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Quantum Computing Finance & Banking: Portfolio Optimization & Risk Analysis

Quantum finance news: JPMorgan, Goldman Sachs quantum banking. Portfolio optimization, risk modeling, Monte Carlo & algorithmic trading.

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Financial services represent the largest commercial opportunity for near-term quantum computing, with institutions developing quantum algorithms for portfolio optimization, risk analysis, derivative pricing, and fraud detection. The sector's mathematical foundations in optimization and stochastic modeling align naturally with quantum computational advantages.

High-value use cases include portfolio optimization using quantum algorithms to solve mean-variance optimization across thousands of assets; risk analysis and Monte Carlo simulations where quantum amplitude estimation offers quadratic speedup; and derivative pricing for path-dependent options requiring high-dimensional integration.

India's Banking and Financial Services Quantum Landscape

India's banking and financial services sector, with over $2.5 trillion in assets, represents a significant potential market. The National Quantum Mission includes financial applications within its quantum computing applications scope. The Reserve Bank of India (RBI) and Securities and Exchange Board of India (SEBI) monitor quantum computing implications for market infrastructure and security.

Tata Consultancy Services (TCS) partners with IBM and the Andhra Pradesh government to deploy India's largest quantum computer at the Quantum Valley Tech Park in Amaravati, with applications including financial optimization. TCS develops quantum algorithms for portfolio optimization, risk modeling, and fraud detection. Infosys explores quantum computing through its Quantum Living Labs (QLL), offering advisory and proof-of-concept services with demonstrated capabilities in logistics, finance, cybersecurity, and healthcare.

The NQM targets developing quantum machine learning and optimization algorithms applicable to financial services, with commercial deployment expected as hardware matures toward the 50-1000 qubit range.

Researchers classify neutrino events with a quantum computerquantum-computing

Researchers classify neutrino events with a quantum computer

Researchers have achieved testing accuracy near 80% with the NPQK and approximately 70% accuracy with the QCNN in classifying events detected by neutrino telescopes using a quantum computer, a result comparable to traditional methods. Pablo Rodriguez-Grasa, University of the Basque Country UPV/EHU and colleagues demonstrated this capability by investigating neural projected quantum kernels and quantum convolutional neural networks. This work, published August 21, 2026, in Quantum Science and Technology, Number 4, establishes the feasibility of applying quantum machine learning to astronomical data analysis with current hardware. The study explores how quantum computers can distinguish between different types of neutrino events, crucial for understanding rare cosmic phenomena. NPQK and QCNN Approaches to Neutrino Event Classification Achieving testing accuracy near 80%, the neural projected quantum kernel (NPQK) approach demonstrated a capacity to classify neutrino events directly on both simulators and the IBM Strasbourg quantum processor. This result suggests a shift toward practical quantum applications in astrophysics. Researchers led by Pablo Rodriguez-Grasa at the University of the Basque Country UPV/EHU detailed this performance in a study published August 21, 2026, in Quantum Science and Technology, Number 4, focusing on distinguishing between muon tracks and hadronic/electromagnetic cascades, key signatures within neutrino telescope data. This direct implementation on quantum hardware bypasses the need for purely simulated results, validating the methodology against the inherent noise and limitations of current quantum systems. The team addressed a critical challenge in applying quantum machine learning to high-energy physics: the encoding of large feature spaces. Traditional methods struggle with the vast amounts of information generated by neutrino telescopes like IceCube, limiting the feasibility of quantum graph neural networks. To address this, Rodrigue

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Noise-induced equalization improves quantum learning accuracyquantum-computing

Noise-induced equalization improves quantum learning accuracy

Researchers at the University of Basel and the University of Pavia published findings in Quantum Science and Technology on August 21, 2026, detailing an approach to quantum learning. The work demonstrates that introducing controlled quantum noise can improve the accuracy of variational quantum algorithms, despite noise typically hindering quantum computation. This research proposes a pre-training procedure to identify noise levels that induce equalization within quantum learning models, redistributing sensitivity across key directions. Analysis through the quantum Fisher information matrix provides a method for estimating the noise level inducing the strongest equalization, ultimately leading to improved generalization. Quantum Noise Impacts on Variational Quantum Algorithms This equalization effectively flattens steep curves and enhances shallow ones within the algorithm’s Riemannian manifold, facilitating more efficient exploration of the parameter space. The team’s analysis centers on the quantum Fisher information matrix, a key tool in quantum parameter estimation theory that quantifies a quantum state’s sensitivity to changes in its parameters. The rank of the QFIM reveals the number of informative directions available for optimization, and the researchers discovered that a strategically chosen noise level alters the QFIM’s eigenspectrum, promoting a more balanced distribution of sensitivity. This reshaping is not simply adding noise to improve signal; it’s about restructuring the optimization process itself, moving away from potentially misleading, sharply peaked landscapes. Francesco Scala of the University of Basel and the University of Pavia/INFN explains that while quantum noise typically hinders computation, their results demonstrate that “modest, optimized noise levels reshape the Riemannian manifold associated to the quantum model of interest.” Classical machine learning already leverages noise for improved generalization through techniques like data au

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New estimates From Google Quantum AI show quantum attack on Bitcoin is closer than thoughtquantum-computing

New estimates From Google Quantum AI show quantum attack on Bitcoin is closer than thought

Google Quantum AI researchers have determined that breaking the core cryptography of various cryptocurrencies, secured by the secp256k1 curve, may require as few as 1200 logical qubits and 90 million Toffoli gates, a significantly lower threshold than previously understood. The team’s work elucidates specific vulnerabilities blockchain technologies face with the development of quantum computers and potential mitigation strategies. To ensure responsible disclosure, the researchers validated their findings using a zero-knowledge proof without revealing specific attack vectors. This analysis reveals that emerging “fast-clock” quantum computers could enable attacks on cryptocurrency transactions in the public mempool. Shor’s Algorithm Estimates for secp256k1 Bitcoin Attacks This represents a significant reduction in the estimated resources needed for a successful attack compared to earlier projections, bringing the threat of quantum decryption closer to reality. These architectures, the researchers note, could enable “on-spend” attacks targeting public mempool transactions, potentially allowing malicious actors to seize funds before they are confirmed on the blockchain. A key distinction highlighted in the analysis is the difference between fast-clock and “slow-clock” quantum computers, such as those based on neutral atoms or ion traps. The researchers found that circuits executing Shor’s algorithm on superconducting architectures, with a 10-3 physical error rate and planar connectivity, could complete the calculation in minutes using fewer than half a million physical qubits. This speed is critical because it suggests a viable attack window exists once sufficiently powerful quantum computers become available. The implications extend beyond Bitcoin, encompassing any cryptocurrency reliant on the secp256k1 curve for securing transactions. Technical solutions would benefit from accompanying public policy, and highlight ongoing efforts to transition to Post-Quantum Cryptog

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WiMi builds quantum network for better data sortingquantum-computing

WiMi builds quantum network for better data sorting

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) is applying a quantum convolutional neural network, built with three-qubit interactions, to classify classical data. The company’s new network aims to improve expressive power and entanglement generation, key to advancing quantum deep learning model design. WiMi’s approach utilizes block partitioning for image data and combines structured amplitude and angle encoding for one-dimensional data, efficiently translating classical information into a quantum format. Three-Qubit Interactions Enhance Quantum Convolutional Neural Network Expressivity WiMi Hologram Cloud Inc. This approach deviates from the typical focus of quantum computing on problems intractable for conventional computers, instead targeting established machine learning tasks with a novel quantum architecture. A core innovation lies within the network’s interaction layers, specifically designed around three-qubit interactions. WiMi researchers systematically studied how these layers impact the quantum state space coverage, finding that introducing three-body interactions significantly expands the range of reachable states within the network’s parameter space. This expansion directly addresses a common limitation in traditional quantum neural networks, a lack of expressive capacity for complex patterns, while simultaneously maintaining manageable circuit depth. The team further analyzed the network’s entanglement capabilities through quantum information theory, revealing that the three-qubit interaction layer generates high-intensity, multi-scale entanglement at relatively shallow depths, which is crucial for capturing nonlinear correlations within input data and offers a clear advantage over models relying solely on two-qubit entanglement gates. For image data, WiMi employs block partitioning and local mapping to embed pixel information into quantum subsystems, while one-dimensional data benefits from a combination of structured amplitude encoding and angle encoding,

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Power-Law Tails Signal Semi-Fractal States on Chiral Cayley Treesquantum-computing

Power-Law Tails Signal Semi-Fractal States on Chiral Cayley Trees

Google Quantum AI researchers have reported a surprising distribution of local density of states, specifically, broad power-law tails, in the behavior of quantum particles moving on a Cayley tree, suggesting a novel wave-function statistic they term semi-fractality. This differs from standard fractal behavior, representing an intermediate state between fully extended and localized quantum states, achieved by designing a model where particle movement is heavily influenced by connections with diminished strength. The work demonstrates that as the exponent controlling the power-law hopping distribution is changed, the system transitions from a semi-fractal regime to a localized one. Researchers found this represents an extreme intermediate form of quantum state, challenging the traditional understanding of how wave functions behave in non-ergodic systems. Carlo Vanoni of Princeton University, Vladimir E. Kravtsov of The Abdus Salam ICTP, and Boris L. Altshuler of Columbia University detailed their findings in recent work, focusing on a quantum particle traversing an infinite Cayley tree where the strength of connections between nodes varies significantly. The researchers deliberately designed the model with a distribution of hopping amplitudes to explore unusual quantum phenomena. Exact diagonalization further revealed a hierarchy of eigenstate weights, supporting the interpretation of semi-fractality as a consequence of this weight distribution. Researchers are increasingly focused on quantum states that defy easy categorization, and work with Cayley trees, infinitely branching structures, is revealing a particularly subtle case. This setup allows researchers to observe how the particle’s movement is heavily influenced by these diminished connections. The team’s analysis reveals that the system occupies an extensive portion of the tree, yet its higher-order moments behave as if it were a multifractal state, a complex interplay of extension and localization. The quest

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Researchers Link Neutral-Atom Qubit Spacing to Noise Levelsquantum-computing

Researchers Link Neutral-Atom Qubit Spacing to Noise Levels

Neutral-atom processors conventionally enforce minimum geometric spacing rules for qubit arrays with a finite Rydberg blockade radius of approximately 4.3μm during gate operations. Meeting these separation requirements does not guarantee elimination of residual noise arising from van der Waals interactions between qubits, however modest increases in inter-gate spacing can sharply suppress correlated exposure. Xinyi Li of Stevens Institute of Technology and colleagues found that increasing the space between qubits reduces unwanted interactions caused by weak van der Waals forces even when devices meet basic operational geometry. The team demonstrated that moderate increases beyond minimum spacing effectively suppress correlated errors impacting reliability during quantum computation. This work distinguishes meeting design rules from achieving genuinely safe qubit arrangements by considering how spacing affects both physical noise and computational cost. The researchers have shown simply adhering to minimum spacing for neutral-atom processor qubits does not eliminate unwanted interaction due to weak van der Waals forces. The team discovered increased space beyond this requirement sharply suppresses correlated errors degrading computational reliability. This is akin to ensuring gears mesh smoothly; merely fitting them together isn’t enough if they snag during operation. They treated entangling-zone spacing as a key design variable influencing both physical noise and calculation speed, similar to project management timelines where extending one task impacts overall completion date. The findings distinguish hardware legality from genuine safety against residual noise, prompting evaluations of compiler designs considering geometry, error correction capabilities, and scheduling efficiency, but looser spacings may ultimately deliver substantial gains in qubit stability. Increased qubit separation minimises interaction-induced decoherence and lowers error rates Error rates f

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Researchers Build Adaptive Quantum Sensor Designs with Reinforcement Learningquantum-computing

Researchers Build Adaptive Quantum Sensor Designs with Reinforcement Learning

A new set of tools called AUTOQSENSE addresses challenges in high-precision parameter estimation where performance is key to quantum circuit architecture during probe preparation and measurement periods. The method optimises continuous parameters within pre-defined ansatzes, restricting the explored design space and hindering adaptability to specific sensing tasks and hardware constraints periods. Jie Liu and Xin Wang at the University of Science and Technology of China present a reinforcement-learning framework designed to search for optimal circuit architectures using Fisher-information-based objectives periods. In few-qubit systems, an agent sequentially constructs both preparation and measurement circuits periods. For larger systems, a distributed formulation assigns local circuit design responsibilities to subsystem agents and establishes inter-block communication protocols periods. Automated circuit design enhances parameter estimation with reduced gate complexity Entangling gate counts decreased by up to 30% compared to established hardware-efficient approaches while maintaining precise parameter estimation periods. This improvement unlocks previously unattainable sensing protocols due to resource limitations. Conventional methods struggle when faced with complex noise models or large numbers of qubits requiring extensive optimisation periods. textsc{AutoQSense}, a new framework from David Hayes and his team alongside collaborators Quantum AI, automatically designs optimal circuits for quantum sensors using reinforcement learning, a technique where an agent learns through trial and error, and Fisher information, which measures data gained from each measurement period. The system successfully rediscovers known strategies whilst adapting effectively to dephasing noise, a common source of errors in quantum systems, demonstrating its flexible application across diverse scenarios periods. Achieving superior results on simulations involving up to four qubits was ve

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Quantum algorithm solves matrix equations much faster than classical methodsquantum-computing

Quantum algorithm solves matrix equations much faster than classical methods

Rolando D. Somma of Google Quantum AI and colleagues have devised a quantum algorithm that efficiently solves the Sylvester equation, a fundamental linear matrix equation used in fields from control theory to physics. The approach constructs a solution matrix using a technique, allowing for faster access to its properties than traditional methods of preparing a quantum state. The query and gate complexities of the quantum circuit that implements this block-encoding are almost linear in a condition number that depends on the input matrices and logarithmically with the problem’s dimension and desired accuracy. The team demonstrates this circuit can efficiently tackle problems within the BQP class, suggesting a pathway toward practical quantum solutions for complex linear algebra. Quantum Algorithm for the Sylvester Equation Google Quantum AI researchers have devised a quantum circuit capable of solving the Sylvester equation with computational demands scaling favorably with problem size. Somma and colleagues, centers on constructing a block-encoding of the solution matrix, offering a potential pathway to exponential speedups in instances where the condition number scales polylogarithmically with the problem size. Unlike traditional approaches that treat matrix equations as systems of linear equations with extremely large dimensions, this quantum algorithm employs specialized techniques tailored to directly construct the solution matrix. The core of this advancement lies in the algorithm’s efficiency in accessing properties of the solution matrix’s entries, achieving this faster than preparing the matrix as a quantum state. This is accomplished through a block-encoding, a unitary transformation where the first block represents the solution matrix, normalized by a rescaling factor, x. The query and gate complexities of the resulting quantum circuit are almost linear in a condition number, denoted as κ, which depends on the input matrices, and scale logarithmically with

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QOBLIB gains quantum optimization data from JIJquantum-computing

QOBLIB gains quantum optimization data from JIJ

JIJ is now contributing benchmark results to QOBLIB, an open library evaluating both quantum and classical optimization methods, signaling a push for standardized comparisons across the field, the company says. The company benchmarked its quantum optimization method using a subset of problems from QOBLIB and shared the data with the IBM Quantum team, who then referenced JIJ in a recent technical blog. “Shared benchmarks such as QOBLIB provide a common basis for comparing optimization methods,” JIJ stated, emphasizing the importance of transparent evaluation as it continues developing its technology within the broader quantum optimization community. JIJ Benchmarks Quantum Optimization Method with QOBLIB Datasets IBM Quantum recently highlighted JIJ’s contribution in a technical blog post detailing QOBLIB, publicly acknowledging the new benchmark submissions. This recognition indicates IBM is actively monitoring JIJ’s progress in quantum optimization and values the transparency offered by shared benchmarking datasets. JIJ intends to continue refining its technology and participating in open benchmarking initiatives within the quantum optimization community, according to QOBLIB. The OMMX Quantum Benchmarks repository currently includes a subset of problems sourced from QOBLIB, furthering the availability of standardized datasets for researchers. Utilizing these benchmarks is an important step in understanding the performance characteristics of its method through transparent, comparable evaluation, according to the company. Source: https://www.j-ij.com/en/news/20260821 Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: Ivy Delaney Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity t

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AI Coding Assistants are Getting Smarterquantum-computing

AI Coding Assistants are Getting Smarter

AI Coding Assistants are Getting Smarter By Doug Finke Last year, we published an article titled Quantum SDKs are Dying, Long Live Quantum AI SDK describing how the classical computing concept of “Vibe Coding” is entering the quantum programming space. (Perhaps we should call it Vibe Qoding!) We continue to see this trend accelerate and believe it will profoundly impact the future use of quantum computing technology. Workforce development remains a major concern within the quantum community. The central question is simple: How can we train thousands of potential users to program large-scale quantum systems within a reasonable timeframe? At industry conferences, speakers often ask, “If we put a 1-million-qubit quantum computer online next week, would anyone know how to program it and take advantage of its capabilities?” At GQI, we believe vibe coding will help solve this bottleneck. Placing these powerful AI tools directly into users’ hands will significantly shorten the time required to develop, test, and run quantum programs to solve real-world problems. Most industry experts we speak with agree: AI-assisted quantum coding will be the primary way people program quantum computers by 2030. The recent developments we describe below reinforce this outlook. The AI Decryption Optimization Race In April, we published The Decryption Threshold — Re-estimating the Quantum Threat to Blockchain Infrastructure covering a Google whitepaper on breaking the secp256k1 cryptographic algorithm. Google achieved this using a quantum computer an order of magnitude smaller than previously thought possible—requiring only 1,200–1,450 logical qubits and 70–90 million Toffoli gates. Because secp256k1 powers Bitcoin’s public-key cryptography and digital signatures, breaking it would have severe consequences. Today’s quantum hardware isn’t quite powerful enough yet, but GQI expects capable machines to arrive within the next few years. While we believe that Google manually developed its algorit

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Rigetti Establishes Dedicated Systems Delivery Organization to Scale On-Premises Deploymentsquantum-computing

Rigetti Establishes Dedicated Systems Delivery Organization to Scale On-Premises Deployments

Rigetti Establishes Dedicated Systems Delivery Organization to Scale On-Premises Deployments Superconducting quantum hardware developer Rigetti Computing, Inc. (Nasdaq: RGTI) has reorganized its operating structure to separate commercial system deployments from core quantum processor R&D. The company has established a dedicated Systems Delivery organization to manage manufacturing operations, commercial sales, and customer-facing engineering, while consolidating all hardware engineering and chiplet development into a streamlined Technology organization. [ Rigetti Reorganized Operational Architecture ] │ ┌──────────────────────────────────────┴──────────────────────────────────────┐ ▼ ▼ Systems Delivery & Operations Technology & Hardware Engineering • Manufacturing Operations & Fab-1 Delivery. • Quantum Processor Architecture & Chiplet Design. • On-Premises System Install & Customer Support. • Multi-Chip Interconnects (IMCs) & Cryo-RF. • Commercial Sales, Software & Government Programs. • Targeted 99.5% 2Q Gate Fidelity on Cepheus-1-108Q. Scaling On-Premises Deployments and Isolating R&D Historically, customer installation and field support activities were handled directly within Rigetti’s core engineering group. The structural shift isolates commercial delivery logistics from core hardware research, allowing the R&D team to focus exclusively on processor fidelity and roadmap execution: Commercial System Demand: The dedicated delivery unit will manage scaling and deployment for on-premises hardware, ranging from the 9-qubit Novera QPU to 36-qubit systems and large-scale 108-qubit Cepheus-class systems deployed at national laboratories and quantum computing centers. Cepheus-1-108Q Performance Targets: The technology organization remains focused on achieving its benchmark target of 99.5% median two-qubit gate (CZ) fidelity on the Cepheus-1-108Q platform—a 108-qubit QPU constructed by tiling twelve 9-qubit chiplets together using

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Superpositions Partners with EBU Luxembourg to Integrate Quantum Workflows into Business Curriculaquantum-computing

Superpositions Partners with EBU Luxembourg to Integrate Quantum Workflows into Business Curricula

Superpositions Partners with EBU Luxembourg to Integrate Quantum Workflows into Business Curricula European quantum software company Superpositions has entered into a strategic educational partnership with the European Business Institute of Luxembourg (EBU) to power its Q-Ready academic initiative. Under the agreement, EBU is incorporating quantum computing and hybrid quantum-classical algorithms into its business school curriculum, providing students with direct hands-on access to the Superpositions Studio platform. [ EBU Q-Ready & Superpositions Platform Architecture ] │ ┌─────────────────────────────────────┼─────────────────────────────────────┐ ▼ ▼ ▼ Curriculum Integration Automated Workflow Engine Multi-Hardware Backends • Dedicated Business Q-Courses. • Natural-Language Problem Input. • IBM Quantum Processors. • Embedded Modules in BBA/MBA. • Classical vs. Quantum Benchmarking. • IonQ Trapped-Ion QPUs. • Executive & Student Training. • Automated Hybrid Code Generation. • IQM & Rigetti Superconducting. The collaboration is structured around a non-technical, problem-first approach to quantum software adoption: Natural-Language Problem Translation: Students input business use cases into Superpositions Studio in plain language. The multi-agent platform automates the formulation of quantum and hybrid algorithms, producing executable code, performance reports, and cost-benefit comparisons between classical and quantum execution. Hardware-Agnostic Execution: The platform connects directly to hardware backends from IBM Quantum, IonQ, IQM, and Rigetti, allowing business students to evaluate how specific combinatorial optimization, machine learning, and simulation problems perform across distinct QPU architectures. Curriculum Scope: EBU is introducing dedicated quantum courses while embedding practical quantum concepts into existing degree tracks (including BBA, MBA, and executive programs), preparing future corporate decision-makers to evaluate quantum adv

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FormationQ and STFC Hartree Centre Partner to Drive Enterprise Quantum and AI Adoption in the UKquantum-computing

FormationQ and STFC Hartree Centre Partner to Drive Enterprise Quantum and AI Adoption in the UK

FormationQ and STFC Hartree Centre Partner to Drive Enterprise Quantum and AI Adoption in the UK Quantum enablement company FormationQ and the STFC Hartree Centre—part of the Science and Technology Facilities Council (STFC) and UK Research and Innovation (UKRI)—have announced a strategic partnership to accelerate the commercial adoption of quantum computing, artificial intelligence (AI), and high-performance computing (HPC) across UK industry, public sector, and research organizations. [ FormationQ & STFC Hartree Centre Partnership Stack ] │ ┌───────────────────────────────────────┼───────────────────────────────────────┐ ▼ ▼ ▼ Industry Verticals Targeted Technical & Workforce Objectives Ecosystem Integration • Healthcare & Life Sciences. • Hybrid Quantum-AI-HPC Workflows. • STFC / UKRI Hartree Infrastructure. • Manufacturing & Logistics. • Bridging Quantum Talent Gaps. • University & Academic Pipelines. • Energy, Grid & Infrastructure. • Pilot & Proof-of-Concept Frameworks. • International Partner Networks. Tackling Commercialization Barriers and Workforce Shortages While quantum hardware continues to mature, enterprises face integration bottlenecks, including infrastructure alignment and specialized workforce shortages. Citing findings from OECD policy studies and industry surveys, over 40% of organizations identify a lack of skilled talent as a primary obstacle to quantum adoption. To bridge this gap, the collaboration focuses on structured adoption pathways: Application Identification & Pilot Development: Helping enterprises translate early proof-of-concept projects into scalable, operational deployments across healthcare, logistics, energy, manufacturing, and defense. Hybrid Workflows: Combining quantum computing architectures with classical HPC systems and AI workflows to address complex optimization, simulation, and materials science challenges. Workforce & Ecosystem Development: Engaging UK universities, research instituti

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How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detectionquantum-computing

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

--> Quantum Physics arXiv:2608.18155 (quant-ph) [Submitted on 13 Aug 2026] Title:How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection Authors:Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui View a PDF of the paper titled How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection, by Syeda Anshrah Gillani and 4 other authors View PDF HTML (experimental) Abstract:Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous studies find that well-tuned classical models remain competitive, and that apparent quantum gains may be artefacts of classical dimensionality reduction and implicit regularisation rather than genuine quantum effects. We ask not whether a quantum model can post a high accuracy, but how quantum the advantage really is. We present a unified, reproducible QML-IDS benchmark evaluating hybrid variational quantum circuits and quantum-kernel SVMs against five honestly-tuned classical baselines across four standard NIDS datasets (NSL-KDD, UNSW-NB15, CICIDS2017, NF-ToN-IoT-v2) under one leakage-controlled protocol, with an equal-budget feature view, imbalance- and calibration-aware metrics with significance testing, and a simulated NISQ noise sweep. We introduce a quantum-attribution audit (parameter-matched classical controls, a random-feature kernel, and a regularisation sweep) that quantifies how much of any gain is genuinely attributable to the quantum component. Tuned classical models (Random Forest, XGBoost) match or exceed the quantum models on aggregate detection on every dataset, and the audit attributes this to classical preprocessing and regularisation rather than quantum effects. Two advantages survive false-discover

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Variational Quantum Algorithms for Hyperelasticity: Incorporating Nonlinear Constitutive Behaviorquantum-computing

Variational Quantum Algorithms for Hyperelasticity: Incorporating Nonlinear Constitutive Behavior

--> Quantum Physics arXiv:2608.18363 (quant-ph) [Submitted on 18 Aug 2026] Title:Variational Quantum Algorithms for Hyperelasticity: Incorporating Nonlinear Constitutive Behavior Authors:Uditnarayan Kouskiya, Caglar Oskay View a PDF of the paper titled Variational Quantum Algorithms for Hyperelasticity: Incorporating Nonlinear Constitutive Behavior, by Uditnarayan Kouskiya and Caglar Oskay View PDF HTML (experimental) Abstract:This paper extends a recently proposed Variational Quantum Algorithm framework for nonlinear elasticity to a broader class of constitutive nonlinearities involving rational powers of the stretch. One-dimensional incompressible Ogden and Mooney-Rivlin models are employed as representative examples to demonstrate the proposed methodology. Nonlinear constitutive terms are transformed into forms compatible with the available quantum algorithmic primitives through the introduction of auxiliary variables and penalty constraints, yielding approximate solutions via a Variational Quantum Algorithm. An iterative correction strategy based on a sequence of Variational Quantum Algorithms is then introduced to improve solution accuracy. A Numerical example demonstrates the proposed approach. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.18363 [quant-ph]   (or arXiv:2608.18363v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.18363 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Uditnarayan Kouskiya [view email] [v1] Tue, 18 Aug 2026 22:28:50 UTC (490 KB) Full-text links: Access Paper: View a PDF of the paper titled Variational Quantum Algorithms for Hyperelasticity: Incorporating Nonlinear Constitutive Behavior, by Uditnarayan Kouskiya and Caglar OskayView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-08 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semant

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Infleqtion Plans 2027 Colorado Field Test of Quantum Gravity Gradiometryquantum-computing

Infleqtion Plans 2027 Colorado Field Test of Quantum Gravity Gradiometry

Infleqtion will conduct a 2027 field test of its quantum gravity gradiometry technology in Colorado to improve discovery and mapping of underground critical-mineral deposits before costly drilling. The demonstration supports the pending Quantum-Enhanced Critical Minerals Mapping Act of 2026 while remaining separate from its proposed federal survey requirements. Technical details on gravitational measurements and commercial implications for domestic supply chains are covered inside. The post Infleqtion Plans 2027 Colorado Field Test of Quantum Gravity Gradiometry appeared first on The Qubit Report.

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Allot Leads Founding Industry and Academic Consortium to Launch Israeli Post-Quantum Communications Initiativequantum-computing

Allot Leads Founding Industry and Academic Consortium to Launch Israeli Post-Quantum Communications Initiative

Allot Leads Founding Industry and Academic Consortium to Launch Israeli Post-Quantum Communications Initiative Cybersecurity and network intelligence vendor Allot Ltd. (NASDAQ: ALLT) has been appointed founding member and Chair of Israel’s newly established Post-Quantum Communications (PQC) Consortium. Supported by the Israel Innovation Authority’s (IIA) Technological Infrastructure Division, the national consortium brings together multinational technology leaders, defense contractors, and six major research universities to develop quantum-safe communication protocols across optical, satellite, and mobile networks. [ Israel Post-Quantum Communications Consortium Stack ] │ ┌───────────────────────────────────────┼───────────────────────────────────────┐ ▼ ▼ ▼ Industry & Commercial Partners Defense & Hardware Innovators Academic Research Institutions • Allot Ltd. (Consortium Chair). • NVIDIA & Elta Systems. • Technion & Bar-Ilan University. • Ceragon & Gilat Satellite. • RAD Data Communications & Heqa. • Hebrew University & Ben-Gurion. • Classiq & Ribbon Communications. • Hybrid PQC-QKD Hardware Architectures.• Open Univ. & Univ. of Haifa. Multi-Layer Network Protection Architecture The consortium focuses on securing multi-layer telecommunications infrastructure against “harvest now, decrypt later” (HNDL) attacks and future cryptanalytically relevant quantum computers (CRQCs): Hybrid Defense Protocols: Research efforts will evaluate pure Post-Quantum Cryptography (PQC) software algorithms, physical-layer Quantum Key Distribution (QKD), and hybrid architectures combining mathematical PQC with optical quantum key exchange. Cross-Domain Network Scopes: The technological scope spans multiple OSI networking layers—including optical transport, Ethernet, IP routing, 5G/6G mobile cores, data center interconnects, satellite channels, and commercial cloud communications. Consortium Governance: Allot VP CTO Dr. Yaakov Stein serves as Chair of

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IBM Links Modular Cryogenic Cells to Scale Multi-Chip Architectures for 2029 Starling Quantum Computerquantum-computing

IBM Links Modular Cryogenic Cells to Scale Multi-Chip Architectures for 2029 Starling Quantum Computer

IBM Links Modular Cryogenic Cells to Scale Multi-Chip Architectures for 2029 Starling Quantum Computer Two connected modular cryostat prototypes operating in Poughkeepsie, NY. IBM (NYSE: IBM) has announced the successful linking and cooldown of its first modular cryogenic cells, completing a key hardware milestone toward its planned fault-tolerant quantum computer, IBM Quantum Starling, scheduled for delivery in 2029. Operating at its Poughkeepsie, New York quantum facility, IBM joined two box-shaped cryogenic modules into a single thermal environment, achieving an operating temperature below 15 millikelvin (-273.135∘C). [ IBM Modular Cryogenic Infrastructure Stack ] │ ┌─────────────────────────────────┼─────────────────────────────────┐ ▼ ▼ ▼ Modular Cryogenic Cells "L-Coupler" Interconnects Multi-Chip Processor Roadmap • Rectangular Aluminum Shells. • Direct Chip-to-Chip Quantum • Nighthawk Processors Installed (2026). • 2.75 m³ Vacuum Chamber Vol. Links. • 1,000+ Programmable Qubits (2027). • 12x More Wiring Space vs. QSo. • Short Meter-Scale Interconnects. • Starling Fault-Tolerant System (2029). Re-Engineering Cryogenic Architecture for Modular Scaling Traditional superconducting quantum processing units (QPUs) reside in isolated, cylindrical “chandelier” cryostats. These single-chip enclosures impose spatial constraints, generate heat bottlenecks, and induce qubit crosstalk when routing thousands of coaxial cables. IBM’s modular cell architecture replaces cylindrical fridges with rectangular, aluminum-framed cryogenic units that sit tightly side-by-side: Inter-Cell Thermal Shielding: When cells join, quantum cables route through a shared opening, protected by multi-layered thermal shielding tunnels that preserve dilution refrigeration temperatures below 15 millikelvin without increasing cooldown times. Expanded Physical Capacity: Each cell provides 2.75 cubic meters of internal vacuum volume and 0.53 square meters of wiring surface area—yielding up to 12 times

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Infleqtion will map Colorado minerals with quantum sensors by 2027quantum-computing

Infleqtion will map Colorado minerals with quantum sensors by 2027

By 2027, Infleqtion plans a field demonstration in Colorado to map underground mineral deposits using quantum gravity gradiometry, a technology aimed at reducing the costs and uncertainties of current exploration methods, the company says. The company’s work supports the Quantum-Enhanced Critical Minerals Mapping Act of 2026, which would direct the U.S. Geological Survey to integrate this quantum sensing into its Earth Mapping Resources Initiative. “America cannot secure the supply chains it cannot see,” says Matt Kinsella, CEO of Infleqtion, emphasizing the need to identify domestic resources for national security and advanced manufacturing. Quantum Gravity Gradiometry for Critical Mineral Mapping Infleqtion plans to deploy quantum gravity gradiometry technology in Colorado by 2027, aiming to significantly reduce the costs associated with critical mineral exploration. Current methods rely heavily on drilling, a process that is both expensive and environmentally disruptive; quantum gravity gradiometry offers a non-invasive alternative for initial subsurface mapping. This technology measures minute variations in Earth’s gravitational field, revealing differences in underground density and geological structure that are often undetectable through conventional surface surveys. Matt Kinsella, CEO of Infleqtion, testified before the House Committee on Natural Resources in July, advocating for this integration and emphasizing the strategic importance of domestic mineral resources. Infleqtion is currently evaluating potential field-test locations within Colorado’s Third Congressional District, with the goal of identifying promising geological structures before committing to drilling. Congressman Jeff Hurd championed the legislation, stating, “America should not have to rely on foreign countries for the critical minerals we need for our economy and national security.” The potential of quantum gravity gradiometry lies in its ability to narrow search areas, allowing exploratio

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SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inferencequantum-computing

SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inference

--> Quantum Physics arXiv:2608.16939 (quant-ph) [Submitted on 11 Aug 2026] Title:SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inference Authors:Nayan D'Souza, Christopher J. Agostino View a PDF of the paper titled SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inference, by Nayan D'Souza and Christopher J. Agostino View PDF HTML (experimental) Abstract:Training variational quantum models requires choosing between parameter-shift gradients, which are exact but cost $O(P)$ forward evaluations, and simultaneous perturbation stochastic approximation (SPSA), which uses only two samples but produces high-variance estimates that can degrade optimisation on small supervised tasks. Whether the cheap gradient is usable depends on the variance that results from different choices of the SPSA perturbation scale, learning rate, and gain-decay schedule. We varied those quantities across a broad grid on a 6-qubit, 60-parameter QNLI classifier and compared the best configurations to parameter-shift AdamW and BuresQNG. AdamW-style SPSA with $c_0=0.01$, $\eta=0.10$, $\gamma=0.10$ reached $55\% \pm 11\%$ test accuracy, improving over the default configuration ($49\% \pm 6\%$) but remaining 16-19 percentage points below the parameter-shift baselines because the two-sample SPSA gradient estimate has too much variance for reliable optimisation of 60 parameters in 40 epochs. Classical-gain SPSA and Bures-preconditioned SPSA performed worse, at $51\%$ and $46\%$ respectively. Bures-preconditioning a noisy two-sample SPSA gradient amplifies perturbation noise. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2608.16939 [quant-ph]   (or arXiv:2608.16939v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.16939 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Christopher Agostino PhD [view email] [v1] Tue, 11 Aug 2026 16:39:36 UTC (115 KB) Full-text links: Acc

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