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-computingOQC, JPMorganChase and AMD commence research collaboration to develop new quantum-AI platform in London
PRESS RELEASE OQC, JPMorganChase and AMD commence research collaboration to develop new quantum-AI platform in London Research integrates OQC GENESIS with AI and high-performance classical computing with JPMorganChase’s industry-leading quantum and AI R&D AMD to provide high-performance computing resources SHARE ARTICLE London, UK — 3 June 2026 — OQC, JPMorganChase and AMD today announced a research collaboration leveraging a new and dedicated Quantum-AI Data Centre, built by OQC in London. JPMorganChase researchers will test near-term quantum and hybrid quantum-classical computing applications via a secure enterprise environment to examine how quantum computing, AI and high-performance classical infrastructure can work together on complex financial services challenges. The partners will use the platform to conduct research on the application of near-term quantum and hybrid quantum-classical computing including areas such as portfolio optimization and expanding explorations around quantum machine learning, while also developing specialized AI models to improve quantum circuit performance. We also plan to investigate how these quantum-enhanced AI models can accelerate the discovery of novel algorithms purpose-built for financial use cases, and the role of classical compute towards scalable fault-tolerant quantum algorithms. JPMorganChase will be OQC’s first dedicated user of the UK platform, which is expected to be fully operational within 12 months. The environment will physically integrate the OQC GENESIS quantum system with AMD-supported AI and classical compute, high performance computing resources and application-level tooling, for simulation, optimisation, AI model development and benchmarking. AMD compute technologies will provide infrastructure to support the AI and classical compute layer of the platform. By placing quantum hardware inside a secure enterprise compute environment, the platform is designed to let JPMorganChase test hybrid quantum-classical
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quantum-computingORCA Computing Integrates Photonic Systems into London Digital Realty Innovation Lab
ORCA Computing Integrates Photonic Systems into London Digital Realty Innovation Lab Full-stack quantum hardware developer ORCA Computing has announced its active participation in the newly launched Digital Realty Innovation Lab (DRIL) in London, a next-generation testing environment engineered by Digital Realty, the world’s largest cloud- and carrier-neutral data center provider. The newly established facility allows enterprise customers and technology developers across the EMEA region to benchmark emerging artificial intelligence and quantum acceleration platforms under live, co-located operational conditions before executing full-scale commercial deployments. By establishing this active infrastructure node in London, the partnership provides organizations with a secure, low-risk foundry to validate hybrid classical-quantum orchestration layers within standard high-performance computing (HPC) data center topologies. Data Center-Native Topologies and Multi-Vendor AI Infrastructure Integration The integration underscores the physical advantages of ORCA’s PT Series photonic quantum computing systems, which utilize room-temperature Time-Bin Interferometer architectures to manipulate quantum states of light within standard server racks. Unlike matter-based quantum processors that require complex liquid-helium dilution refrigerators or ultra-high vacuum containment fields, ORCA’s systems operate natively alongside legacy classical servers and modern graphics processing unit (GPU) arrays without requiring specialized environmental cooling or structural infrastructure retrofits. Backed by strategic software-hardware alliances with industry leaders like NVIDIA, Toyota Tsusho, SiC Systems, and JIJ Inc., ORCA’s deployment inside the DRIL serves as a live validation platform for enterprise users to interleave generative AI workloads, tensor-network computations, and combinatorial optimization routines directly with an on-premises photonic co-processor. Commercial Deployment F
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quantum-computingJIJ and ORCA Computing Demonstrate Hybrid Quantum-Classical Energy Optimization
Quantum optimization software provider JIJ Inc. and photonic hardware developer ORCA Computing, in a joint corporate project with energy conglomerate bp, and the UK National Quantum Computing Centre (NQCC), have released a comprehensive benchmarking white paper validating a hybrid quantum-classical workflow tailored for the energy sector. The industrial study focuses on deploying co-processed optimization routines to solve the Unit Commitment Problem (UCP)—the large-scale computational challenge of scheduling power generator configurations to meet grid demands at the lowest possible economic and environmental cost. Numerical simulations and physical hardware execution indicate that hybrid quantum-classical decomposition models can efficiently scale to manage large-scale grid variables, offering a near-term pathway to outperform purely classical optimization heuristics on industrially relevant energy datasets. The High-Impact Challenge: Modeling the Unit Commitment Problem The Unit Commitment Problem represents one of the energy sector’s most mathematically complex and financially significant operational hurdles. Grid operators must continuously determine the exact start-up, shutdown, and continuous power output timelines for a diverse fleet of generators to satisfy dynamic household and industrial electricity demands. As modern grids integrate highly variable, intermittent renewable energy sources—and as aggregate load requirements expand due to energy-intensive infrastructure like AI data centers—the combinatorial complexity of the UCP scales exponentially. To conduct rigorous hardware benchmarking, the project team utilized the standardized unit_cal_7 dataset verified by bp’s digital R&D team, encompassing 25,755 discrete variables and 48,939 operational constraints, including strict ramp-up/ramp-down limits, minimum duration bounds, and spinning reserve requirements. Full-Stack Algorithmic Pipeline: Dual Decomposition and QUBO Compilation To ingest and p
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quantum-computingIQM Radiance 54 Superconducting Quantum Computer Goes Live at CINECA in Italy
IQM Radiance 54 Superconducting Quantum Computer Goes Live at CINECA in Italy Sovereign hardware developer IQM Quantum Computers, alongside the Italian Research Centre on High Performance Computing, Big Data, and Quantum Computing (ICSC), has announced the official inauguration of the IQM Radiance 54 quantum computer at the CINECA supercomputing facility. Located at the DAMA Tecnopolo in Bologna, the system represents a strategic technology asset engineered to accelerate regional research across complex combinatorial optimization, physical simulations, and quantum machine learning. The deployment deepens Italy’s sovereign computing capacity, marking the first on-premises superconducting quantum platform to go live at CINECA and the second IQM hardware system to become operational nationwide. The NOX QPU Architecture and Co-Located Leonardo Orchestration The newly launched system, designated NOX, is powered by a 54-qubit superconducting quantum processing unit (QPU) built on IQM’s flagship Radiance hardware architecture. To transition the machine from an isolated research prototype into a high-throughput production asset, NOX is structurally integrated into the hardware backplane of Leonardo—one of the world’s fastest pre-exascale supercomputers, ranked 10th on the global Top500 index. This co-located layout enables site operators to execute native, hybrid high-performance computing (HPC) and quantum workflows. By linking classical parallelized GPU-CPU nodes with the low-latency superconducting loops of the Radiance processor, the unified cluster provides researchers with a stabilized environment to test emerging classical-quantum co-processing paradigms. Vertical Supply Chain Control and Nasdaq SPAC Merger Timelines Headed by Chief Commercial Officer Sylwia de Weydenthal, IQM’s on-premises delivery pipeline emphasizes a vertically integrated business model that encompasses in-house chip design software, a custom fabrication foundry, assembly lines, and dedicated dat
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quantum-computingQuantum Network Routing based on Surface Code Error Correction
--> Quantum Physics arXiv:2606.12781 (quant-ph) [Submitted on 11 Jun 2026] Title:Quantum Network Routing based on Surface Code Error Correction Authors:Tianjie Hu, Jindi Wu, Qun Li View a PDF of the paper titled Quantum Network Routing based on Surface Code Error Correction, by Tianjie Hu and 2 other authors View PDF HTML (experimental) Abstract:Quantum networks encounter unavoidable channel noises and erasure errors, presenting a huge obstacle in designing protocols that attain both high reliability and efficiency. Typically, quantum networks fall into two categories: those utilize quantum entanglements for quantum teleportation, and those directly transfer the actual quantum messages. In this paper, we present SurfNet, a quantum network that inherits the main advantages from both categories. It employs surface codes as logical qubits for encoding messages, and utilizes two parallel communication channels to fault-tolerantly transfer each surface code in a modular manner. Our approach of using surface codes can timely correct both operational and photon loss errors within the network, and the integration of the two channels within the network can greatly improve network throughput. For the implementation of SurfNet, we propose a novel network architecture, designed to better integrate surface codes into quantum networks. We also propose a novel error correction decoder, designed to fully utilize the modular characteristic of surface codes within our network. Simulation results demonstrate that SurfNet with its decoder significantly enhances the communication fidelity within quantum networks. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.12781 [quant-ph] (or arXiv:2606.12781v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.12781 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1109/ICDCS60910.2024.00117 Focus to learn more DOI(s) linking to related resources Submission
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quantum-computingPasqal Inaugurates Italy’s First Neutral-Atom Quantum Computer at CINECA Supercomputing Center
Pasqal Inaugurates Italy’s First Neutral-Atom Quantum Computer at CINECA Supercomputing Center Neutral-atom quantum hardware developer Pasqal has officially inaugurated its SOL quantum computer at the DAMA Technopole in Emilia-Romagna, Bologna. Hosted by CINECA, Italy’s largest public supercomputing consortium and a core member of the Italian Research Center on HPC, Big Data, and Quantum Computing (ICSC), the deployment marks the installation of Italy’s first operational neutral-atom quantum platform. Co-funded by the EuroHPC Joint Undertaking (JU) and Italy’s Ministry of University and Research, the system acts as Pasqal’s third federated EuroHPC installation in Europe, following the active placement of identical hardware arrays at computing centers in France and Germany. The Orion QPU Architecture and Heterogeneous Leonardo Integration The technological foundation of the SOL platform relies on Pasqal’s Orion quantum processing unit (QPU), a hardware architecture featuring 140 qubits trapped and manipulated via optical tweezers. Unlike solid-state qubits, neutral-atom platforms use laser arrays to isolate and position individual atoms in multi-dimensional grids, utilizing highly excited Rydberg states to execute multi-qubit entangling operations. The hardware is specifically engineered for tight, low-latency co-processing integration alongside CINECA’s Leonardo pre-exascale supercomputer—currently ranked 10th on the global Top500 supercomputing index. This hybrid configuration allows scientific operators to offload mathematically complex tasks, such as large-scale combinatorial optimization, machine learning modeling, and quantum chemistry simulations, directly to the Orion QPU while relying on Leonardo for heavy classical dataset management. Unified Software Interfacing, Core Schedulers, and Nasdaq Listing Strategy To expose the 140-qubit processor as a native computational accelerator, Pasqal deployed its full-stack high-performance computing (HPC) integration la
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quantum-computingHorizon Quantum Expands Hardware Testbed with 256-Qubit Trapped-Ion System Placement in Ireland
Horizon Quantum Expands Hardware Testbed with 256-Qubit Trapped-Ion System Placement in Ireland Integrated software infrastructure pioneer Horizon Quantum Holdings Ltd. has announced plans to deploy its second hardware testbed location at its European headquarters in Dublin, Ireland. The facility will host a sixth-generation, chip-based 256-qubit trapped-ion system developed by IonQ. Backed by Ireland’s National Semiconductor Strategy (Silicon Island) and supported by IDA Ireland, the hardware expansion builds directly upon the company’s existing multi-vendor superconducting testbed array assembled at its Singapore headquarters. It aims to accelerate the deployment of high-fidelity, hardware-agnostic coding applications across the European Union deep-tech corridor. Trapped-Ion Architecture and Low-Abstraction Compilation Stacks The engineering milestone centers on the integration of IonQ’s 256-qubit processor topology with Horizon Quantum’s native software ecosystem to evaluate real-time runtime compiler execution stacks. While traditional superconducting qubits are fixed geometrically on solid-state chip surfaces, trapped-ion systems utilize individual, electromagnetically isolated ions suspended in free space via micro-fabricated electrode traps, offering long-range connectivity graphs and uniform qubit reproducibility. Horizon Quantum plans to map this noise-biased architectural behavior into its Triple Alpha integrated development environment (IDE). The IDE allows software engineers to compile hardware-agnostic programs across multiple tiers of computational abstraction without manually reconstructing gate-level pulser code for differing quantum modalities. Ecosystem Integration and National Strategic Infrastructure Realization Led by CEO and Founder Dr. Joe Fitzsimons, the deployment strategy anchors high-value operations and specialized engineering teams within Dublin to interface directly with local supply chains, academic research labs, and regional enterpri
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quantum-computingNu Quantum Demonstrates Subsystem Erasure Tolerance in Networked QPU Architectures
Nu Quantum Demonstrates Subsystem Erasure Tolerance in Networked QPU Architectures Distributed quantum hardware developer Nu Quantum Ltd. has reported structural mechanics and numerical simulations validating a fault-tolerant network framework capable of tolerating the complete failure of individual Quantum Processing Units (QPUs). Detailed in a technical manuscript deposited on the open-access arXiv repository, the research introduces a distributed quantum error correction (QEC) paradigm that handles catastrophic hardware node dropouts as correctable localized erasures. By shifting away from large monolithic processors and instead encoding logical information across an interconnected multi-node network, the system prevents the permanent loss of quantum data, allowing continuous computation during both unscheduled subcomponent failures and routine hardware calibration. The Entanglement Fabric Architecture and Node Replacement Loops The structural framework developed by Nu Quantum splits a high-distance global QEC code across multiple modular QPUs, where each individual hardware node hosts an intermediate capacity of 16 to 48 physical qubits. Rather than routing local connections via dense physical layers, non-local gate operations and syndrome extractions spanning separate nodes are mediated through specialized Qubit-Photon Interfaces (QPIs). These optical interfaces generate non-local Bell states and multi-qubit GHZ resource states across a fully connected photonic routing network or “Entanglement Fabric.” When a targeted QPU must be taken offline for scheduled downtime or routine maintenance, the network initiates a transversal physical teleportation protocol, transferring the live state of all hosted data qubits to a standby node before reconfiguring the network’s optical switch routing. Mitigating Unscheduled Failures and Architectural Overhead Savings For unheralded, catastrophic node failures—such as ion chain losses in trapped-ion machines or cosmic-ray quasi
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Pasqal Launches Italy’s First Neutral-Atom Quantum Computer at CINECA
Insider Brief Pasqal has inaugurated Italy’s first neutral-atom quantum computer at CINECA in Bologna, expanding Europe’s hybrid HPC-quantum computing infrastructure. The 140-qubit Pasqal Orion system, named SOL, is integrated with the Leonardo supercomputer to support hybrid quantum-classical workloads. The deployment is Pasqal’s third EuroHPC-linked quantum system in Europe, following earlier installations in France and Germany. PRESS RELEASE — Pasqal, one of the global leaders in neutral-atom quantum computing, today announced the inauguration of Europe’s third Pasqal quantum computer hosted at CINECA, Italy’s largest public supercomputing operator and a member of the Italian Research Center on High Performance Computing (ICSC), Big Data, and Quantum Computing, in Bologna, Italy. The system was unveiled at the DAMA Technopole in Emilia-Romagna during a ribbon-cutting ceremony marking the launch of new high-performance computing (HPC) and quantum computing systems procured by the EuroHPC Joint Undertaking (JU) and co-financed together with the Italy’s Ministry of University and Research through ICSC, including the system delivered by Pasqal. This milestone marks a major step forward in the deployment of Europe’s hybrid HPC and quantum computing infrastructure. The system is Italy’s first neutral-atom quantum computer. Named SOL, it is a Pasqal Orion quantum processing unity (QPU) featuring 140 qubits. It has been engineered for tight integration with the Leonardo pre-exascale EuroHPC supercomputer — one of the world’s most powerful HPC platforms, ranked 10th on the Top500 list– representing an important step forward in quantum accelerated high-performance computing. At CINECA, Pasqal deploys its HPC–quantum integration stack exposing the QPU as a native resource within the supercomputing environment, enabling hybrid workflows that combine quantum and classical computing resources through standard HPC scheduling and operational mechanisms. The deployment build
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quantum-computingHorizon Quantum Selects Dublin for Second Quantum Computer Testbed
Insider Brief Horizon Quantum plans to locate its second quantum computer, an anticipated IonQ 256-qubit trapped-ion system, at its European headquarters in Dublin, Ireland. The system will support Horizon Quantum’s software development platform and expand its hardware testbed beyond the superconducting system already operating in Singapore. Horizon Quantum expects the project to strengthen collaboration with Ireland’s universities, researchers, and broader quantum technology ecosystem. PRESS RELEASE — Horizon Quantum Holdings Ltd. (“Horizon Quantum”), a pioneer of software infrastructure for quantum applications, today announced that it expects to locate its second quantum computer—anticipated to be one of the most advanced commercial quantum systems in the world—in Dublin, Ireland. By placing this IonQ 256-qubit system at its European headquarters, Horizon Quantum aims to benefit from Ireland’s growing quantum ecosystem, strong university network, and robust talent pool for deep-tech development, both within the country and across the EU. Horizon Quantum believes the installation of this frontier system will be a significant technology milestone for the nation, positioning Ireland to play an increasingly prominent role in frontier quantum computing. Minister Peter Burke, Department of Enterprise, Tourism and Employment, said: “I welcome Horizon Quantum’s decision to locate its second quantum computer testbed in Dublin. This significant investment reinforces Ireland’s position at the forefront of advanced technologies and reflects the strength of our growing quantum ecosystem, world-class research base, and highly skilled workforce. The establishment of one of the most advanced commercial quantum systems here is an important milestone that will support innovation, collaboration, and economic growth, while further enhancing Ireland’s ambition to be a global hub for cutting-edge technologies. This also aligns with our strategic focus in Silicon Island—Ireland’s Natio
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quantum-computingIonQ’s 256-Qubit Chip Arrives at Horizon Quantum’s Dublin HQ
Horizon Quantum will install an IonQ 256-qubit system at its Dublin headquarters, establishing a system the company anticipates will be one of the most advanced commercial quantum systems globally. The move positions Ireland to play a prominent role in quantum computing by adding a second quantum computer testbed to the nation’s growing deep-tech infrastructure. Minister Peter Burke, from the Department of Enterprise, Tourism and Employment, welcomed the decision, stating this investment reinforces Ireland’s position in advanced technologies and aligns with Ireland’s National Semiconductor Strategy. By integrating the 256-qubit trapped-ion system with its software tools, Horizon Quantum intends to broaden support for diverse quantum hardware and accelerate the path toward practical quantum applications. This expansion beyond its initial Singapore headquarters signifies a deliberate strategy to build a hardware-agnostic testing environment, crucial for developing robust quantum software infrastructure. The Dublin installation will integrate with Horizon Quantum’s Triple Alpha integrated development environment, enhancing support for trapped-ion systems and bolstering the real-time capabilities of its quantum execution stack; the company aims to unlock quantum advantage through this software-hardware synergy. Horizon Quantum cites Ireland’s expanding quantum ecosystem, strong university network, and availability of skilled deep-tech talent as key factors in the decision. The arrival of this 256-qubit trapped-ion system represents a significant technology milestone for Ireland, positioning the nation to play an increasingly prominent role in quantum computing. This isn’t simply about attracting foreign investment; it’s about strategically building a domestic quantum capability and supply chain. Horizon Quantum anticipates expanding its Irish science and engineering teams to oversee the establishment and management of the new system, deepening engagement with local indu
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quantum-computingAs Google 'refuses' funding from Trump administration for Quantum Computing; Google Quantum AI COO says: - The Times of India
As Google 'refuses' funding from Trump administration for Quantum Computing; Google Quantum AI COO says: The Times of India
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quantum-computingLocally Acting Grover Mixers for Constraint-Preserving QAOA
--> Quantum Physics arXiv:2606.11530 (quant-ph) [Submitted on 10 Jun 2026] Title:Locally Acting Grover Mixers for Constraint-Preserving QAOA Authors:Minjin Choi, Dongkeun Lee, Junghee Ryu View a PDF of the paper titled Locally Acting Grover Mixers for Constraint-Preserving QAOA, by Minjin Choi and Dongkeun Lee and Junghee Ryu View PDF HTML (experimental) Abstract:The Grover mixer quantum alternating operator ansatz (GM-QAOA) employs the Grover mixer to confine the quantum evolution to the feasible subspace defined by the problem. Its mixing unitary, however, requires a global multi-controlled phase-shift gate acting on all qubits, resulting in substantial circuit overhead on near-term quantum devices. In this work, we propose locally acting Grover mixers tailored to initial states that admit a product structure over disjoint qubit subsystems, which may be obtained by encoding only a subset of problem constraints into the initial state preparation. The proposed method preserves the search space defined by the initial state while significantly lowering implementation cost, as the global multi-controlled phase-shift gate is replaced with local operations on disjoint subsystems. Numerical simulations on the exact-cover problem and the traveling salesman problem (TSP) demonstrate that the proposed method achieves convergence behavior comparable to that of the original GM-QAOA, while using shallower circuits with fewer gates. We further compare two constraint encoding strategies for the TSP, encoding only a subset of constraints versus all constraints into the initial state preparation, and show that the former combined with the proposed mixer yields markedly more compact circuits at the point where comparable solution quality is achieved. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.11530 [quant-ph] (or arXiv:2606.11530v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.11530 Focus to learn more arXiv-issued DOI via Dat
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quantum-computingSEC Declares IQM Registration Statement Effective Ahead of SPAC Merger Vote
SEC Declares IQM Registration Statement Effective Ahead of SPAC Merger Vote Sovereign hardware developer IQM Quantum Computers and special purpose acquisition company Real Asset Acquisition Corp. (Nasdaq: RAAQ) have announced that their Form F-4 registration statement has been declared effective by the U.S. Securities and Exchange Commission (SEC). This regulatory milestone clears the path to transition IQM into a publicly traded entity, positioning it to become the first European quantum computing company to list on a major U.S. stock exchange. The corporate transaction is headed toward a final shareholder vote, with the combined company intending to trade its American Depositary Shares on the Nasdaq Global Exchange under the designated ticker symbol IQMX. Shareholder Meeting Scheduling and Capitalization Structure Following the SEC clearance, RAAQ has scheduled an Extraordinary General Meeting for June 25, 2026, where shareholders of record as of June 3, 2026, will vote on the proposed business combination. The transacting entities recently expanded their financial foundation by upsizing private investment in public equity (PIPE) capital commitments to over 146 million USD—anchored by institutional backers like Finnish pension insurer Ilmarinen. This vehicle, paired with roughly 175 million USD held in RAAQ’s trust account and IQM’s existing balance sheet assets, provides a post-merger cash runway of up to 465 million USD. Upon successful execution of the business combination, the newly public entity also plans to apply for a secondary dual listing on the Nasdaq Helsinki exchange under the matching symbol IQMX. Commercial Metrics and Sovereign On-Premises Delivery Pipelines The final proxy disclosures highlight IQM’s commercial scale within the superconducting quantum hardware sector, revealing audited 2025 financial revenues of 31 million EUR (approximately 36 million USD). Operating a vertically integrated business model that spans custom chip design tools, a na
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quantum-computing5th European Summer School on Quantum AI
5th European Summer School on Quantum AI Acronym: EQAI2026Dates: Monday, August 31, 2026 to Friday, September 4, 2026Web page: EQAI2026Registration deadline: Wednesday, July 22, 2026Submission deadline: Saturday, August 15, 2026Tags: quantum computingAIartificial intelligencesummer school💥 Registrations are now open for the 5th edition of the European Summer School on Quantum AI #EQAI2026, taking place from August 31 to September 4, 2026, in Lignano Sabbiadoro, Italy. 🎙️ Speakers announced: Lukasz Cincio (Los Alamos National Laboratory), Adrián Pérez-Salinas (Institute for Theoretical Physics, ETH Zurich), Laia Domingo Colomer (Computer Vision Center, Barcelona), Muhammad Usman (CSIRO – Australia National Research Organisation, University of Melbourne) …and more to be revealed! 👩🏻💻 Don't miss this chance to learn from world-class researchers in the field! Subscribe here ↪ https://ditedi.activehosted.com/f/24 All news, speakers and program updates are available at the official EQAI website ↪ https://eqai.eu/eqai-2026 🚀 Log in or register to post comments
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quantum-computingJGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
--> Quantum Physics arXiv:2606.09964 (quant-ph) [Submitted on 8 Jun 2026] Title:JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks Authors:Gianluca Scanu, Luca Barletta, Stefano Rini View a PDF of the paper titled JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks, by Gianluca Scanu and 2 other authors View PDF Abstract:The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resilience to perturbations are well studied: deep neural networks (DNNs) maintain high performance despite pruning, noise injection, and structural perturbations due to inherent redundancy in their representations. A central challenge in quantum machine learning is to transfer this notion of robustness to quantum neural networks (QNNs) under realistic NISQ noise. While classical deep learning exhibits robustness through structural redundancy, analogous principles for QNNs remain underdeveloped. We propose JGRA: a framework for assessing robustness in noise-aware QNNs via Jacobian geometry, capturing model sensitivity to parameter perturbations induced by noise. Our method includes entropy-matched noise calibration, noise-aware training, and noise-conditioned Jacobian extraction, yielding geometric descriptors that link clean-regime structure to noisy inference behaviour. We also empirically demonstrate that these descriptors encode predictive information about robustness under unseen noise. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2606.09964 [quant-ph] (or arXiv:2606.09964v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.09964 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Gianluca Scanu [view email] [v1] Mon, 8 Jun 2026 15:40:05 UTC (1,770 KB) Full-text links: Access Pape
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quantum-computingAbsence of poor local minima in matrix product states
--> Quantum Physics arXiv:2606.09988 (quant-ph) [Submitted on 8 Jun 2026] Title:Absence of poor local minima in matrix product states Authors:Hao-Kai Zhang, Chenghong Zhu, Shuo Liu, Shi-Xin Zhang, Tao Xiang View a PDF of the paper titled Absence of poor local minima in matrix product states, by Hao-Kai Zhang and 4 other authors View PDF HTML (experimental) Abstract:Quantum circuits suffer from severe trainability issues: even shallow circuits are swamped with poor local minima. Yet matrix product states (MPS), which can be prepared by sequential circuits, are remarkably trainable in practice -- as demonstrated by decades of successful density matrix renormalization group calculations. In this work, we resolve this apparent paradox by proving that the energy landscapes of MPS are free from poor local minima, under the same setting where brickwork circuits are not. The key insight is that the gauge freedom of MPS creates an effective local overparametrization that causes local minima to concentrate near the global minimum, analogous to overparametrized classical neural networks. We rigorously prove that the local minimum distribution of the MPS energy landscape is invariant under moves of the orthogonality center. Numerical experiments further confirm that the optimization of sequential circuits converges to near-optimal solutions even for random Hamiltonians, in stark contrast to brickwork circuits. Our findings highlight the pivotal role of effective local overparametrization in determining trainability, providing a valuable guide for overcoming the trainability bottleneck of variational quantum algorithms. Comments: Subjects: Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Strongly Correlated Electrons (cond-mat.str-el); Computational Physics (physics.comp-ph) Cite as: arXiv:2606.09988 [quant-ph] (or arXiv:2606.09988v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.09988 Focus to learn more arXiv
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quantum-computingQuantum resources in non-stoquastic quantum annealing
--> Quantum Physics arXiv:2606.09995 (quant-ph) [Submitted on 8 Jun 2026] Title:Quantum resources in non-stoquastic quantum annealing Authors:Chiara Capecci, Sebastian Nagies, Naga Dileep Varikuti, Philipp Hauke View a PDF of the paper titled Quantum resources in non-stoquastic quantum annealing, by Chiara Capecci and 3 other authors View PDF HTML (experimental) Abstract:Quantum annealing promises to solve combinatorial optimization problems by preparing the ground state of a target Hamiltonian. Standard annealing protocols are, however, stoquastic and can thus be simulated by sign-problem-free quantum Monte-Carlo methods. To obtain a true quantum advantage, it has been proposed to use non-stoquastic catalyst Hamiltonians. Active only at intermediate stages of the protocol, these can, for certain problems, convert first-order into second-order quantum phase transitions and thus permit an exponential speedup over the stoquastic protocol. At the same time, the non-stoquastic catalyst renders quantum Monte-Carlo methods inefficient. It remains, however, an open question how other classical computation methods are affected by the non-stoquastic terms. We address this question by computing quantum resources -- entanglement entropy and stabilizer Rényi entropy -- whose presence makes classical computations based on tensor networks and stabilizer-tableau methods exponentially hard. We compare these with the spectral gap along the annealing path for two paradigmatic benchmark models, the fully connected $p$-spin model and a geometrically local Ising model. While the exact behavior shows a subtle dependency on the underlying model and the annealing path, our numerics suggest consistently that the scaling of entanglement and non-stabilizerness is at least maintained in the deeply non-stoquastic regime and in some cases even significantly enhanced. Our results thus suggest that improvements of quantum performance in non-stoquastic annealing coincide with significant presence o
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quantum-computingTowards the implementation of a quantum classifier
--> Quantum Physics arXiv:2606.10150 (quant-ph) [Submitted on 8 Jun 2026] Title:Towards the implementation of a quantum classifier Authors:Lorenzo Confalonieri, Adrián Pérez Salinas, Stefano Carrazza View a PDF of the paper titled Towards the implementation of a quantum classifier, by Lorenzo Confalonieri and 2 other authors View PDF HTML (experimental) Abstract:In this work, we investigate the use of a quantum circuit as a binary classification model in the context of quantum machine learning. We call this model, binary quantum classifier. First, we describe fundamental concepts of quantum computing and introduce the computational tool used: Qibo, an open-source framework for efficient quantum simulations and quantum hardware control. Then, we describe how to design a binary quantum classifier for the classification of images and small arrays of variables by showing how to input data in the circuit, defining a quantum circuit model Ansatz with trainable parameters and a loss function, and implementing multiple minimizers. We test our quantum classifier with two data sets. The first one is the MNIST data set which is composed of handwritten digits (reduced to only handwritten zeros and handwritten ones for binary classification). We study the behavior of different minimizers by increasing the number of layers of the Ansatz. The second data set represents two different high energy collisions that can occur at colliders such as LHC (CERN). Due to in-time proton-proton interactions known as pile-up, we distinguish two different data sets: "without pile-up" and "with pile-up". These collisions can be represented by images of size 32x32 or by six high-level variables that we call features. By increasing the size of the training data set and the number of layers of the Ansatz, we search for the best minimizer. Splitting the data set in training set and test set, we compute: ROC curve, AUC score, confusion matrices and test set accuracy. For "with pile-up" images, we compa
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quantum-computingTrainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
--> Quantum Physics arXiv:2606.10179 (quant-ph) [Submitted on 8 Jun 2026] Title:Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization Authors:Gennaro De Luca View a PDF of the paper titled Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization, by Gennaro De Luca View PDF HTML (experimental) Abstract:Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule. Recent work has provided a method to classically train QCBMs with Instantaneous Quantum Polynomial (IQP) circuits via the Maximum Mean Discrepancy (MMD) loss. Despite the assumed intractability of sampling from IQP circuits classically, their expectation values can be computed classically, enabling training of these IQP QCBMs. However, quantum machine learning (QML) models have various other challenges, including trainability issues caused by exponential concentration or barren plateaus. While these issues have been explored for parameters sampled from a uniform distribution, little work has been done to rigorously treat the use of arbitrary Gaussian initialization schemes. This work leverages Stein's lemma and Lipschitz concentration bounds for Gaussian random variables to provide an analytical lower bound of the variance of the gradient and a probabilistic concentration bound of the deviation of the gradient from its mean. It discusses strategies to either avoid or encourage exponential concentration, as well as the conditions under which barren plateaus are more likely to occur. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2606.10179 [quant-ph] (or arXiv:2606.10179v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.10179 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Gennaro De Luca [view email] [v1] Mon, 8 Jun 2026 21:14:21 UTC (13 KB) Full-text links: Access Paper: View a
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