Quantum Optimization & Logistics: Supply Chain & Routing Applications
Quantum optimization news: logistics, supply chain quantum, routing optimization, QAOA. Combinatorial optimization & enterprise deployments.
Optimization problems—finding the best solution among millions or billions of possibilities—represent the most immediate commercial application for quantum computing. Logistics, supply chain management, manufacturing, and transportation face combinatorial explosion where classical algorithms struggle.
Quantum approaches include quantum annealing solving optimization natively using quantum tunneling; QAOA (Quantum Approximate Optimization Algorithm) as a gate-based alternative; and quantum-inspired algorithms providing immediate business value on classical hardware.
India's Quantum Optimization Landscape
India's National Quantum Mission prioritizes optimization applications given the country's complex logistics challenges. The Indian Railways, the world's largest employer and passenger carrier, represents a prime use case for quantum scheduling optimization. The NQM Thematic Hub at IIT Bombay focuses on quantum algorithms for optimization problems.
Tata Consultancy Services (TCS) develops quantum optimization solutions for Indian enterprises including supply chain, logistics, and manufacturing applications. The Quantum Valley Tech Park in Andhra Pradesh, anchored by an IBM Quantum System Two with 156-qubit Heron processor, targets optimization applications among its use cases including supply chain resilience and energy optimization.
The NQM specifically targets quantum computing applications in optimization, with intermediate-scale quantum computers expected to demonstrate utility in logistics and scheduling problems within the mission timeline.
quantum-computingQuantum AI Shortcut Could Speed up Language Models with Reduced Complexity
Scientists are developing novel methods to improve sequence prediction, a crucial task in areas such as natural language processing and dynamical systems modelling. Alessio Pecilli and Matteo Rosati, both from the Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche at the Universit`a degli Studi Roma Tre, alongside et al., present a variational implementation of self-attention, termed Quantum Attention by Overlap Interference (QSA), which leverages quantum principles to predict future sequence elements. This research is significant because QSA achieves nonlinearity through state overlap interference and directly calculates a loss function as an observable expectation value, circumventing conventional decoding processes. Moreover, the team demonstrates that QSA exhibits potentially advantageous computational scaling compared to classical methods and successfully learns sequence prediction from both classical data and complex many-body quantum systems, establishing a trainable attention mechanism for dynamical modelling. Quantum self-attention via direct Renyi-1/2 entropy measurement Scientists have developed a novel quantum self-attention mechanism, termed QSA, that directly addresses computational bottlenecks within transformer architectures and large language models. This breakthrough focuses on the core self-attention operation, crucial for predicting sequential data by weighting combinations of past information. Unlike previous quantum approaches, the research realizes necessary non-linearity through interference of quantum state overlaps, directly translating a Renyi-1/2 cross-entropy loss into an expectation value measurable as an observable. This innovative design bypasses the need for complex decoding processes typically required to convert quantum predictions into classical outputs, streamlining the training procedure. Furthermore, the QSA naturally integrates a trainable data-embedding, establishing a direct link between quantum s
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quantum-computingQuantum Compilation Speeds up 100x, Bringing Practical Quantum Computers Closer
Researchers are tackling the challenge of efficiently translating complex quantum algorithms into instructions for near-term quantum hardware. Aaron Hoyt from University of Washington and Pacific Northwest National Laboratory, alongside Meng Wang and Fei Hua from Pacific Northwest National Laboratory, et al., present QASMTrans, a novel end-to-end quantum compilation framework designed for just-in-time deployment. This work is significant because QASMTrans achieves over 100x faster compilation speeds than existing tools like Qiskit on certain circuits, while maintaining comparable fidelity and uniquely offering direct integration with hardware control systems via pulse generation. By bridging the gap between logical circuits and physical implementation, and incorporating noise-aware optimisation and circuit space sharing, QASMTrans facilitates the development and execution of real-time adaptive quantum algorithms on current quantum processing units. Rapid Quantum Circuit Transpilation via Pulse-Level Gate Set Optimisation Scientists have unveiled QASMTrans, a high-performance quantum compiler designed to rapidly translate abstract quantum algorithms into device-specific control instructions. This C++-based framework achieves over 100x faster compilation than existing tools like Qiskit for certain circuits, enabling the transpilation of large, complex circuits in a matter of seconds. QASMTrans distinguishes itself by offering complete, end-to-end device-pulse compilation and direct integration with quantum control systems such as QICK, effectively bridging the gap between logical circuits and the underlying hardware. The research focuses on accelerating the process of transpilation, which converts high-level quantum circuits into a format compatible with the limitations of near-term quantum devices. By employing latency-aware Application-tailored Gate Sets at the pulse level, QASMTrans identifies critical sequences within a circuit and generates optimized pulse schedu
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quantum-computingQuantum Algorithm Cuts Molecular Energy Calculations’ Costs with Streamlined Approach
Scientists are continually seeking improvements to variational quantum eigensolver algorithms for accurate molecular ground state energy calculations. Runhong He, Xin Hong (Key Laboratory of System Software, Chinese Academy of Sciences), and Qiaozhen Chai, alongside Ji Guan, Junyuan Zhou, and Arapat Ablimit, present a novel approach to enhance the adaptive derivative-assembled pseudo-trotter variational eigensolver (ADAPT-VQE). Their research introduces Param-ADAPT-VQE, an algorithm that intelligently selects excitation operators using a parameter-based criterion, effectively reducing redundancy and associated measurement costs. By combining this with a sub-Hamiltonian technique and a hot-start optimisation strategy, the authors demonstrate significant gains in computational accuracy and scalability, paving the way for more practical applications of ADAPT-VQE in molecular simulations. Parameter selection optimises variational quantum eigensolver performance for molecular simulations, leading to improved accuracy and efficiency Scientists have developed a new algorithm, Param-ADAPT-VQE, that significantly enhances the efficiency of molecular ground state energy calculations performed on quantum computers. This breakthrough addresses critical limitations in existing methods by reducing computational inaccuracies, minimising the size of the required quantum circuits, and dramatically lowering the number of measurements needed to achieve reliable results. The research introduces a parameter-based criterion for selecting excitation operators, a key component in building the quantum circuit, effectively avoiding the inclusion of redundant operators that hinder performance. This innovative approach moves beyond traditional gradient-based methods, offering a more robust and streamlined pathway to accurate molecular simulations. The core of this advancement lies in the optimisation of the adaptive derivative-assembled pseudo-trotter variational quantum eigensolver, or ADAPT-
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quantum-computingDST Task Force Report: India Prepares for Post-Quantum Security by 2028
India is preparing to defend its digital infrastructure against the looming threat of quantum computing, with a national task force outlining a roadmap to achieve post-quantum security by 2028. The February 2026 report, “Implementation of Quantum Safe Ecosystem in India,” details a phased approach, beginning with pilot programs in critical sectors like banking and finance. Recognizing the risk of “Harvest Now, Decrypt Later” (HNDL) attacks, the Task Force emphasizes proactive measures, stating that all cryptographic transition planning shall proceed under an “assume breach” principle. This ambitious plan includes establishing a National PQC Testing & Certification Program by December 2026 and mandating the adoption of quantum-safe products in government procurement, signaling a significant investment in future-proof cybersecurity. Quantum Computing Threat & India’s National Quantum Mission This isn’t a distant concern; the report outlines phased actions, beginning with pilots in high-priority systems like banking and finance, to be implemented by 2028, with Critical Information Infrastructure (CII) targeted by 2027. Procurement requirements will prioritize “crypto-agile and PQC-compliant assets,” including a detailed “Bill of Materials (BOM)” encompassing software, hardware, and cryptographic configurations. Furthermore, the report emphasizes the need to “promote the adoption of existing indigenous quantum-safe solutions” developed by Indian R&D labs, industries, and startups, while simultaneously initiating new product development where gaps exist. This strategic roadmap positions India alongside nations formally defining PQC migration timelines, aiming for a secure and resilient digital future. Report of the Task Force: Sub-Group Summaries The current landscape of cryptographic security is bracing for a paradigm shift, driven by the rapidly approaching threat of quantum computing. Short-term actions, targeted for completion by 2028 – and 2027 for Criti
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quantum-computingEntanglement reveals the difficulty of computational problems
Adiabatic quantum computing An example problem represented by an energy landscape. Each point on the landscape represents a candidate solution. The deepest valley represents the actual solution with the lowest energy in dark blue. A difficult problem involves multiple valleys with similar depth and therefore similar energy. Arriving at the solution – the lowest energy valley – requires a large amount of entanglement and time. This is where quantum speed-up can be most crucial. (Courtesy: Einar Gabbassov)"> Adiabatic quantum computing An example problem represented by an energy landscape. Each point on the landscape represents a candidate solution. The deepest valley represents the actual solution with the lowest energy in dark blue. A difficult problem involves multiple valleys with similar depth and therefore similar energy. Arriving at the solution – the lowest energy valley – requires a large amount of entanglement and time. This is where quantum speed-up can be most crucial. (Courtesy: Einar Gabbassov) Entanglement is a key resource for quantum computation and quantum technologies, but it can also tell us much about a computational problem. That is the conclusion of a recent paper by Achim Kempf and Einar Gabbassov – who are applied mathematicians at Canada’s University of Waterloo and are affiliated with Waterloo’s Institute for Quantum Computing and the Perimeter Institute for Theoretical Physics. Writing in Quantum Science and Technology, Gabbassov and Kempf show how entanglement plays a fundamental role in determining both the efficiency and the hardness of quantum computation problems. They considered the role of entanglement in adiabatic quantum computing. This considers a landscape of hills and valleys (the problem) where the shape of the landscape depends on the problem to be solved. A point on the landscape represents a candidate solution to the problem. This could be a configuration of possible states of three qubits, for example, or “a possible
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Quantum Computers Sidestep Major Flaw, Paving Way for Larger, More Accurate Calculations
Scientists are increasingly exploring variational quantum eigensolvers as practical approaches to prepare ground states, but their potential for quantum advantage remains unclear. Baptiste Anselme Martin from Eviden Quantum Lab and Thomas Ayral from CPHT, CNRS, Ecole Polytechnique, IP Paris, alongside et al., demonstrate a novel method utilising differentiable 2D tensor networks to optimise parameterised circuits for the transverse field Ising model. This research is significant because it enables the preparation of highly accurate ground states for systems exceeding one dimension and crucially, mitigates the detrimental barren plateau issue by identifying enhanced gradient zones that maintain performance as system size increases. By evaluating the classical simulation cost at these optimised starting points, the team delineate regimes where quantum hardware may ultimately outperform tensor network simulations. Tensor network pre-optimisation overcomes barren plateaus in variational quantum circuits by improving initial parameterisation Researchers are pioneering a new approach to harness the power of quantum computing by integrating classical tensor network algorithms with parameterized quantum circuits. This work details the use of differentiable two-dimensional tensor networks to optimize circuits designed to prepare the ground state of the transverse field Ising model, achieving high energy accuracy even for complex systems exceeding one-dimensional limitations. The study demonstrates that pre-optimization using tensor networks effectively mitigates the barren plateau issue, a significant obstacle in quantum computation, by unlocking enhanced gradient zones that maintain their size even as system complexity increases. Specifically, the research focuses on optimizing quantum circuits using projected entangled pair states, a type of two-dimensional tensor network, combined with automatic differentiation techniques. This method allows for the efficient preparation
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quantum-computingQuantum Computer Controls Refined to Pinpoint Sources of Error in Calculations
Researchers are increasingly focused on mid-circuit measurements as essential building blocks for achieving scalable quantum computation. Piper C. Wysocki (University of New Mexico and Sandia National Laboratories), Luke D. Burkhart (MIT Lincoln Laboratory), and Madeline H. Morocco (MIT Lincoln Laboratory) et al. present a detailed characterisation of these measurements on a transmon qubit, offering a significant advance in understanding their underlying mechanisms. Their work tackles the difficulty of interpreting experimentally obtained measurement data by adapting a generator formalism, previously used for noisy quantum gates, to mid-circuit measurements. By deploying this new analysis, the team successfully quantified contributions from amplitude damping, readout errors, and imperfect state collapse, demonstrating a parsimonious model that recovers key features of dispersive readout and provides a more physically intuitive understanding of this crucial quantum process. Characterising mid-circuit measurement errors using an error generator formalism Researchers have developed a new method for dissecting and understanding errors within mid-circuit measurements, a crucial component for building large-scale, fault-tolerant quantum computers. These measurements, which read qubit states during computation without fully collapsing them, are essential for quantum error correction and advanced quantum algorithms. However, characterizing the errors inherent in these mid-circuit measurements has proven challenging, limiting the ability to debug and improve quantum circuits. This work introduces a framework adapting the error generator formalism, previously used to analyze noisy quantum gates, to the unique characteristics of mid-circuit measurements. The study overcomes a key obstacle by constructing a representation of errors that mirrors the established error generators used for logic gates, despite the fundamentally different nature of mid-circuit measurement transfer m
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quantum-computingOQC and QinetiQ Demonstrate Critical Quantum Computing Application for Defence and Security
PRESS RELEASE OQC and QinetiQ Demonstrate Critical Quantum Computing Application for Defence and Security SHARE ARTICLE London, UK — 10 February 2-26 — OQC and QinetiQ have announced the successful completion of a joint research project demonstrating how quantum computing can be applied to strengthen the security and resilience of defence communication networks. Using OQC’s Toshiko quantum computer and cloud-accessible API, QinetiQ integrated its Quantum Approximation Optimisation Algorithm (QinetiQAOA) to identify critical nodes in Mobile Ad-Hoc Networks (MANETs) – the dynamic, infrastructure-free networks used in military and emergency operations. The collaboration represents a significant step forward in applying quantum computing to real-world defence challenges, proving that quantum systems have the potential to offer valuable insights into complex network vulnerabilities and support more secure, adaptive communications in the field. Unlocking Quantum Advantage for Defence The project used OQC’s Toshiko system to model MANETs as optimisation problems, helping to identify nodes whose failure could disrupt mission-critical communications. The results demonstrated that quantum algorithms can successfully pinpoint these vulnerabilities: a breakthrough with direct implications for network resilience, cyber defence, and operational planning. By analysing network topologies, QinetiQ and OQC showed how defence organisations could one day use quantum computing to: Strengthen communications networks against interference or attack; Optimise logistics and mission planning; Support more informed decision-making in dynamic environments. “This project is a tangible example of quantum computing’s power to deliver real operational value,” said Gerald Mullally, CEO of OQC. “Working with QinetiQ has shown how sovereign quantum technology can be applied today to challenges that directly impact defence capability.” While the project successfully identified critical nodes within com
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quantum-computingPutting fermions onto a digital quantum computer
--> Quantum Physics arXiv:2602.07151 (quant-ph) [Submitted on 6 Feb 2026] Title:Putting fermions onto a digital quantum computer Authors:Riley W. Chien, Mitchell L. Chiew, Brent Harrison, Jason Necaise, Weishi Wang, Maryam Mudassar, Campbell McLauchlan, Thomas M. Henderson, Gustavo E. Scuseria, Sergii Strelchuk, James D. Whitfield View a PDF of the paper titled Putting fermions onto a digital quantum computer, by Riley W. Chien and 10 other authors View PDF HTML (experimental) Abstract:Quantum computers are expected to become a powerful tool for studying physical quantum systems. Consequently, a number of quantum algorithms for studying the physical properties of such systems have been developed. While qubit-based quantum computers are naturally suited to the study of spin-1/2 systems, systems containing other degrees of freedom must first be encoded into qubits. Transformations to and from fermionic degrees of freedom have long been an important tool in physics and, now the simulation of fermionic systems on quantum computers based on qubits provides yet another application. In this perspective, we review methods for encoding fermionic degrees of freedom into qubits and attempt to dispel the persistent notion that fermionic systems beyond one dimension are fundamentally more difficult to deal with. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.07151 [quant-ph] (or arXiv:2602.07151v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.07151 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: James Whitfield [view email] [v1] Fri, 6 Feb 2026 19:50:44 UTC (352 KB) Full-text links: Access Paper: View a PDF of the paper titled Putting fermions onto a digital quantum computer, by Riley W. Chien and 10 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-02 References &am
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quantum-computingEncoding Matters: Benchmarking Binary and D-ary Representations for Quantum Combinatorial Optimization
--> Quantum Physics arXiv:2602.07357 (quant-ph) [Submitted on 7 Feb 2026] Title:Encoding Matters: Benchmarking Binary and D-ary Representations for Quantum Combinatorial Optimization Authors:Shashank Sanjay Bhat, Peiyong Wang, Joseph West, Udaya Parampalli View a PDF of the paper titled Encoding Matters: Benchmarking Binary and D-ary Representations for Quantum Combinatorial Optimization, by Shashank Sanjay Bhat and 2 other authors View PDF HTML (experimental) Abstract:Combinatorial optimization problems are typically formulated using Quadratic Unconstrained Binary Optimization (QUBO), where constraints are enforced through penalty terms that introduce auxiliary variables and rapidly increase Hamiltonian complexity, limiting scalability on near term quantum devices. In this work, we systematically study Quadratic Unconstrained D-ary Optimization (QUDO) as an alternative formulation in which decision variables are encoded directly in higher dimensional Hilbert spaces. We demonstrate that QUDO naturally captures structural constraints across a range of problem classes, including the Traveling Salesman Problem, two variants of the Vehicle Routing Problem, graph coloring, job scheduling, and Max-K-Cut, without the need for extensive penalty constructions. Using a qudit-level implementation of the Quantum Approximate Optimization Algorithm (qudit QAOA), we benchmark these formulations against their binary QUBO counterparts and exact classical solutions. Our study show consistently improved approximation ratios and substantially reduced computational overhead at comparable circuit depths, highlighting QUDO as a scalable and expressive representation for quantum combinatorial optimization. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.07357 [quant-ph] (or arXiv:2602.07357v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.07357 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shashank
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quantum-computingRecursive QAOA for Interference-Aware Resource Allocation in Wireless Networks
--> Quantum Physics arXiv:2602.07483 (quant-ph) [Submitted on 7 Feb 2026] Title:Recursive QAOA for Interference-Aware Resource Allocation in Wireless Networks Authors:Kuan-Cheng Chen, Hiromichi Matsuyama, Wei-hao Huang, Yu Yamashiro View a PDF of the paper titled Recursive QAOA for Interference-Aware Resource Allocation in Wireless Networks, by Kuan-Cheng Chen and 3 other authors View PDF HTML (experimental) Abstract:Discrete radio resource management problems in dense wireless networks are naturally cast as quadratic unconstrained binary optimization (QUBO) programs but are difficult to solve at scale. We investigate a quantum-classical approach based on the Recursive Quantum Approximate Optimization Algorithm (RQAOA), which interleaves shallow QAOA layers with variable elimination guided by measured single- and two-qubit correlators. For interference-aware channel assignment, we give a compact QUBO/Ising formulation in which pairwise interference induces same-channel couplings and one-hot constraints are enforced via quadratic penalties (or, optionally, constraint-preserving mixers). Within RQAOA, fixing high-confidence variables or relations reduces the problem dimension, stabilizes training, and concentrates measurement effort on a shrinking instance that is solved exactly once below a cutoff. On simulated instances of modest size, including a four-user, four-channel example, the method consistently returns feasible assignments and, for the demonstrated case, attains the global optimum. These results indicate that recursion can mitigate parameter growth and feasibility issues that affect plain QAOA, and suggest a viable pathway for near-term quantum heuristics in wireless resource allocation. Subjects: Quantum Physics (quant-ph); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2602.07483 [quant-ph] (or arXiv:2602.07483v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.07483 Focus to learn more arXiv-issued DOI v
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quantum-computingPhysicists discover what controls the speed of quantum time
Science News from research organizations Physicists discover what controls the speed of quantum time Date: February 9, 2026 Source: Ecole Polytechnique Fédérale de Lausanne Summary: Time may feel smooth and continuous, but at the quantum level it behaves very differently. Physicists have now found a way to measure how long ultrafast quantum events actually last, without relying on any external clock. By tracking subtle changes in electrons as they absorb light and escape a material, researchers discovered that these transitions are not instantaneous and that their duration depends strongly on the atomic structure of the material involved. Share: Facebook Twitter Pinterest LinkedIN Email FULL STORY Quantum events are not instantaneous, and their timing depends on the hidden structure of the material itself. Credit: Shutterstock "The concept of time has troubled philosophers and physicists for thousands of years, and the advent of quantum mechanics has not simplified the problem," says Professor Hugo Dil, a physicist at EPFL. "The central problem is the general role of time in quantum mechanics, and especially the timescale associated with a quantum transition." At the smallest scales, physical processes unfold at astonishing speeds. Events such as tunneling or an electron shifting to a new energy state after absorbing light can happen in just a few tens of attoseconds (10-18 seconds). That interval is so brief that even light would not travel across the width of a small virus during that time. Why Measuring Quantum Time Is So Difficult Tracking such tiny slices of time has proven extremely challenging. Any external timing device risks interfering with the fragile quantum process being studied and changing its behavior. "Although the 2023 Nobel prize in physics shows we can access such short times, the use of such an external time scale risks to induce artifacts," Dil says. "This challenge can be resolved by using quantum interference methods, based on the link betwee
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quantum-computingFaster Quantum Simulations Unlock New Materials and Drug Discoveries
Scientists tackling the simulation of quantum many-body systems face a persistent challenge due to the exponential growth of computational complexity. Belal Abouraya from the German University in Cairo, Jirawat Saiphet from the University of Tübingen, and Fedor Jelezko et al. present a new method to improve the efficiency of matrix product states (MPS), a key technique for modelling one-dimensional quantum systems. Their research introduces a streamlined approach to simulating time-dependent Hamiltonians, achieving second-order convergence, a significant improvement over standard first-order methods. Demonstrating this advancement with simulations of nitrogen-vacancy colour centres in diamond, the team shows a reduction in average error by a factor of approximately 1000, potentially enabling more accurate and scalable modelling for future quantum technologies. High-order quadrature improves time-dependent quantum many-body simulations Researchers have developed a new method for simulating the complex behaviour of quantum many-body systems, addressing a long-standing challenge in physics and quantum information science. These systems are notoriously difficult to model due to the exponential growth in computational requirements as the system size increases. This work introduces an efficient augmentation to existing matrix product state (MPS) algorithms, enabling more accurate and faster simulations of time-dependent quantum dynamics. The proposed technique achieves second-order convergence, a significant improvement over the first-order convergence of standard methods currently employed. The breakthrough centres on replacing the instantaneous Hamiltonian used in conventional MPS time-evolution solvers with a carefully calculated average Hamiltonian derived from a high-order quadrature rule. This approach, inspired by classical numerical integration techniques like Simpson’s rule, allows for a more precise approximation of the time evolution operator over short time in
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quantum-computingFlynn Zito Dumps 100,000 D-Wave Quantum Shares Worth $2.9 Million
Specializing in quantum computing hardware and cloud services, D-Wave Quantum serves enterprise clients across diverse industries.On February 5, 2026, Flynn Zito Capital Management disclosed in an SEC filing that it sold 100,000 shares of D-Wave Quantum (QBTS +2.37%).What happenedAccording to a SEC filing dated February 5, 2026, Flynn Zito Capital Management reduced its stake by 100,000 shares in D-Wave Quantum during the fourth quarter of 2025. The estimated value of shares sold, calculated using the average closing price for the quarter, was $2.91 million. The fund’s position value in D-Wave Quantum declined by $2.41 million over the quarter, a figure that includes both trading and price movements.What else to knowThis was a sell, leaving the D-Wave Quantum position at 0.37% of the fund’s 13F AUM after the transaction.Top five holdings after the filing:NYSEMKT: HFXI: $20,609,453 (7.4% of AUM)NYSEMKT: PRF: $20,383,474 (7.3% of AUM)NASDAQ: AAPL: $20,002,736 (7.1% of AUM)NYSEMKT: IWF: $18,790,446 (6.7% of AUM)NYSEMKT: FLQM: $17,814,776 (6.4% of AUM)As of February 5, 2026, shares of D-Wave Quantum were priced at $17.21, up 174.9% over the past year with one-year alpha of 162.76 percentage points versus the S&P 500.Company overviewMetricValuePrice (as of market close February 5, 2026)$17.21Market capitalization$6.31 billionRevenue (TTM)$24,144,000Net income (TTM)($398,813,000)Company snapshotProvides quantum computing systems, cloud-based quantum access, professional onboarding services, and open-source software tools.Generates revenue through hardware sales, cloud subscriptions, and enterprise quantum consulting and deployment services.Targets manufacturing, logistics, financial services, life sciences, and other sectors seeking advanced computational solutions.D-Wave Quantum is a technology company specializing in the development and commercialization of quantum computing hardware, software, and cloud-based services. The company leverages its proprietary quantum
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quantum-computingQuantum Measurements Force Spin Chains to Develop Long-Range Connections
Researchers investigating the transition between short- and long-range ordered states in quantum spin chains demonstrate how local measurements fundamentally alter quantum entanglement. Sven Bachmann, Mahsa Rahnama, and Gabrielle Tournaire, all from The University of British Columbia, detail how on-site measurements of local charge on expanding intervals transform initial short-range entangled states into those exhibiting increasingly long-range correlations. This work establishes that post-measurement states consistently deviate from uniform short-range entanglement, and, utilising a cellular automaton to generate initial states, the authors construct infinite-volume post-measurement states with demonstrably strong, almost local correlations, offering significant insight into the dynamics of entanglement and its response to observation. Local measurements induce long-range entanglement in symmetry-protected topological spin chains Researchers have demonstrated a fundamental shift in the behaviour of quantum spin chains through the application of local measurements. This work details how these measurements induce a transition from states exhibiting short-range entanglement to those with long-range correlations, a phenomenon previously understood to require more complex interactions. Specifically, the study focuses on infinite spin chains initially in a symmetry-protected topological phase possessing an Abelian symmetry group. By performing on-site measurements of the local charge, researchers induced increasingly long-range correlations within the system, definitively establishing that the resulting post-measurement states are not uniformly short-range entangled. The research builds upon the established understanding of topological order and its relation to local unitaries, highlighting how local measurements offer a distinct pathway to create long-range entanglement. In instances where the initial state originates from a product state via a quantum cellular automat
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quantum-computingQuantum-Proof Software Tools Tackle Looming Cyber Threats with Novel Adaptation Framework
Scientists are increasingly focused on the impending threat to current cybersecurity infrastructure posed by the development of quantum computers. Lei Zhang from the University of Maryland, Baltimore County, and colleagues demonstrate that transitioning to post-quantum cryptographic (PQC) algorithms requires more than simply updating software libraries. This research highlights a significant challenge, as existing software engineering tools struggle with the unique characteristics of PQC, including probabilistic behaviour and performance complexities. The authors outline a vision for a new field, Quantum-Safe Software Engineering (QSSE) , and introduce the Automated Quantum-Safe Adaptation (AQuA) framework, proposing a three-pillar approach to PQC-aware detection, refactoring, and verification, thereby establishing a crucial research direction for future cybersecurity development. Migrating existing software to these new, quantum-resistant algorithms is proving far more complex than a simple library update. The research centres on a vision for a new generation of tools capable of intelligently adapting legacy software for a post-quantum world. The AQuA framework is built around a three-pillar agenda focusing on PQC-aware detection, semantic refactoring, and hybrid verification. This integrated pipeline aims to automate the process of identifying cryptographic components within existing codebases, restructuring them to accommodate post-quantum algorithms, and rigorously verifying the security and performance of the resulting system. The framework directly addresses the limitations of current cryptographic inventories, which lack the code-level semantics needed for safe and efficient transformation. Specifically, the study highlights three key gaps in current PQC migration strategies. Existing approaches fail to capture how cryptographic operations are embedded within a system’s control and data flow, lack systematic refactoring patterns for PQC algorithms, and lack c
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quantum-computingQuantum Advice Cuts Communication Needed to Wake up Networks of Nodes
Researchers are tackling the fundamental distributed wake-up problem, investigating how to efficiently activate sleeping nodes in a network given limited initial knowledge. Peter Robinson and Ming Ming Tan, both from Augusta University, present novel upper and lower bounds on message complexity within the routing model. Their work establishes an algorithm achieving a message complexity of with high probability, utilising bits of advice per node and surpassing previous limitations in dense graphs. Complementing this, Robinson and Tan demonstrate a lower bound of for wake-up without advice, a result with broad implications as many core graph problems, including single-source broadcast and spanning tree construction, inherently rely on solving wake-up first. This research addresses a fundamental challenge in network communication: efficiently waking up all nodes in a network after an adversary activates only a subset. The work introduces a novel distributed advising scheme that, given α bits of advice per node, successfully wakes up all nodes with a message complexity of O q n3 2max{⌊(α−1)/2⌋,0} · log n with high probability. This result surpasses the Ω n2 2α barrier previously known for classical algorithms in sufficiently dense graphs, demonstrating a substantial improvement in efficiency. The core of this advancement lies in leveraging quantum communication capabilities to minimize the number of messages required for wake-up. Researchers demonstrate that by utilising α bits of advice per node, the algorithm achieves a message complexity scaling with the cube of the number of nodes, n, and a logarithmic factor. This contrasts sharply with classical approaches, which face inherent limitations in message efficiency. Complementing this algorithmic achievement is a rigorous lower bound proof, establishing that wake-up requires a quantum message complexity of Ω n3/2 even without any advice. This bound holds regardless of the allotted computation time, highlighting a funda
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quantum-computingAI Predicts Quantum System Behaviour for Faster, More Reliable Control
Scientists are increasingly focused on achieving high-fidelity control in quantum systems, a necessity for robust information processing, especially given the challenges posed by environmental noise. JunDong Zhong and ZhaoMing Wang, from their respective institutions, alongside et al., present a novel optimal control framework that leverages the predictive power of long short-term memory neural networks (LSTM-NNs) to streamline control design for open quantum systems. This research significantly advances the field by replacing computationally intensive numerical simulations of system dynamics with LSTM-NN predictions, enabling rapid, efficient optimisation. They demonstrate the effectiveness of their approach through the design of an optimal control scheme for adiabatic speedup in a two-level system, achieving fidelity improvements across both trajectory and pulse optimisation stages and offering potential applications in diverse areas such as computing and communication. Scientists recognise quantum control as crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional optimal control designs typically require numerical calculations of the system dynamics. Recent studies have demonstrated that long short-term memory neural networks (LSTM-NNs) can accurately predict the time evolution of open quantum systems. Based on LSTM-NN predicted dynamics, we propose an optimal control framework for rapid and efficient optimal control design in open quantum systems. As an exemplary example, we apply our scheme to design an optimal control for the adiabatic process. Optimisation of pulse sequences for enhanced adiabatic speedup is crucial for practical quantum computation Scientists are investigating adiabatic speedup in a two-level system within a non-Markovian environment. Their optimization procedure comprises two steps: driving trajector
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quantum-computingQuantum Routing Cuts Network Delays Even with Two Link Failures Simultaneously
Researchers are increasingly focused on optimising network resilience against simultaneous link failures, a critical challenge for modern telecommunications infrastructure. This work presents a novel approach to latency-resilient Layer 3 routing, formulated as a graph-based optimisation problem and adapted for solution using quantum computing. Led by Maher Harb, Nader Foroughi, and Matt Stehman of Comcast Corporation, alongside Nati Erez and Erik Garcell of Classiq Technologies et al., this study demonstrates the potential of the quantum approximate optimisation algorithm to minimise latency and maximise network robustness under dual-link failure scenarios. Significantly, the findings validate the proposed mathematical formulation by achieving optimal network designs on both quantum simulators and hardware, paving the way for future quantum solutions to complex network optimisation problems. Scientists address the latency-resilient Layer 3 routing optimisation problem in telecommunications networks with predefined Layer 1 optical links. The research formulates this problem as a graph-based optimisation problem with the objective of minimising latency, creating vertex-disjoint paths from each site to the internet backbone, and maximising overall resiliency by limiting the impact of dual-link failures. By framing the problem as finding two disjoint shortest paths, coupled together with a resiliency component to the objective function, they establish a single formulation to produce optimal path design. The mathematical formulation was adapted to solve the problem. QAOA performance evaluation using uncorrelated and correlated link failure scenarios requires careful consideration of network topology Scientists are employing quantum approximate optimization algorithm (QAOA) executed over both quantum simulator and quantum hardware. QAOA was tested on a toy graph topology with 5 vertices and 7 edges and considering two limiting scenarios respectively representing independe
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quantum-computingQuantum Computing Speed-Up Achieved with New State Preparation Technique
Quantum state preparation represents a fundamental subroutine within numerous quantum algorithms, and minimising its circuit complexity remains a crucial endeavour. Giacomo Belli and Michele Amoretti, both from the Quantum Software Laboratory at the University of Parma, alongside Giacomo Belli, present a novel approach to optimise this process. Their research introduces a simplified algebraic decomposition that segregates the preparation of the real and complex components of a desired quantum state, demonstrably reducing circuit depth, total gate count, and complexity when ancillary qubits are employed. This improvement stems from utilising a single operator per uniformly controlled gate, contrasting with the three required by the original method, and establishes a significant advancement in the field by offering a more efficient pathway to prepare both dense and sparse quantum states, as validated through simulations using the PennyLane library. This work introduces a novel algorithm for quantum state preparation that demonstrably improves upon existing methods, specifically optimising the algorithm developed by Sun et al., which previously defined the asymptotically optimal bounds for this process. The breakthrough lies in a simplified algebraic decomposition, effectively separating the preparation of the real and complex components of the desired quantum state. This separation allows for the implementation of uniformly controlled gates using a single operator, a substantial reduction from the three operators required in the original decomposition. By leveraging the PennyLane library, the team implemented and rigorously tested this new algorithm in a simulated environment, assessing its performance across both dense and sparse quantum states, including those with physical relevance. The resulting circuits exhibit reduced circuit depth, a lower total gate count, and fewer controlled-NOT gates when utilising ancillary qubits. Performance comparisons against the wide
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