Quantum Computing Finance & Banking: Portfolio Optimization & Risk Analysis
Quantum finance news: JPMorgan, Goldman Sachs quantum banking. Portfolio optimization, risk modeling, Monte Carlo & algorithmic trading.
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.
quantum-computingTerra Quantum AG Valued at $3.25 Billion in SPAC Deal
Terra Quantum AG, a company developing quantum technologies, will become publicly listed through a $3.25 billion deal with Mountain Lake Acquisition Corp. II (Nasdaq: MLAA), indicating strong investor confidence in the pre-public quantum sector. The transaction aims to accelerate the commercialization of Terra Quantum’s algorithms and software, with early adoption already demonstrated across defense, finance, pharmaceuticals, and logistics, industries that are moving beyond theoretical applications. “This milestone marks a significant step forward in Terra Quantum’s mission to deliver practical quantum solutions on a global scale,” said Markus Pflitsch, Chairman & Chief Executive Officer of Terra Quantum AG. The combined entity intends to leverage enhanced access to capital markets to fuel product development, global expansion, and potential strategic acquisitions. $3.25 Billion SPAC Deal for Terra Quantum AG A valuation of $3.25 billion has been placed on Terra Quantum AG through a proposed merger with Mountain Lake Acquisition Corp. II (Nasdaq: MLAA), a special purpose acquisition company, reflecting considerable investor optimism in the pre-public quantum technology firm at a stage where such valuations are uncommon. The deal, structured as a non-binding letter of intent, aims to accelerate Terra Quantum’s development and deployment of quantum technologies, providing access to capital for product refinement, international expansion, and potential acquisitions. This transaction signals a shift from purely theoretical quantum research toward tangible commercial applications, a move supported by early adoption across several key industries. Terra Quantum is already demonstrating commercial traction in sectors including defense, finance, pharmaceuticals, and logistics, indicating the company’s focus extends beyond fundamental research to address practical challenges within established markets. The combined entity intends to strengthen its financial position to fa
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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 […]
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quantum-computingComputing quantum magic of state vectors
AbstractNon-stabilizerness, also known as “magic,'' quantifies how far a quantum state departs from the stabilizer set. It is a central resource behind quantum advantage and a useful probe of the complexity of quantum many-body states. Yet standard magic quantifiers, such as the stabilizer Rényi entropy (SRE) for qubits and the mana for qutrits, are costly to evaluate numerically, with the computational complexity growing rapidly with the number $N$ of qudits. Here we introduce efficient, numerically exact algorithms that exploit the fast Hadamard transform to compute the SRE for qubits ($d=2$) and the mana for qutrits ($d=3$) for pure states given as state vectors. Our methods compute SRE and mana at cost $O(N d^{2N})$, providing an exponential improvement over the naive $O(d^{3N})$ scaling, with substantial parallelism and straightforward GPU acceleration. We further show how to combine the fast Hadamard transform with Monte Carlo sampling to estimate the SRE of state vectors, and we extend the approach to compute the mana of mixed states. All algorithms are implemented in the open-source Julia package HadaMAG, which provides a high-performance toolbox for computing SRE and mana with built-in support for multithreading, MPI-based distributed parallelism, and GPU acceleration. The package, together with the methods developed in this work, offers a practical route to large-scale numerical studies of magic in quantum many-body systems.Featured image: HadaMAG workflow: a quantum state vector $|\psi\rangle$ with $d^N$ amplitudes is fed through $d^N$ fast Hadamard transforms, i.e., butterfly networks of additions and subtractions, to efficiently extract all $d^{2N}$ Pauli expectation values $\langle P \rangle$, from which measures of quantum magic, the stabilizer Rényi entropy $M_2(|\psi\rangle)$ for qubits ($d=2$) and the mana $\mathcal{M}(|\psi\rangle)$ for qutrits ($d=3$), are obtained.Popular summaryStabilizer states form a special class of quantum states that align
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quantum-computingOpen-source Python toolkit for quantum machine learning (variational classifiers, quantum kernels, reproducible workflows)
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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
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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
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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.
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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
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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
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quantum-computingQuantum Fluctuations Broaden Pathways for Faster Particle Transport
A new method to understand particle movement in complex systems has been formulated by extending Lagrangian descriptors into quantum mechanics. Javier Jiménez-López and V. J. García-Garrido, from the Universidad Complutense de Madrid, formulate these descriptors within a path integral framework, incorporating quantum effects into the analysis of particle transport. The formulation reveals that traditionally key boundaries defining movement become blurred due to quantum fluctuations, offering a geometric explanation for tunneling phenomena. Applying this approach with a Hamiltonian saddle establishes a new framework for studying phase space transport and potentially using Lagrangian descriptors in field theory. Quantifying broadened boundaries via path integral averaging of Lagrangian descriptors Invariant manifold widths, a key indicator of quantum tunneling probability, have increased by a factor of 200 when analysed using a new path integral formulation. Previously, accurately quantifying these widths was impossible due to the computational demands of resolving structures smaller than the sampling resolution; this method circumvents that limitation by characterising broadening through path integral averaging. This work introduces a quantum formulation of Lagrangian descriptors, extending their geometric description of classical transport into the quantum realm and establishing a direct link between quantum mechanics and dynamical systems theory. The broadening was demonstrated for a Hamiltonian saddle, a standard problem in physics used to model energy landscapes, where path integral sampling revealed the extent of manifold widening and subsequent barrier penetration. Quantitative analysis showed agreement within 1% between theoretical predictions and Monte Carlo simulations of the manifold width as a function of the number of modes used in the calculation, up to 800 modes. Furthermore, the ratio of broadenings between different systems remained consistent regardl
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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
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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
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quantum-computingSoft-Quantum Algorithms
--> Quantum Physics arXiv:2604.06523 (quant-ph) [Submitted on 7 Apr 2026] Title:Soft-Quantum Algorithms Authors:Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy, Alexey Melnikov View a PDF of the paper titled Soft-Quantum Algorithms, by Basil Kyriacou and 3 other authors View PDF HTML (experimental) Abstract:Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parameters into gate-based operations and optimize the parameters via gradient descent. The high cost of training and low fidelity of current quantum devices, however, restricts much of quantum machine learning to classical simulation. For few-qubit problems with large datasets, training the matrix elements directly, as is done with weight matrices in classical neural networks, can be faster than decomposing data and parameters into gates. We propose a method that trains matrices directly while maintaining unitarity through a single regularization term added to the loss function. A second training step, circuit alignment, then recovers a gate-based architecture from the resulting soft-unitary. On a five-qubit supervised classification task with 1000 datapoints, this two-step process produces a trained variational circuit in under four minutes, compared to over two hours for direct circuit training, while achieving lower binary cross-entropy loss. In a second experiment, soft-unitaries are embedded in a hybrid quantum-classical network for a reinforcement learning cartpole task, where the hybrid agent outperforms a purely classical baseline of comparable size. Comments: Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2604.06523 [quant-ph] (or arXiv:2604.06523v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.06523 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submissio
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quantum-computingQDG Summit: 13 Nations Boost Secure Quantum Technology
Delegates from thirteen nations convened in London to solidify international collaboration on secure quantum technology development, following a summit hosted by the United Kingdom. The fifth meeting of the Quantum Development Group (QDG), comprised of countries including Australia, Canada, France, Germany, and the United States, focused on bolstering research security, investment, and supply chain resilience for this rapidly advancing field. Members agreed to deepen cooperation across these three priority areas to support trusted international collaboration and the safe development of quantum technology. “Quantum has the potential to be one of the most exciting and defining technologies of the coming years,” said Science and Technology Secretary Liz Kendall, “with the power to transform healthcare, energy, defence and transport, its impact will touch all of our lives.” This QDG meeting follows the UK government’s commitment of £2 billion to quantum technologies on March 17th, and a new procurement program, signaling a strong intent to drive innovation and deployment. Thirteen Nations Convene Within Quantum Development Group (QDG) The convergence of thirteen nations within the Quantum Development Group (QDG) signals a heightened focus on securing international leadership in a technology expected to redefine multiple sectors. Convening in London from March 30th to April 1st, representatives from Australia, Canada, Denmark, Finland, France, Germany, Japan, Korea, the Netherlands, Sweden, Switzerland, the UK, and the US collectively reinforced their dedication to the responsible and economically beneficial advancement of quantum technologies. The QDG’s discussions centered on three key areas intended to accelerate progress and mitigate risks: research security, investment security, and supply chain resilience were identified as crucial for fostering trustworthy international collaboration. Members agreed to prioritize deeper engagement between governments and investors
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quantum-computingFewer Measurements Unlock More Precise Quantum Sensing Techniques
Jeongho Bang and colleagues at Yonsei University show that single-shot measurement learning (SSML) acts as an adaptive estimator, preserving the quantum advantages of a probe while using only one classical bit of information per measurement. The research reveals that the process itself provides an inherent measure of accuracy, with longer successful measurement runs indicating higher fidelity. Simulations using photonic states demonstrate that SSML maintains established gains over conventional methods and offers a pathway towards achieving Heisenberg scaling, identifying it as a key set of tools for building self-certifying estimators in quantum sensing applications. Adaptive quantum estimation via iterative refinement and run-length tracking Single-shot measurement learning (SSML) iteratively refines measurements based on simple success or failure outcomes, learning a correction to improve future readings. Unlike traditional quantum sensing techniques which rely on pre-defined, fixed measurement parameters, SSML dynamically adjusts its measurement strategy in response to each outcome. This adaptive nature is crucial for optimising sensitivity and mitigating the effects of environmental noise. The core principle involves learning a ‘compensation unitary’, a quantum operation that corrects for systematic errors in the measurement process. It contrasts sharply with classical estimation methods that often require extensive calibration and are susceptible to biases. SSML not only provides a result but also tracks the length of consecutive successful measurements, the run-length, serving as an intrinsic measure of accuracy. This run-length provides a direct indication of the estimator’s confidence in the obtained result, offering a self-assessment capability absent in many conventional sensing schemes. GHZ/NOON probes with entanglement depth ‘m’ and a fixed total resource were utilised in simulations, with performance assessed using Monte Carlo methods comprising 10 4 tr
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quantum-computingQuantum Networks Gain Secure Path Checks Without Revealing Internal Layouts
Researchers at AIT Austrian Institute of Technology, led by Stephan Krenn, have developed a novel path validation protocol addressing a critical challenge in the deployment of secure, large-scale quantum communication networks. This method enables verification of compliance with security policies within quantum key distribution (QKD) networks without necessitating the disclosure of confidential network topology information. The advancement provides a provably secure and efficient solution, addressing the increasing need for robust trust mechanisms encompassing both hardware and network operators as QKD systems scale towards more extensive, practical implementations. Reduced overhead enables auditable validation of large quantum key distribution networks Communication overhead in quantum key distribution (QKD) networks has been significantly reduced to under 70kB, a substantial improvement compared to previous systems that demanded full topology disclosure for path validation. Traditional approaches to verifying the integrity of QKD networks often required revealing the complete network configuration, detailing node connections and routing paths, to an auditor. This presented a significant security vulnerability, as the disclosure of such sensitive information could itself be exploited. Previously, the need to reveal this information hampered the practical implementation of secure, auditable QKD networks, particularly those exceeding 100 nodes. The new protocol ensures compliance with critical policies, including node certification, verifying the authenticity and security of each repeater node, and path disjointness, guaranteeing that communication paths do not share common links, thereby mitigating the risk of eavesdropping or interference. Strengthening trust in increasingly complex quantum communication infrastructure is paramount for widespread adoption. Formal models and constructions rigorously underpin the protocol’s security, demonstrating its efficiency and
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quantum-computingOrbifold Simulations Reveal Compounding Costs, Scaling up to 10 Billion Times Larger
Henry Lamm and colleagues at FERMILAB show that key hidden costs undermine the potential exponential speedup of orbifold lattices for quantum simulation of Yang-Mills theory. Their analytical derivations, Monte Carlo simulations, and circuit construction reveal compounding issues including a mass-dependent Trotter overhead, gauge-violating dynamics, and a necessary mass extrapolation. Simulations establish a universal scaling that binds the Trotter step to lattice spacing, ultimately demonstrating the orbifold approach to be sharply more expensive, by a factor of $10^$4 to $10^{10}$ for a typical calculation, than previously published alternatives. This clarifies a fundamental limitation in orbifold-based quantum simulation and highlights the challenges remaining in this field. Orbifold lattice simulations exhibit prohibitive computational cost with increasing particle mass A significant performance deficit has been quantified in the orbifold lattice, revealing it to be $10^$4 to $10^{10}$ times more expensive than established quantum simulation alternatives for calculations involving 10³ particles. This dramatic cost increase arises from previously unrecognised computational burdens linked to particle mass, effectively hindering progress towards practical quantum simulation using this method. Detailed analysis reveals a mass-dependent ‘Trotter overhead’ scaling as m⁴, compounded by gauge-violating dynamics growing with m² and a mandatory requirement for mass extrapolation; these factors collectively negate the initially proposed exponential speedup. Universal relationships binding the Trotter step size to lattice spacing were established using Monte Carlo simulations, highlighting a fundamental limitation in the orbifold lattice’s efficiency. For a calculation involving 10³ particles, explicit circuit construction confirmed that the orbifold lattice is between 10⁴ and 10¹⁰ times more expensive than existing quantum simulation methods. The orbifold lattice offers an
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quantum-computingMultipeak and RAQS Partner on Quantum Deployment
Insider Brief Multipeak Global and RAQS Quantum announced a strategic collaboration to accelerate commercial deployment of quantum and quantum-inspired solutions. The partnership will focus on practical industry use cases across sectors such as automotive, energy, manufacturing, and logistics in Asia and Europe. The collaboration combines scientific expertise and commercialization capabilities to help enterprises adopt quantum-ready technologies. PRESS RELEASE — Multipeak Global and RAQS Quantum today announced a strategic collaboration designed to bridge the gap between quantum solutions and commercial deployment. By uniting Multipeak’s deep-tech commercialisation expertise with RAQS Quantum’s scientific leadership, the collaboration aims to deploy high-impact, “quantum-ready” solutions to the Asian and European markets. The partnership aims to accelerate the deployment of next-generation technologies across industries, with a strong focus on quantum and quantum-inspired computing, as well as advanced deep-tech solutions. By integrating strategic advisory, scientific expertise, and execution capabilities, both organisations will support companies in unlocking new levels of innovation and competitiveness. Quantum technology is emerging as a key enabler for solving complex challenges in areas such as supply chain optimisation, financial modelling, material discovery, and energy efficiency. Through this collaboration, Multipeak Global and RAQS Quantum will focus on identifying practical use cases and enabling organisations to adopt quantum-ready, hybrid and quantum-inspired with clear commercial impact, initially, targeting automotive, maritime, energy, manufacturing and logistics industries. The collaboration will begin with projects and customers across Asia and Europe, with plans for further global expansion. Led by Multipeak Global’s CEO Henry Maillet with RAQS Quantum’s leadership team and domain expertise, including Raghunath Koduvayur (Co-founder and CEO),
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quantum-computingTrans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data
AbstractHigh-throughput characterization often requires estimating parameters and model dimension from experimental data of limited quantity and quality. Such data may result in an ill-posed inverse problem, where multiple sets of parameters and model dimensions are consistent with available data. This ill-posed regime may render traditional machine learning and deterministic methods unreliable or intractable, particularly in high-dimensional, nonlinear, and mixed continuous and discrete parameter spaces. To address these challenges, we present a Bayesian framework that hybridizes several Markov chain Monte Carlo (MCMC) sampling techniques to estimate both parameters and model dimension from sparse, noisy data. By integrating sampling for mixed continuous and discrete parameter spaces, reversible-jump MCMC to estimate model dimension, and parallel tempering to accelerate exploration of complex posteriors, our approach enables principled parameter estimation and model selection in data-limited regimes. We apply our framework to a specific ill-posed problem in quantum information science: recovering the locations and hyperfine couplings of nuclear spins surrounding a spin-defect in a semiconductor from sparse, noisy coherence data. We show that a hybridized MCMC method can recover meaningful posterior distributions over physical parameters using an order of magnitude less data than existing approaches, and we validate our results on experimental measurements. More generally, our work provides a flexible, extensible strategy for solving a broad class of ill-posed inverse problems under realistic experimental constraints.Featured image: Schematic of inputs and outputs to a hybrid MCMC algorithmPopular summaryModern experiments—especially in areas like quantum materials and nanotechnology—often face a frustrating challenge: trying to extract detailed physical information from limited and noisy data. In many cases, there isn’t a single “correct” answer. Instead, multiple
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quantum-computingAn Operational Framework for Nonclassicality in Quantum Communication Networks
AbstractQuantum resources, such as entanglement or quantum communication, offer significant communication advantages in information processing. We develop an operational framework for realizing these communication advantages in resource-constrained quantum networks. The framework computes linear bounds on the input/output probabilities of classical networks with limited communication and globally shared randomness. Since the violation of these classical bounds witnesses nonclassicality, a measurable communication advantage, the framework maximizes the violation of the classical bound using variational quantum optimization methods tailored to the communication network and quantum resources. This operational framework for nonclassicality can be scaled on quantum computers or deployed in the field to optimize noisy quantum networks for communication advantages. Applying this framework, we investigate the nonclassicality of communication networks that are assisted by quantum resources. We find that entanglement between communication-constrained parties is sufficient for nonclassicality to be found, whereas in networks with multiple senders, quantum communication with no entanglement-assistance is sufficient for nonclassicality to be found. As a result, entanglement is necessary for nonclassicality when a single sender broadcasts to multiple receivers.Featured image: A classical processor optimizes quantum network hardware for a particular information processing task or demonstration of nonclassicality.Popular summaryQuantum communication resources, such as entanglement, can improve the information processing capabilities of communication networks. These communication advantages are directly related to a measurable property known as nonclassicality. In this work, we develop an approach for realizing nonclassicality by using a classical processor to optimize the communication advantage of simulated or real-world network. Our methods are compatible with quantum processors,
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