM ESTKey PointsBitcoin is in the midst of a long downward trend.Many of its most ardent holders are sitting on losses.There's an opportunity here if you can stomach it. Bitcoin (BTC +1.61%) is down by 6% over the last 12 months and 43% from its all-time high of just above $126,000, set in October 2025. If you're thinking of selling it after such a prolonged and steep decline, you aren't alone. In fact, at its current price, about 47% of all Bitcoin in circulation is now held at a loss. That's a vast amount of pain for investors to be carrying, and the urge to cut losses is natural. But selling into the market's fear has historically been a losing strategy with this asset far more often than not. Here's what the data says about what you should do. Image source: Getty Images. Even some evangelists are cracking One important detail is that Bitcoin's long-term holders, which includes all kinds of wallets with balances unmoved for six months or more, are bearing the heaviest burden. Over 4.6 million of their coins, roughly 30% of their holdings, are now underwater, the largest share since 2023. Some are selling at their deepest losses in three years. So if you're suddenly feeling a lot less convinced about the investment thesis for Bitcoin, know that some of its most loyal and longtime boosters are now feeling the same doubt. Fresh anxiety arrived in the last week of March when Alphabet's Google Quantum AI published a new paper outlining a smattering of theoretical attack paths against the cryptography underpinning Bitcoin, including scenarios where quantum computers could crack its encryption significantly faster than previously estimated. The practical threat from such quantum computers still remains at least a handful of years away, but the news compounds the ongoing unease about the coin, stemming from geopolitical conflict and a very questionable macro environment. ExpandCRYPTO: BTCBitcoinToday's Change(1.61%) $1144.65Current
The Motley FoolLoading...0
quantum-computingUnleashing the Advantage of Quantum AI
As experimental capabilities advance rapidly, the quantum computing community faces a critical elephant in the room: What will these quantum machines eventually be useful for? Will they deliver the promised broad societal impact, or will they remain highly specialized devices for exotic tasks known only to the experts? The elephant in the room Despite decades of effort, conclusive evidence of large quantum advantage in real-world applications remains confined to a few niche domains, such as simulating quantum materials and cryptanalysis. These problems are either inherently quantum to begin with, or they possess specialized mathematical structure that quantum algorithms can easily exploit. But it seems unlikely that such structures appear broadly in everyday life. Indeed, most applications of modern computation hinge on the processing of massive, noisy classical data, generated at an unprecedented pace across society. That is the driving force behind the overwhelming success of machine learning and AI. Since the data originates from the macroscopic classical world, there is no obvious reason it should exhibit the delicate, specialized structures that quantum computers require. To playfully adapt Richard Feynman’s famous quote: We live in an effectively classical world, dammit, and maybe classical computers and AI already suffice for most of our problems. (For those unfamiliar, Feynman originally quipped: “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.”) The central challenge To truly unlock the power of a quantum computer, quantum algorithms typically need to access data in quantum superposition, processing many different samples simultaneously in different branches of the quantum multiverse. To use technical jargon, this is called querying a quantum oracle. But in reality, the classical data samples that we want to process are generated from everyday activities in a classical world, and we ca
Quantum Frontiers (Caltech)Loading...0
quantum-computingTerra Quantum to Go Public in $3.25 Billion SPAC Merger
Terra Quantum to Go Public in $3.25 Billion SPAC Merger Terra Quantum AG, a St. Gallen-based leader in hybrid quantum-classical solutions, has signed a non-binding letter of intent (LOI) to go public through a business combination with Mountain Lake Acquisition Corp. II (Nasdaq: MLAA). The transaction values Terra Quantum at $3.25 billion, reflecting significant market confidence in the company’s portfolio of quantum algorithms, high-performance software, and quantum-secure communication tools. Upon completion, the combined entity will be publicly listed, providing the company with enhanced access to capital markets to fuel its next phase of global expansion and strategic acquisitions. The strategic rationale for the merger focuses on accelerating the commercialization of “ready-to-deploy” quantum technologies. Terra Quantum has already established commercial traction across high-value sectors, including finance, defense, pharmaceuticals, and logistics. The capital infusion is expected to strengthen the company’s balance sheet, allowing it to scale its operations and deepen its partnerships with both governmental and enterprise customers. According to Chairman and CEO Markus Pflitsch, the partnership with MLAC II marks a defining step in the company’s mission to deliver practical, industrial-grade quantum utility on a global scale. Leading the transaction are specialized advisors, with Cohen & Company Capital Markets serving as the exclusive financial advisor to Terra Quantum and BTIG advising MLAC II. The deal comes at a time of increased activity in the quantum public markets, as category-defining companies seek the liquidity necessary to move from R&D to large-scale deployment. While the completion of the transaction remains subject to definitive agreement negotiations, due diligence, and regulatory approvals, the move positions Terra Quantum as a major contender in the race to provide hardware-agnostic quantum infrastructure for the global data economy.
Quantum Computing ReportLoading...0
quantum-computingDemonstration of measurement-free universal logical quantum computation - Nature Communications
“The ability to perform quantum error correction (QEC) and robust gate operations on encoded qubits opens the door to demonstrations of quantum algorithms. Contemporary QEC schemes typically require mid-circuit measurements with feed-forward control, which are challenging for qubit control, often slow, and susceptible to relatively high error rates. In this work, we propose and experimentally demonstrate a universal toolbox of fault-tolerant logical operations on error-detecting codes without mid-circuit measurements on a trapped-ion quantum processor. We present modular logical state teleportation between two four-qubit error-detecting codes without measurements during algorithm execution. Moreover, we realize a fault-tolerant universal gate set on an eight-qubit error-detecting code hosting three logical qubits, based on state injection, which can be executed by coherent gate operations only. We apply this toolbox to experimentally realize Grover’s quantum search algorithm fault-tolerantly on three logical qubits encoded in eight physical qubits, with the implementation displaying clear identification of the desired solution states. Our work demonstrates the practical feasibility and provides first steps into the largely unexplored direction of measurement-free quantum computation.” submitted by /u/Earachelefteye [link] [comments]
Reddit r/QuantumComputing (RSS)Loading...0
quantum-computingHorizon Quantum to Acquire IonQ 256-Qubit Trapped-Ion System for Multi-Modal Testbed
Horizon Quantum to Acquire IonQ 256-Qubit Trapped-Ion System for Multi-Modal Testbed Horizon Quantum Holdings Ltd. (Nasdaq: HQ) and IonQ (NYSE: IONQ) have announced a strategic agreement for the purchase of a 6th-generation, chip-based 256-qubit trapped-ion system. This acquisition is a core component of Horizon Quantum’s strategy to expand its hardware testbed beyond its existing superconducting systems. By integrating a second, technologically distinct modality, Horizon Quantum becomes one of the few commercial efforts globally to operate a multi-modal hardware environment. The 256-qubit system is designed with all-to-all connectivity and parallel operations, utilizing microwave gate operations to achieve a world-record 99.99% gate fidelity established by IonQ in 2025. The integration of the IonQ system into Horizon Quantum’s Triple Alpha software platform is intended to move beyond static circuit execution toward more expressive, adaptive quantum programming. The collaboration will focus on enhancing real-time runtime capabilities, including general control flow, dynamic memory allocation, and concurrent classical-quantum function evaluation. These technical features are designed to provide a hardware-agnostic environment where developers can write sophisticated programs at multiple levels of abstraction, facilitating a more direct path to achieving broad quantum advantage across industries such as drug discovery and financial modeling. The agreement, finalized on March 31, 2026, aligns with Horizon Quantum’s recent business combination with dMY Squared Technology Group and its subsequent listing on Nasdaq. While IonQ continues to scale its IonQ Tempo line for major cloud providers like AWS and NVIDIA, this direct acquisition allows Horizon Quantum to tightly couple its software infrastructure with frontier hardware. According to CEO Dr. Joe Fitzsimons, the addition of high-fidelity trapped-ion qubits to the testbed is a foundational step in bridging the gap betw
Quantum Computing ReportLoading...0
quantum-computingHow Mega Fortune Stock Tripled This Week
By Anders Bylund – Apr 9, 2026 at 1:23PM ESTKey PointsMega Fortune stock gained as much as 238% in just four days with no news to justify the move.Daily trading volume surged from $2.3 million to $16.9 million as speculators piled in.Price swings like this are classic meme stock behavior and often reverse sharply.Shares of Mega Fortune (MGRT +14.29%) absolutely skyrocketed this week, according to data from S&P Global Market Intelligence. From last Friday's market close to 12:30 p.m. ET on Thursday, the stock had gained 182%. That's actually a sharp intraday dip; two hours earlier, the stock was up by 238% from last Friday. Mega Fortune's stock price has swung from $49 to $64, then back down to $28, before rebounding to $44 per share. That's just Thursday's action. Mega Fortune had no reason for any of these moves, though. You're watching the wild, unpredictable price swings of a meme stock in action. ExpandNASDAQ: MGRTMega FortuneToday's Change(14.29%) $6.25Current Price$50.00Key Data PointsMarket Cap$602MDay's Range$28.01 - $64.5052wk Range$1.50 - $64.50Volume194KAvg Vol103KGross Margin50.62% Meet the mystery stock du jour You've probably never heard of Mega Fortune before. Neither had I, two hours ago. Here are the basics. It's a Cayman Islands-based microcap that helps other companies build software for their Internet of Things (IoT) devices, all under the wholly owned Hong Kong brand QBS Systems. The stock has been trading on the Nasdaq exchange since July 2025. One week ago, its market cap was just $206 million -- barely large enough to merit coverage here at The Motley Fool. The company reported full-year 2025 results on Feb. 3, showing $11.1 million of annual revenue and $1.8 million in net income. But that's old news in April. The only fresh news on its website and investor relations page over the last week was a routine stock purchase report from one of its directors. Image source: Getty Images. The short squeeze that wasn't Yet, the stock more than tri
The Motley FoolLoading...0
quantum-computingTerra Quantum AG to Go Public in $3.25 billion SPAC Deal
Insider Brief Terra Quantum has signed a non-binding LOI to go public via a SPAC merger with Mountain Lake Acquisition Corp. II, valuing the company at $3.25 billion. The proposed transaction is intended to provide Terra Quantum with access to public capital markets to support product development, global expansion, and potential acquisitions. The deal reflects investor confidence in Terra Quantum’s quantum software, algorithms, and hybrid solutions, as well as its commercial traction across sectors including defense, finance, pharmaceuticals, and logistics. PRESS RELEASE — Terra Quantum AG (“Terra Quantum”), a leading quantum technology company, and Mountain Lake Acquisition Corp. II (“MLAC II”) (Nasdaq: MLAA), a special purpose acquisition company, today announced that they have signed a non-binding letter of intent (“LOI”) to enter into a business combination that values Terra Quantum at $3.25 billion. The proposed transaction reflects strong confidence in Terra Quantum’s differentiated quantum algorithms, software, quantum security, and hybrid quantum-classical solutions, as well as its commercial traction across multiple industries including defence, finance, pharmaceuticals, and logistics. Upon completion of the transaction, the combined entity will be publicly listed, providing Terra Quantum with enhanced access to capital markets to support its next phase of growth, including product development, global expansion, and strategic acquisitions. Strategic Rationale The contemplated business combination is expected to enable Terra Quantum to: Accelerate the commercialization of ready to deploy quantum technologies Strengthen its balance sheet to support scaling operations globally Expand partnerships with enterprise and government customers Enhance visibility in the quantum computing sector Management Commentary “This milestone marks a significant step forward in Terra Quantum’s mission to deliver practical quantum solutions on a global scale today,” said Markus P
Quantum DailyLoading...0
quantum-computingQuantum Fellowship in Applications of Quantum Computing
Quantum Fellowship in Applications of Quantum Computing Application deadline: Monday, May 4, 2026Employer web page: https://www.quantumsoftwarelab.comJob type: FellowshipPostDocTags: fellowshipChancellor's postdoctoral fellowshippostdocquantum computingQuantum Software LabQuantum Software Lab at the University of Edinburgh is hiring for five Tenure-Track Positions! Up to five positions are available for prestigious 5-year tenure-track fellowships hosted by the University of Edinburgh's School of Informatics, School of Chemistry, School of Physics and Astronomy, School of Mathematics, and EPCC. As part of the recently funded "Quantum Advantage TurboCHarger" a.k.a "QATCH" programme awarded to the Quantum Software Lab (QSL) at the University of Edinburgh https://informatics.ed.ac.uk/news/latest-news/funding-boost-to-turbochar... , the "Quantum Fellows" will lead interdisciplinary research in applications of quantum computing in one or several of the following QATCH application sectors. QATCH, is developed to support the National Quantum Computing Centre and the UK’s quantum community in achieving the goals of the National Quantum Technology Missions. https://www.gov.uk/government/publications/national-quantum-strategy/nat... Over the next four years, QATCH will deliver the UK’s first integrated software and verification infrastructure through a coordinated suite of tools. At its core, QATCH operates through a two-dimensional structure linking three research Pillars: (i) Quantum Advantage Engine; (ii) HPC & Fault-Tolerant Architecture; (iii) Performance Evaluation; with six application Sectors in healthcare, material science, cybersecurity, finance, energy, and AI. The Pillars develop methods, systems, and tools, while the Sectors provide data, challenges, and end-user validation. Together they form the QATCH workflow from theory to deployment, where research is verified on national testbeds and benchmarked against measurable sector metrics. The
QuantikiLoading...0
quantum-computingHorizon Quantum will acquire a 256-qubit trapped-ion system from IonQ
Horizon Quantum will acquire a 256-qubit trapped-ion system from IonQ, expanding its capacity to develop software infrastructure for quantum applications and pursue broad quantum advantage. The system, representing IonQ’s latest technology, is designed with “all-to-all connectivity” and boasts 99.99% gate fidelity, promising more accurate and flexible calculations for complex problems. This acquisition will position Horizon Quantum among a limited number of organizations operating commercial quantum systems of multiple modalities, allowing for a more versatile hardware-agnostic environment for quantum software development. “I could not be more delighted to be working with IonQ to bring trapped ion and world-leading gate fidelities to our testbed,” said Horizon Quantum Founder and CEO Dr. Joe Fitzsimons, emphasizing the importance of this resource in unlocking quantum advantage for developers. Horizon Quantum Acquires IonQ 256-Qubit Trapped-Ion System Horizon Quantum is expanding its quantum computing capabilities with the acquisition of a 256-qubit trapped-ion system from IonQ, a move intended to accelerate the development of practical quantum applications. The purchased system promises a substantial increase in computing capacity for researchers and developers tackling complex problems. This acquisition is about more than just adding qubits; the IonQ system boasts 99.99% gate fidelity, a critical metric for ensuring the accuracy and reliability of quantum calculations, and utilizes microwave gate operations to enhance performance. The system’s “all-to-all connectivity” and parallel operations are designed to allow for a wider range of calculations with increased flexibility, moving beyond the limitations of current quantum hardware. Horizon Quantum intends to integrate this trapped-ion technology alongside its existing superconducting system, establishing a rare capability to operate commercial systems with multiple modalities. This hardware-agnostic approach aims
Quantum ZeitgeistLoading...0
quantum-computingQuantum Searches Become Vastly Faster with Reinforcement Learning
Reducing the time to find a specific item within a quantum system from scaling with the square root to the natural logarithm of its size represents a fundamental shift in computational efficiency. This advance means a search in a system of ten million parts could, in theory, be completed in just twenty-three steps, rather than three thousand one hundred and sixty-two. Numerical simulations suggest this reinforcement strategy also tolerates exponentially more noise than standard approaches, paving the way for more reliable quantum algorithms.
Quantum ZeitgeistLoading...0
quantum-computingChaotic Systems Need Fewer Steps to Mimic Random Quantum Behaviour
Chaotic Hamiltonian evolution efficiently mimics truly random quantum dynamics, a key element in both quantum information theory and many-body physics. Yi-Neng Zhou and colleagues at University of Geneva demonstrate that generating unitary $k$-designs does not necessarily require numerous independent Hamiltonians or precise control of evolution times. Their research reveals a new approach utilising a ‘quenched temporal ensemble’, where randomness is introduced through the duration of fixed Hamiltonian applications. Analysis of two-step and three-step protocols shows that while a two-step process falls short of creating a general unitary $k$-design, a three-step protocol successfully achieves this for any value of $k$. This advancement stems from the additional random phases within the three-step process, which impose stronger constraints and improve accuracy even with imperfect time averaging Three-step protocols enable general unitary k-design generation for simplified quantum simulation A three-step protocol (3SP) generates unitary $k$-designs, key tools for quantum simulations, with a parametrically narrower time window than existing two-step protocols (2SP). Previously, achieving these designs demanded either numerous independent Hamiltonian realisations or extremely precise control over evolution times. The 3SP bypasses these requirements by introducing randomness solely through the duration of fixed Hamiltonian applications. Rigorous proof demonstrates that while a 2SP cannot create a general unitary $k$-design, the 3SP successfully achieves this for any value of $k$, representing a strong advancement in simplifying quantum simulations. Unitary $k$-designs are particularly valuable because they provide a sufficient condition for the average behaviour of a quantum circuit to be equivalent to that of a completely random circuit, simplifying the analysis of complex quantum algorithms and many-body systems. The parameter $k$ dictates the order of the design; highe
Quantum ZeitgeistLoading...0
quantum-computingQuantum Circuits Gain Predictable Power with New Structural Mapping
A new framework connects the structure of quantum circuits to how well they learn, according to Kyle James Stuart Campbell and colleagues at The University of Edinburgh. The framework links circuit structure to correlations between learnable features and the geometry of training kernels. This data-independent approach enables the analytical reconstruction of kernel structure and coefficient statistics directly from circuit design, separating architectural influences from those dependent on data. By making circuit-induced structure explicit, the work provides a foundation for rigorously analysing and comparing parametrised quantum circuits based on their intrinsic design characteristics. Analytical Circuit Design Predicts Quantum Learning Behaviour and Reduces Computational Expense Coefficient covariances, previously requiring full training and datasets, are now reconstructed analytically from circuit design, resulting in a reduction in computational cost of over 50% for complex circuits. The new framework directly links circuit structure to learning behaviour, a connection previously inaccessible and necessitating extensive simulations to determine how parametrised quantum circuits learn. This framework maps circuits into an architecture matrix, revealing correlations between learnable features and the geometry of training kernels, offering a data-agnostic approach to analysing quantum machine learning models. By explicitly detailing these connections, circuit designs can now be rigorously compared based on intrinsic characteristics, independent of training data or optimisation trajectories, and performance can be predicted before implementation. Accurate reconstruction of coefficient covariances from circuit design alone achieved a 53% reduction in the computational time needed to assess circuit performance. This analytical reconstruction relies on mapping circuits to an ‘architecture matrix’ which reveals how learnable features correlate and influence training ker
Quantum ZeitgeistLoading...0
quantum-computingQuantum Computers Tackle Genome Assembly’s Toughest Puzzles
Scientists at the University of Cambridge and University of Oxford have developed a new approach to pangenome-guided sequence assembly, leveraging quantum optimisation techniques to address the computationally intensive challenges of genome reconstruction from sequencing data. Josh Cudby and colleagues tackle limitations inherent in repetitive genomic regions, where existing methods often falter due to reference bias and combinatorial complexity. Their research explores both quadratic unconstrained binary optimisation and a higher-order binary optimisation formulation, sharply reducing the number of required variables for complex calculations. By employing the Iterative-QAOA framework and a custom circuit compilation strategy, the team achieved promising results in simulations and on IBM quantum hardware, identifying optimal assemblies with a tiny fraction of candidate solutions. Pangenome assembly is established as a compelling application where quantum computing may offer a practical advantage soon. Quantum optimisation streamlines complex genome mapping Iterative-QAOA, a quantum algorithm akin to a guided search through many possibilities, proved central to overcoming computational hurdles in genome assembly. This algorithm belongs to a class of approximate optimisation algorithms designed to find near-optimal solutions to complex problems. Unlike classical algorithms that exhaustively search all possibilities, QAOA leverages quantum phenomena like superposition and entanglement to explore the solution space more efficiently. The Iterative-QAOA framework avoids painstakingly fine-tuning every parameter of the quantum process, instead employing a pre-defined schedule and iteratively refining its approach based on previous attempts. This iterative refinement is crucial for adapting to the specific characteristics of the genome assembly problem and improving solution quality over time. A custom circuit compilation strategy effectively streamlined the instructions se
Quantum ZeitgeistLoading...0
quantum-computingCloudflare Accelerates Post-Quantum Roadmap to 2029 Amid Major Algorithmic Breakthroughs
Cloudflare Accelerates Post-Quantum Roadmap to 2029 Amid Major Algorithmic Breakthroughs Cloudflare has officially updated its post-quantum (PQ) security roadmap, shifting its target for full system-wide resilience to 2029. This acceleration is driven by recent and unexpected advancements in quantum factoring efficiency, which suggest that the window for migrating global internet infrastructure is closing faster than previously modeled. While the company enabled post-quantum encryption for all websites and APIs in 2022 to mitigate “harvest now, decrypt later” (HNDL) risks, the new roadmap prioritizes the much more complex challenge of post-quantum authentication. The urgency stems from two independent breakthroughs announced in late March and early April 2026. First, Google’s Quantum AI team published a whitepaper demonstrating a 20-fold reduction in the resources required to break ECDSA-256, the elliptic curve cryptography securing Bitcoin, Ethereum, and much of the public web. According to a recent Quantum Computing Report (QCR) Qnalysis, this development represents a “decryption threshold” that necessitates an immediate re-evaluation of the quantum threat to global blockchain infrastructure and decentralized finance. Verified via a zero-knowledge proof, Google’s optimized algorithm suggests that fewer than 500,000 physical qubits could be sufficient to crack these keys—a sharp decline from the 10 million qubits estimated just a few years ago. Parallel research from the Caltech-linked startup Oratomic has further compressed this timeline by focusing on neutral atom architectures. Oratomic’s research indicates that breaking RSA-2048 and P-256 could require as few as 10,000 reconfigurable atomic qubits. This efficiency is gained through a massive reduction in error-correction overhead; while superconducting systems typically require 1,000 physical qubits for a single logical qubit, neutral atom machines—which allow for dynamic, “high-rate” connectivity—may require o
Quantum Computing ReportLoading...0
quantum-computingQuantum Fellowships in Applications of Quantum Computing
Quantum Fellowships in Applications of Quantum Computing Application deadline: Monday, May 4, 2026Research group: Research Center for Quantum Information Institute for Quantum Information ScienceQuantum TransportOxford Ion Trap Quantum Computing GroupQuantum Computation and Information Project, ERATO-SORST, JSTQuantum Technology at QueensQuantum Optics Theory at ICFOQuantum Theory groupQueensland Quantum Optics LabQuantum Optics Group at ICN, UNAM, MexicoEmployer web page: www.quantumsoftwarelab.com Job type: FellowshipTags: quantum computingquantumjobresearchQuantum Software Lab is hiring! Up to five positions are available for prestigious 5-year tenure-track fellowships hosted by the University of Edinburgh's School of Informatics, School of Chemistry, School of Physics and Astronomy, School of Mathematics, and EPCC. Fellows will lead interdisciplinary research in Applications of Quantum Computing in one or several of the following QATCH application sectors. The initial focus of the fellowships will be on establishing their research careers, including developing their distinctive research programme, including innovation and/or knowledge exchange activities, producing research outputs and research support applications, and engaging in career development. The Fellow will be expected to make a growing contribution to research-led teaching/training and academic leadership in their host School, particularly after the first few years, to develop the skills and experience required in a typical academic role. If you’re building the next generation of quantum solutions, we’d love to hear from you. Job Title: Quantum Fellowships in Applications of Quantum Computing Affiliation: Quantum Software Lab Website: www.quantumsoftwarelab.com Link to LinkedIn: TBC (will post tomorrow) Location: Edinburgh, United Kingdom 🔗 Learn more about the job and apply: https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/C... Log in or register to pos
QuantikiLoading...0