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Quantum Software Development: Qiskit, Cirq & Quantum Programming

Quantum programming news: Qiskit, Cirq, quantum SDKs, compilers. Quantum software stack & hybrid quantum-classical development.

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Quantum software development bridges abstract quantum algorithms with physical hardware execution, requiring specialized programming frameworks, compilers, and hybrid classical-quantum orchestration.

Major programming frameworks include Qiskit (IBM) with 500,000+ users including substantial Indian participation; Cirq (Google); and PennyLane (Xanadu) for differentiable quantum programming.

India's Quantum Software Development Landscape

India's software development capabilities feature prominently in NQM plans. Tata Consultancy Services (TCS) partners with IBM to develop cloud-based interfaces and quantum algorithms. The DRDO-TIFR-TCS collaboration developed the cloud interface for India's 6-qubit superconducting quantum processor.

The NQM Thematic Hub at IISc Bengaluru develops quantum software including compilers, control electronics, and algorithm libraries. The Centre for Development of Advanced Computing (C-DAC) integrates quantum computing with India's high-performance computing infrastructure.

Educational institutions including IISc Bengaluru, IIT Delhi, and IIT Bombay offer quantum computing courses and certifications. The IISc Centre for Continuing Education provides a Certificate Programme in Quantum Computing and Artificial Intelligence with hands-on training in Qiskit and PennyLane.

IBM Launches Directed Execution Model and Executor Primitive to Give Researchers Client-Side Control Over QEC Workflowsquantum-computing

IBM Launches Directed Execution Model and Executor Primitive to Give Researchers Client-Side Control Over QEC Workflows

IBM Quantum has introduced a new, low-level execution framework known as the directed execution model, powered by a new Executor primitive in qiskit-ibm-runtime v0.50.0. The framework transitions error mitigation logic—previously executed as a server-side black box—onto the client side, granting researchers explicit, composable control over circuit transformations, twirling, and error-correction pipelines on utility-scale hardware. The directed execution model is designed for algorithm developers, hardware characterization experts, and quantum error correction (QEC) researchers who require custom control over large families of circuit variants. Rather than manually generating thousands of randomized circuits on the client and transferring them across the network, directed execution allows developers to define execution blueprints locally using circuit annotations and template parameter tensors, which the Executor primitive processes in the near-time environment of the IBM Quantum Compute Service. [ Core Components of the Directed Execution Architecture ]Component / ToolSoftware Layer & PackageFunctional Role & Execution Pipeline• Boxes & Annotations• Qiskit SDK v2.0+ (BoxOp)• Groups circuit instructions into boxes and attaches directives (e.g., Twirl, InjectNoise, ChangeBasis) for the transpiler stack.• Samplomatic• samplomatic open-source library• Interprets annotated boxes to generate an inspectable template circuit and a structured randomization recipe (samplex).• Executor Primitive• qiskit-ibm-runtime backend engine• Low-level execution engine that consumes QuantumProgram objects and handles shot loops, tensor parameter sweeping, and broadcasting across 5,000+ circuit randomizations.• NoiseLearnerV3• qiskit-ibm-runtime helper program• Client-side noise characterization tool providing sparse Pauli-Lindblad and TREX measurement noise learning for unique circuit layers.• Qiskit Mitigation• qiskit-mitigation client package• Open-source library delivering

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IBM Expands Qiskit Beyond Python with Native C API Bindings for Fortran, C++, and Juliaquantum-computing

IBM Expands Qiskit Beyond Python with Native C API Bindings for Fortran, C++, and Julia

IBM Quantum has released details on the expansion of its open-source software stack, introducing native C API bindings (qiskit.h) that connect the core Rust data model of Qiskit v2.0 directly to the primary programming languages of high-performance computing (HPC): Fortran, C++, and Julia. While Python remains the standard entry point for quantum software development, classical supercomputing simulation codes in chemistry, physics, and material science rely heavily on compiled languages. By exposing a unified C interface to Qiskit’s Rust core, IBM enables researchers to construct, transpile, and execute quantum circuits natively within classical HPC application suites without calling an intermediate Python interpreter or incurring Global Interpreter Lock (GIL) performance overhead. [ Summary of Qiskit Native Language Bindings ]Language BindingIntegration MechanismHPC Workflows & Target Ecosystems• qiskit-fortran• Standard foreign-function interface (iso_c_binding)• Direct memory pointer passing for quantum chemistry codes (GAMESS, Quantum ESPRESSO, CP2K, VASP) and nuclear physics tools (BIGSTICK).• qiskit-cpp• Header-only library linked to Qiskit C shared library• High-performance physics simulation frameworks (LAMMPS, GROMACS) and exascale GPU acceleration layers (CUDA, Kokkos).• Qiskit.jl• Julia wrapper for Qiskit C API & QiskitIBMRuntime.jl• Interactive, dynamic scientific workflows integrating classical ODE solvers (DifferentialEquations.jl) and tensor network simulators (ITensors.jl). Because all three language bindings interface directly with the same underlying Qiskit shared library, they are fully interoperable. A quantum circuit constructed in a Fortran linear algebra routine can be passed directly to a C++ execution module or a Julia post-processing pipeline without data conversion. The native bindings support tightly coupled quantum-centric supercomputing (QCSC) workflows, such as Hamiltonian simulation, variational optimization, and dynamic time-

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Xanadu Cuts Quantum Read-Only Memory Costs by ~4x via Dense Encodingquantum-computing

Xanadu Cuts Quantum Read-Only Memory Costs by ~4x via Dense Encoding

Toffoli reduction relative to SelectSwap with D = 256 available dirty qubits. In a pair of research papers published on arXiv, Xanadu Lead Quantum Scientist Danial Motlagh and co-author Matthew Pocrnic have demonstrated an algorithmic technique that reduces the non-Clifford gate cost of Quantum Read-Only Memory (QROM) by nearly 4-fold compared to long-standing industry benchmarks. QROM is the foundational subroutine used to load classical data (like molecular Hamiltonians or financial matrices) into fault-tolerant quantum algorithms. Because table lookups account for most of the Toffoli gate overhead in practical applications, reducing QROM costs directly shrinks hardware runtimes and qubit requirements. Xanadu achieved this ~4-fold reduction using two core innovations: 1. Sequential Bit Packets and SelectCopy (May 2026): By replacing controlled swaps with copies and overlapping consecutive data passes, the leading Toffoli gate cost was cut in half, matching clean-qubit performance while using borrowed “dirty” workspace qubits (arXiv:2605.20334). 2. Dense Encoding in Z and X Bases (October 2026): The new construction temporarily writes two classical bits onto a single dirty qubit simultaneously using both its Z and X Pauli bases, doubling the data loaded per pass (arXiv:2610.02321). For standard 32-bit data entries (b = 32), combining dense encoding with sequential bit packets delivers a 3.9-fold Toffoli gate reduction over traditional SelectSwap architectures, representing a 75% savings in table-loading overhead. [ Key QROM Parameters & Metrics ]ParameterNameDefinition & Operational Meaning• N• Table Size / Entries• The total number of classical data entries to load into the quantum computer.• b• Bitstring Length• The width of each classical data entry in bits (e.g., b = 32 bits per number).• λ• Block Size• The number of table entries loaded simultaneously during one pass.• Dirty Qubits• Borrowed Workspace Qubits• Ancillary qubits borrowed in unknown states

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Guest Post: Quantum Computing’s Transistor Momentquantum-computing

Guest Post: Quantum Computing’s Transistor Moment

A new paper demonstrating Grover’s search on a room-temperature diamond processor may echo the transistor’s takeover from vacuum tubes – and raises questions about how much vacuum-dependent quantum hardware survives the same transition. Guest Post By Prof. Dr. Marius Grundmann, CEO, SAXON Q Every disruptive technology has a quiet laboratory result that outsiders miss and […]

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Quantum X Labs Seeks Patent for Structured-Beam Atom Cooling Systemquantum-computing

Quantum X Labs Seeks Patent for Structured-Beam Atom Cooling System

Insider Brief PRESS RELEASE — Quantum X Labs Inc. (NASDAQ: QXL) (“Quantum X Labs” or the “Company”) today announced that its subsidiary, Quantum X Labs Ltd., a developer of technologies across quantum computing, quantum sensing, quantum software and quantum security, has filed a patent application with the United States Patent and Trademark Office (USPTO) titled […]

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Quantum algorithms for general nonlinear dynamics based on the Carleman embeddingquantum-computing

Quantum algorithms for general nonlinear dynamics based on the Carleman embedding

AbstractImportant nonlinear dynamics, such as those found in plasma and fluid systems, are typically hard to simulate on classical computers. Thus, if fault-tolerant quantum computers could efficiently solve such nonlinear problems, it would be a transformative change for many industries. In a recent breakthrough [Liu et al., PNAS 2021], the first efficient quantum algorithm for solving nonlinear differential equations was constructed, based on a single condition $R \lt 1$, where $R$ characterizes the ratio of nonlinearity to dissipation. This result, however, is limited to the class of purely dissipative systems with negative log-norm, which excludes application to many important problems. In this work, we correct technical issues with this and other prior analysis, and substantially extend the scope of nonlinear dynamical systems that can be efficiently simulated on a quantum computer in a number of ways. Firstly, we extend the existing results from purely dissipative systems to a much broader class of stable systems, and show that every quadratic Lyapunov function for the linearized system corresponds to an independent $R$-number criterion for the convergence of the Carleman scheme. Secondly, we extend our stable system results to physically relevant settings where conserved polynomial quantities exist. Finally, we provide extensive results for the class of non-resonant systems. With this, we are able to show that efficient quantum algorithms exist for a much wider class of nonlinear systems than previously known, and prove the BQP-completeness of nonlinear oscillator problems of exponential size. In our analysis, we also obtain several results related to the Poincaré-Dulac theorem and diagonalization of the Carleman matrix, which could be of independent interest.Featured image: Schematic illustration of the Carleman embedding: nonlinear dynamics are represented by a hierarchy of polynomial observables whose evolution is linear in the lifted space. We establish r

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Enhanced measurements on quantum computers via the simultaneous probing of non-commuting Pauli operatorsquantum-computing

Enhanced measurements on quantum computers via the simultaneous probing of non-commuting Pauli operators

AbstractMeasuring the state of quantum computers is a highly non-trivial task, with implications for virtually all quantum algorithms. A promising avenue is multi-copy schemes, where identical copies of a quantum state are measured jointly so that all Pauli operators within the considered observable can be simultaneously assessed. Here, we present a first implementation of such a two-copy scheme in a measurement protocol. Based on Bayesian statistics, it accurately estimates not only the average of the desired observable but also the error en route. This enables an adaptive shot-allocation algorithm that preferentially samples the most uncertain Pauli terms. In regimes with many non-commuting Pauli operators, this “double'' scheme can outperform the state-of-the-art measurement protocol in minimizing total shots for a given precision. We also numerically confirm the finding in previous theoretical works that the two-copy scheme incurs an overhead due to the square-root relationship between the variance of measured quantities and the number of measurement shots.Featured image: Scheme of our algorithm for the toy example $\hat{O}=\hat{IX} + \hat{XI} + \hat{XX} + \hat{YY} + \hat{ZZ}$ (weights equal one for all $i=1,\dots,5$). These Pauli strings are depicted as vertices of a graph, connected when they commute. All-to-all connected groups (pink, green, and blue) can be simultaneously measured. Alternatively, one can employ the more expensive double scheme to assess the magnitude of all $\hat{P}_{i}$. To choose which group to probe, we assign a virtual measurement that predicts the group that minimizes the estimation variance $(\Delta \widetilde{O})^2$ (based on previous measurements, green in the figure as an example). After the real measurement, $\widetilde{O}$ and $(\Delta \widetilde{O})^2$ are updated and are either outputted by the algorithm (if the budget $M$ is depleted) or employed to assign the next measurement.Popular summaryThe simulation of physical systems i

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Festival season finds Quantum Software Lab in high spiritsquantum-computing

Festival season finds Quantum Software Lab in high spirits

The Quantum Fringe filled the summer with inspiring events for students, scientists and the general public, and also celebrated the third anniversary of Edinburgh’s Quantum Software Lab Towards quantum advantage Elham Kashefi, director of the QSL and chief scientist of theNQCC, speaks at the third annual showcase of the lab. (Courtesy: QSL) The Quantum Software Lab (QSL) at the University of Edinburgh is going from strength to strength. Buoyed by its strategic partnership with the UK’s National Quantum Computing Centre (NQCC), in just three years the lab has become a globally recognized hub for creating software solutions that generate practical value from emerging quantum computers. With more than 70 researchers working across applications, algorithms and the underpinning theory and software, the QSL is also poised to lead a £20m project that will drive the delivery of tools, methods and systems for useful quantum computing. The growing momentum at the QSL has also provided the impetus for the Quantum Fringe, a festival of events that debuted in 2025 to mark UNESCO’s International Year of Quantum Science and Technology. Modelled on Edinburgh’s world-famous Fringe festival, the programme provides a showcase for quantum-inspired events that have been convened by different organizations for a diverse range of audiences. “The Quantum Fringe belongs to everyone who takes part, whether they are researchers, scientists in government and industry, or students at university or school,” says Elham Kashefi, director of the QSL and chief scientist of the NQCC. “That’s the whole point: no-one can do this alone, and each of the 16 events in this year’s festival provided an opportunity for the community to come together.” That cross-cutting ethos was evident in several collaborative workshops. As an example, a two-day event organized by the research hub for Integrated Quantum Networks (IQN) enabled scientists from different sectors and backgrounds to share expertise on the authen

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GPU-Accelerated Quantum Simulation of Stabilizer Circuitsquantum-computing

GPU-Accelerated Quantum Simulation of Stabilizer Circuits

AbstractWe introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs, with a focus on data-parallel tableau evolution and scalable handling of projective measurements. Our approach reformulates key bottlenecks in stabilizer simulation – such as Gaussian elimination and measurement updates – into GPU-tailored primitives that eliminate sequential dependencies and maximize memory coalescing. We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator designed for large qubit counts and many-shot sampling. Across a broad benchmark suite reaching 180,000 qubits and depth 1,000 (roughly 130M gates), QuaSARQ shows substantial runtime improvements, with up to 105$\times$ speedup, and over 80% energy reduction on demanding instances. Moreover, QuaSARQ consistently outperforms Stim, a state-of-the-art CPU-optimized stabilizer simulator, as well as Qiskit-Aer (CPU/GPU), Qibo, Cirq, and PennyLane. Finally, QuaSARQ exhibits a significant advantage in many-shot sampling on large workloads. These results demonstrate that our parallel algorithms can significantly advance the scalability of stabilizer-circuit simulation, particularly for workloads involving extensive measurements and sampling.The open-source implementation of QuaSARQ is available at GitHub.   Popular summaryQuantum computers need error correction before they can run useful algorithms reliably. Designing and operating error correction protocols requires fast classical methods for simulating large circuits consisting of so-called stabilizer or Clifford gates. Current simulation methods only reach tens of thousands of qubits, which is insufficient for modern error correction protocols. We studied whether GPUs, which execute thousands of threads at once, could do better. The obstacle is that the standard measurement algorithm is inherently sequential: each update depends on the result of the previous one, which is precisely the pattern a GPU cannot spee

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Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph propertiesquantum-computing

Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties

--> Quantum Physics arXiv:2609.30399 (quant-ph) [Submitted on 24 Sep 2026] Title:Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties Authors:N. A. Susulovska, Kh. P. Gnatenko View a PDF of the paper titled Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties, by N. A. Susulovska and 1 other authors View PDF HTML (experimental) Abstract:The encoding of 3-uniform edge-ordered weighted hypergraphs into multiqubit quantum states is proposed. We derive an analytical expression for the geometric measure of entanglement of hypergraph quantum states and establish its relation to hypergraph properties. The results establish a direct connection between the local structure of hypergraphs, hyperedge weights, and the entanglement of the corresponding quantum states. For equally weighted hypergraphs, it is shown that the entanglement depends explicitly on the vertex degree. The dependence of entanglement on hypergraph parameters is studied analytically and using quantum computing. Hypergraph states associated with chain, star, regular lattice, and binary-tree hypergraphs are examined, and their entanglement is quantified using quantum computing. These results open up the possibility of studying hypergraph properties with quantum programming. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30399 [quant-ph]   (or arXiv:2609.30399v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.30399 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nataliia Susulovska [view email] [v1] Thu, 24 Sep 2026 18:04:35 UTC (7,410 KB) Full-text links: Access Paper: View a PDF of the paper titled Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties, by N. A. Susulovska and 1 other authorsView PDFHTML (experimental)TeX Source view license Current

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Generation of Photonic Graph States with minimal number of quantum emittersquantum-computing

Generation of Photonic Graph States with minimal number of quantum emitters

--> Quantum Physics arXiv:2609.30400 (quant-ph) [Submitted on 24 Sep 2026] Title:Generation of Photonic Graph States with minimal number of quantum emitters Authors:Konstantinos-Rafail Revis, Nils Tomke Ottink, Pierre-Emmanuel Emeriau, Paul Hilaire View a PDF of the paper titled Generation of Photonic Graph States with minimal number of quantum emitters, by Konstantinos-Rafail Revis and 3 other authors View PDF HTML (experimental) Abstract:Graph states are a fundamental resource for measurement and fusion-based quantum computing, quantum networks, and sensing. Preparing them in a photonic system deterministically is, in principle, possible, but finding efficient schemes to prepare them was a long-standing problem addressed recently. Additionally, heuristic optimization schemes for reducing the required number of two-qubit gates were developed. However, the problem of reducing the number of emitters by optimizing the emission ordering was not addressed, due to its computational complexity, as it is connected to a well-known NP-hard problem from graph theory, the linear rank width computation. In this work, we focus on developing heuristic polynomial algorithms to reduce the number of emitters required. In total, we propose four distinct algorithms, which demonstrate up to $30\%$ emitter reduction on random graphs. Furthermore, we provide numerical and statistical evidence that the combination of our optimization schemes with the preexisting algorithms for optimizing the two-qubit gates of the preparation protocol can further reduce them by around $20\%$. Finally, we examine the developed algorithms for various useful graph state families, such as graphs useful for measurement-based quantum algorithms, and cluster states and graph codes used for quantum error correction, to determine the performance of each algorithm. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30400 [quant-ph]   (or arXiv:2609.30400v1 [quant-ph] for this version)   ht

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Quantum-safe algorithms may fail faster with powerful AI tools From SIKEquantum-computing

Quantum-safe algorithms may fail faster with powerful AI tools From SIKE

A cryptographic algorithm once considered a leading candidate for quantum-resistant encryption fell in just one hour on a standard laptop, not to a quantum computer, but to a mathematical insight from 1997. The Supersonic Multivariates Isogeny Key Encapsulation (SIKE) protocol advanced to the fourth round of evaluation by the National Institute of Standards and Technology before mathematicians Wouter Castryck and Thomas Decru connected its structure to Ernst Kani’s decades-old theorem. This collapse highlights a growing vulnerability, as frontier AI systems now possess the capacity to rapidly explore obscure mathematical connections, potentially shortening the window between overlooked weakness and successful attack, according to security leaders. “Years without a successful attack provide evidence, but they cannot establish that every useful mathematical connection has been explored,” the researchers noted. In May 2026, OpenAI reported that an internal model disproved a longstanding conjecture associated with Erdős’s planar unit-distance problem, first posed in 1946. The model applied sophisticated algebraic number theory to a seemingly elementary geometry question, a transfer of ideas between fields that produced a major result. This month, OpenAI announced an AI-generated solution to the Navier-Stokes existence and smoothness problem, unresolved for roughly 90 years, and released both a written proof and a formalization for computer verification. The announcement remains subject to mathematical scrutiny, but the progression is stunning. Frontier systems are now producing research claims against problems that have occupied generations of the best mathematicians. It is certain these capabilities to accelerate the search for overlooked cryptographic weaknesses are being used on a vast scale, especially by intel agencies with enormous grid-straining compute, long before AI made it cool. Would models have independently broken SIKE in hours?

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Quantum chip predicts time series with a feedback loopquantum-computing

Quantum chip predicts time series with a feedback loop

Nearly four decades passed between the 1971 postulation of the memristor and its eventual demonstration in 2008. The memristor functions as the primary example of neuromorphic components due to its ability to retain memory through hysteresis, mirroring how synapses function in the human brain. This work explores integrating the memristor with quantum computing to create more efficient machine learning platforms. Photonic Quantum Memristor Enables Neuromorphic Computing The implementation of a quantum reservoir computing system using single photon states marks a first for the field, according to work detailed in a recent publication. Researchers designed the device to update its internal phase via feedback based on measurements at one output mode. The reservoir’s output then undergoes processing by a linear regression model, calculating a weighted sum to produce a final result. To test the system’s efficacy, the team addressed four distinct tasks: predicting a smooth nonlinear function and forecasting three random time series, NARMA, Mackey-Glass and Santa Fe, with vowel recognition also numerically simulated. These benchmarks allowed for a direct comparison of performance with and without the quantum memristor’s dynamic enhancements. The photonic quantum memristor itself is modeled as a tunable Mach-Zehnder interferometer, with its internal phase updated by a feedback rule dependent on measurement outcomes. The researchers employed a unitary representation of the memristor action, adaptively updated based on previous outcomes, with coefficients serving as hyperparameters within the model. For time-series prediction, they fixed certain parameters, focusing on the memory decay rate as an adjustable hyperparameter. This design allows the system to exploit the feedback mechanism to implement nonlinear operations on input states and use short-term memory, effectively demonstrating a proof-of-principle quantum reservoir computing system. The team encoded classical data us

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Quantum algorithms gain from filtered-state preparationquantum-computing

Quantum algorithms gain from filtered-state preparation

Researchers from Sungkyunkwan University in South Korea and Xanadu in Canada detailed a new method for improving quantum algorithms in a paper published September 25, 2026, in Quantum Science and Technology. The team developed a technique designed to address a critical limitation in many quantum algorithms: accurate state preparation. This work demonstrates a framework for enhancing the overlap of input states through spectral filtering, potentially reducing runtime by more than two orders of magnitude for certain calculations, with overlap amplification exceeding a factor of one hundred. Filtered Quantum Phase Estimation for Eigenvalue Problems The success probability of quantum phase estimation hinges on initial state overlap; a new framework detailed in Quantum Science and Technology on September 25, 2026, directly addresses this limitation through filtered-state preparation. Researchers from Sungkyunkwan University and Xanadu developed a method to amplify this overlap, potentially reducing the computational cost of determining essential properties of many-body Hamiltonians, such as ground-state energy and excited spectra. The work introduces a unified framework for quantum algorithms centered on enhancing the initial connection with target eigenstates. This framework explicitly defines the trade-off between overlap amplification, the probability of successfully preparing a state, and the resources required to implement the filter itself. Analysis of Gaussian filters and a modified Krylov-subspace-based filter revealed improvements in the success-probability/overlap balance important for preparing states. The team’s approach tackles a central challenge in quantum computing: accurately estimating eigenvalues, a task where standard quantum phase estimation requires a significant initial overlap between the prepared input state and the target eigenstate. Specifically, the success probability of QPE scales with the squared overlap, meaning even small improvements in

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Zapata Quantum’s Sumit Kapur joins Boston’s Power 50 leadersquantum-computing

Zapata Quantum’s Sumit Kapur joins Boston’s Power 50 leaders

Sumit Kapur, chief executive of Boston-based quantum software company Zapata Quantum, has been named to the Boston Business Journal’s 2026 Power 50: Movement Makers list. The recognition follows a two-year period where Kapur led a restructuring focused on developing applications to run on rapidly advancing quantum hardware. “I am honored to be recognized alongside so many outstanding leaders in Boston,” said Kapur, adding that the achievement reflects Zapata Quantum’s origins in “the long arc of innovation that began in Harvard’s quantum computing lab and The Engine built by MIT.” Kapur’s Restructuring Positions Zapata for Quantum Application Leadership The company, which regained SEC reporting status and now trades on the OTCQB Venture Market under the ticker ZPTA, completed a $15 million financing round in April 2026 as the final step in this strategic shift, bringing total funding to $93 million. This capital infusion followed earlier Series An and B rounds in 2021 and 2023, and a bridge financing in 2025, enabling a rebuilding of the team and deepened industry partnerships, Zapata Quantum says. Kapur’s leadership prioritized re-integrating founding expertise from Harvard’s quantum computing lab, alongside new hires from established technology firms like Palantir, Oracle and Northrop Grumman. This blend of academic rigor and commercial experience was designed to accelerate the development of quantum software applications, addressing a critical bottleneck as quantum hardware rapidly advances. The company’s collaborative work with NVIDIA, using agentic AI to expedite quantum algorithm discovery, exemplifies this application-focused approach. Zapata’s Quantum Pilot platform, launched in early access on September 22, further demonstrates this commitment by providing enterprises with tools to evaluate the potential of quantum solutions. The impact of this restructuring extends beyond internal development, as evidenced by recognition of Zapata’s research with the Dana-

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Quantum computer simulates matter “popping into existence” - ScienceDailyquantum-computing

Quantum computer simulates matter “popping into existence” - ScienceDaily

Science News from research organizations Quantum computer simulates matter “popping into existence” Scientists used a quantum computer to simulate particles seemingly “popping into existence,” opening a new window into the physics of the early universe. Date: September 26, 2026 Source: Duke University Summary: Scientists recreated a particle-forming process linked to the extreme physics of the early universe using a 13-ion quantum simulator. The breakthrough suggests quantum computers could eventually help researchers investigate how matter formed and evolved after the Big Bang. Share: Facebook Twitter Pinterest LinkedIN Email FULL STORY Researchers have observed string-breaking dynamics on a quantum simulator that could help probe questions related to the Big Bang. This is an artistic rendering of string-breaking. Credit: Emily Edwards, Duke University Researchers led by the Duke Quantum Center (DQC) have used a quantum simulator to observe string breaking dynamics connected to particle antiparticle formation, marking one of the earliest demonstrations of its kind in quantum physics. The work, published September 23 in Nature Physics, shows how trapped ion quantum computers could become powerful tools for exploring some of the deepest questions in fundamental physics. The experiment simulated a process known as string breaking, in which two connected building blocks of matter are pulled apart until so much energy accumulates that new particles can effectively "pop into existence" when the connection breaks. "Quantum computer simulations provide the best platform to investigate complex questions like matter formation, short of having witnessed the Big Bang itself," said Christopher Monroe, the Gilhuly Family Presidential Distinguished Professor of Electrical and Computer Engineering and Physics at Duke, who led this research. "These findings signal a marked development in the quantum science field and open new avenues for us to understand string-breaking dynamics."

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CGI Federal to research AI and quantum for Defense Logisticsquantum-computing

CGI Federal to research AI and quantum for Defense Logistics

CGI Federal has entered into an applied research agreement with the U.S, the company says. Defense Logistics Agency and the University of Tennessee, Knoxville to explore agentic AI and quantum computing for logistics resilience. The initiative, called Quantum Pathfinder, aims to move these technologies “from promise to practice” within the Defense Logistics Agency’s systems, uniting government needs with academic research and commercial capability. “Quantum computing will not arrive as a single product launch; it will arrive as a series of hard research problems solved against real mission constraints,” said Victor Foulk, Vice-President of Emerging Technologies at CGI Federal. Initial exploration will focus on warehouse operations, reverse logistics, and inventory positioning for contested environments. DLA, CGI Federal, and UT Knoxville Launch Quantum Pathfinder Research CGI Federal will host the Quantum Pathfinder initiative at its onshore delivery center in Knoxville, Tennessee, directly linking research efforts to a specific geographic hub for quantum innovation. This strategic location reflects a collaborative aim to bolster East Tennessee’s growing reputation as a destination for quantum computing by using existing university and national laboratory resources. The initiative’s initial focus will be on dynamic smart-warehouse orchestration, reverse logistics, and resilient inventory positioning in challenging operational environments, with specific use cases to be determined jointly in the coming months. These areas represent critical logistical challenges for the Defense Logistics Agency, offering a practical testing ground for emerging technologies. CGI Federal intends to build a bridge between current systems and the potential of quantum technology, emphasizing the importance of collaboration with partners who understand both the mission requirements and the underlying research. “We are investing to do that work now, alongside a federal partner that understa

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Quantum attack protection built into CaveroCore for defencequantum-computing

Quantum attack protection built into CaveroCore for defence

Cavero Secure is demonstrating a new approach to hardware security intended to proactively defend against supply chain attacks and emerging quantum threats, the company says. CaveroCore secures completed devices even before shipment, allowing for secure activation in the field using a network of trusted devices; the system identifies cloned, counterfeited, or compromised components. This capability extends quantum attack protection to even the most constrained devices, replacing vulnerable static keys. The company states it is seeking partners and investors to help develop trust protocols for defence technology as it participates in Tech Tour Quantum and Defence in Berlin. CaveroCore Secures Defence Hardware Throughout Supply Chains This proactive approach contrasts with conventional methods focused on threat detection after deployment, offering an advantage in securing sensitive technologies. The system flags cloned or counterfeited devices and components, addressing a vulnerability within complex supply chains. This capability extends quantum-safe security to environments previously considered impractical for such protection. Cavero Secure intends to foster collaboration at the Tech Tour Quantum and Defence event in Berlin, seeking investment to accelerate development of these trust protocols. The company’s factory-to-field solution aims to establish a secure chain of custody for mission-critical technology, protecting it from compromise throughout its lifecycle. Source: https://caverosecure.com/insights/tech-tour-quantum-and-defence-berlin More like thisQuantum Computing Business NewsSplendor Labs launches blockchain built to withstand quantum attacksQuantum AlgorithmsDecaQ achieves 2.045-second median for complex quantum workloadQuantum HardwareInfleqtion Entangles 30 Logical Qubits on Sqale ComputerQuantum Computing Business NewsQTREX Quantum’s AME segment drives $1.55M in first halfStay currentSee today’s quantum computing news on Quantum Zeitgeist for the lat

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NIST Standards Drive Demand for 11 Quantum Encryption Approachesquantum-computing

NIST Standards Drive Demand for 11 Quantum Encryption Approaches

Organizations are now prioritizing evaluation of quantum-resistant encryption solutions as finalized government standards and emerging data interception risks demand immediate action in 2026. The core math underlying today’s standard security frameworks, relied upon for web traffic, cloud workloads, and digital identities, will not withstand the processing capabilities of quantum hardware running Shor’s algorithm. Instead of prime factorization, these defenses utilize lattice-based mathematics, hash structures, or physical laws to secure data, with algorithms like Module Learning With Errors (M-LWE) creating complex equations that resist both supercomputers and quantum systems. Transitioning to these methods, and implementing ML-KEM encryption under NIST post-quantum standards, is essential for enterprise security teams. What is the best quantum-resistant encryption solution for enterprises? To achieve robust, future-proof security, enterprises should prioritize crypto-agility platforms, systems designed to seamlessly integrate and update cryptographic algorithms. These platforms combine automated discovery of vulnerable systems, support for emerging standards, and native hybrid cryptography, allowing organizations to adapt quickly to evolving threats. A solution like enQase enables centralized management of quantum security policies and algorithm updates without disrupting existing software workflows, a critical feature for maintaining operational continuity. NIST subsequently selected HQC as a backup key encapsulation mechanism in March 2025, further solidifying the selection of approved algorithms. Western Digital’s Ultrastar HDDs now incorporate hardware-level defense using post-quantum cryptography and NIST-approved algorithms, indicating a trend toward embedding quantum-resistant cryptography directly into data storage solutions. This proactive approach minimizes the risk of data interception, even if encryption is compromised in transit. The agency’s partners

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Qot Labs Restores VQE Convergence with Error Mitigationquantum-computing

Qot Labs Restores VQE Convergence with Error Mitigation

Combining dynamical decoupling, zero noise extrapolation, and Pauli twirling restores convergence within the ADAPT-VQE algorithm under noisy conditions; this enables more reliable preparation of molecular ground states using quantum computation. Both coherent and incoherent forms of hardware noise impede operator selection, a key step in building the computational circuit, preventing accurate results without mitigation strategies. A vulnerability has been identified within ADAPT-VQE, a quantum computing technique used for finding molecular ground states, relating specifically to how accurately the algorithm chooses computational steps. Predictable and random hardware errors disrupt this key step, hindering its ability to build effective circuits without employing error correction methods. Combining dynamical decoupling, zero noise extrapolation, and Pauli twirling overcomes these issues by restoring reliable operation despite existing imperfections in current technology. Researchers from Virginia Tech and Lake Zurich High School have identified a key weakness within ADAPT-VQE, a quantum computing technique used as a recipe for finding the lowest energy state of a molecule. Inaccuracies during operator selection, akin to choosing specific tools from a set of tools based on what needs fixing, can prevent the algorithm from building effective circuits when faced with both predictable and random hardware errors. These errors distort key calculations needed to guide circuit construction, hindering accurate results without mitigation strategies. Fortunately, combining techniques such as dynamical decoupling, zero noise extrapolation, and Pauli twirling, methods similar to filtering static out of a radio signal or averaging multiple measurements, successfully restores reliable operation despite these imperfections. Mitigation of qubit decoherence enables resilient variational quantum eigensolver calculations Orders-of-magnitude improvements in ADAPT-VQE efficiency were ach

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