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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.

Quandela and NVIDIA link quantum processors to AI with NVQLinkquantum-computing

Quandela and NVIDIA link quantum processors to AI with NVQLink

At IEEE Quantum Week 2026 in Toronto, Quandela is demonstrating a pathway to integrate quantum processors into existing artificial intelligence and high-performance computing infrastructure using NVIDIA’s NVQLink, the company says. The collaboration focuses on a three-step approach, access, integration and discovery, and scale, allowing organizations to explore quantum applications without replacing current systems. Algorithms can be developed and simulated on NVIDIA GPUs before deployment on Quandela’s photonic quantum processing unit, positioning the QPU as a specialized accelerator. “The challenge is no longer just about accessing a QPU, but about integrating it directly into AI workflows,” says Jean Senellart, Chief Technology and Product Officer at Quandela, as the companies are publishing a technical white paper outlining this hybrid architecture. Photonic QPU Integration with NVIDIA NVQLink for AI Workflows NVIDIA’s NVQLink enables a low-latency connection under 4 microseconds between the NVIDIA GPU environment and the Quantum System Controller, a critical advancement detailed in a new technical white paper published by Quandela and NVIDIA. This low-latency link facilitates direct communication, enabling the GPU to orchestrate quantum workloads executed on the photonic QPU, rather than relying on slower conventional interfaces. This phased integration strategy allows developers to use existing GPU infrastructure and software ecosystems, such as the NVIDIA CUDA-Q platform and cuQuantum SDK, for algorithm development and simulation before deployment on actual quantum hardware. Algorithms are first refined on GPUs, mitigating the challenges of early-stage quantum processor limitations and accelerating the development cycle, according to the company. “Quantum computing becomes more useful when it moves beyond standalone access and becomes part of the accelerated computing researchers already use,” said Sam Stanwyck, Director of Quantum Product at NVIDIA. “Our wor

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Zapata Quantum’s CEO joins Boston’s ‘Power 50quantum-computing

Zapata Quantum’s CEO joins Boston’s ‘Power 50

Sumit Kapur, chief executive of Zapata Quantum, has been named to the Boston Business Journal’s 2026 Power 50: Movement Makers list, recognizing his impact on Boston’s business and technology sectors. The selection acknowledges Kapur’s leadership during a two-year restructuring that refocused the company on developing quantum software applications, Zapata Quantum says. “I am honored to be recognized alongside so many outstanding leaders in Boston,” Kapur stated. Zapata Quantum, founded in 2017, develops its Orquestra workflow platform and recently uplisted to the OTCQB Venture Market after regaining SEC reporting status. Sumit Kapur Recognized as Boston Business Journal’s 2026 “Movement Maker” Sumit Kapur’s selection as a 2026 by the Boston Business Journal acknowledges a period of focused rebuilding within Zapata Quantum, culminating in deepened industry partnerships and a return to core strengths. The award recognizes individuals driving impactful change in the Boston area’s business and technology sectors, and Kapur’s recognition follows a two-year restructuring effort at the quantum software company. This repositioning prioritized application development, a strategic shift reflecting the accelerating pace of quantum hardware advancement and the growing need for software to harness its potential, according to Zapata Quantum. The company’s renewed focus involved rebuilding its team with both original researchers from Harvard’s quantum computing lab and experienced talent from established enterprises like Palantir, Oracle, and Northrop Grumman. This combination of deep quantum expertise with commercialization experience was deliberate, designed to bridge the gap between theoretical advances and practical applications. Zapata’s collaborative work with NVIDIA, using agentic AI to accelerate quantum algorithm discovery, exemplifies this approach. The company’s research, recognized by Nature Biotechnology as one of the top ten papers of 2025, further highlights its com

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Atlas Technologies helps Oxford physicists track entanglement in LHC’s Z bosonsquantum-computing

Atlas Technologies helps Oxford physicists track entanglement in LHC’s Z bosons

Physicists at the University of Oxford have confirmed quantum entanglement extends to some of the heaviest and most fleeting particles created at CERN’s Large Hadron Collider. The team tracked entanglement in pairs of Z bosons, particles existing for only a fraction of a second before decaying, publishing their findings in Physical Review Letters. “Quantum mechanics underpins computing and security,” explains study co-author Professor Alan Barr, Department of Physics. This demonstration bolsters the foundations of quantum physics and informs the development of technologies like quantum computing and secure communication. Z Boson Entanglement Confirmed in LHC’s High-Energy Collisions Tracking the decay of Z bosons produced in high-energy collisions at the Large Hadron Collider has provided direct evidence of quantum entanglement extending to comparatively massive particles. Researchers reconstructed the spin states of these fleeting bosons by meticulously analyzing the angles at which these particles were emitted, electrons and muons, detected by the ATLAS experiment. This precise reconstruction confirmed a quantum link between Z boson pairs, demonstrating entanglement isn’t limited to lighter particles like photons traditionally used in quantum experiments. The findings, published in Physical Review Letters, expand the known boundaries of this fundamental quantum phenomenon. The ability to observe entanglement in short-lived particles challenges previous assumptions about its fragility and robustness. This has implications for quantum technologies, where maintaining entanglement is important for operations like quantum computation; the more robust entanglement proves to be, the wider the range of potential applications.

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ParityQC Launches Parity Twine Optimizer for Quantum Optimizationquantum-computing

ParityQC Launches Parity Twine Optimizer for Quantum Optimization

Insider Brief PRESS RELEASE — ParityQC, the Quantum Architecture Company, today launched the Parity Twine Optimizer, making its compiler technology, which led to record-setting performance in executing the Quantum Fourier Transform, available as a product. Built on the ParityQC Architecture, it delivers uniquely efficient compilation and is now available via the IBM Qiskit Function, including […]

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Aqarios boosts IBM Qiskit library with quantum constraint toolquantum-computing

Aqarios boosts IBM Qiskit library with quantum constraint tool

Aqarios GmbH launched the Constrained Quantum Optimizer for IBM Qiskit, offering a new approach to solving complex planning and scheduling problems, the company says. Built on the company’s FlexQAOA algorithm, the function integrates real-world constraints directly into quantum computations, a departure from penalty-based methods that cannot guarantee valid solutions. “Optimization is one of the most compelling categories of problems for the development of quantum advantage-capable applications,” said Scott Crowder, Vice President, IBM Quantum Adoption and Business Development. Aqarios validated the function on IBM Quantum Heron hardware, achieving optimal or near-optimal solutions on a 144-bit problem with 48 constraints. Aqarios Integrates FlexQAOA into IBM Qiskit Functions Catalog Aqarios has extended the capabilities of IBM’s Qiskit platform with the launch of the Constrained Quantum Optimizer function, immediately available to users through the Qiskit Functions Catalog as of September 15, 2026. Michael Lachner, CEO of Aqarios, explained, “Most quantum optimization tools still require users to work around their constraints. We build them in natively, and that’s what turns quantum computing into real, usable results for constrained problems.” The function is built upon Aqarios’ existing FlexQAOA algorithm, already deployed for customers utilizing the company’s Luna platform. Aqarios’ integration with Qiskit extends beyond simply offering a new algorithm; it represents a broadening of access to the company’s optimization capabilities. “We’re glad to bring what already runs on our Luna platform directly to IBM Quantum users, via the Qiskit Functions Catalog,” Lachner stated. Luna is a platform designed to route problems to the most suitable solver, classical, AI-based, or quantum, without hardware lock-in, reflecting Aqarios’ commitment to a hybrid approach. Founded in 2021 and now publicly listed on Boerse Duesseldorf following a $140 million SPAC transaction with

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Quantum Dice and the probabilistic bitquantum-computing

Quantum Dice and the probabilistic bit

Quantum Computing is part of Stack Overflow’s open communities: specialist spaces where curiosity is welcome, knowledge is shared freely, and the best answers rise to the top. Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work. Learn more Bring the best of human thought and AI automation together at your work. I work on most quantum computing use cases (optimization , partial differential equations, chemistry, ...). I focus the algorithm development mainly on Fault Tolerant (an Universal) Quantum Computers. Someone recently highlighted the company Quantum Dice which has operational computers relying on what is called probabilistic bit (pbit). Does this probabilistic hardware relies on quantum principles ? can pbits interfere with each other as qubits can ? can a pbit be in a state superposition ? If not, what advantage this probabilistic hardware may bring compared to quantum hardware ? Given that the advantage of quantum computing comes from (to be short) the use of superposition and interference. This question may depend on the use case of course. Thanks for contributing an answer to Quantum Computing Stack Exchange! Use MathJax to format equations. MathJax reference. To learn more, see our tips on writing great answers. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. This comment attacks a person or group. Learn more in our Abusive behavior policy. This comment is rude or condescending. Learn more in our Code of Conduct. A problem not listed above. Try to be as specific as possible. You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful.

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PsiQuantum team shares R&D at IEEE Quantum Week in Torontoquantum-computing

PsiQuantum team shares R&D at IEEE Quantum Week in Toronto

At IEEE Quantum Week in Toronto, PsiQuantum is showcasing new tools designed to accelerate development of fault-tolerant quantum algorithms. Mariia Mykhailova, Michał Stęchły, and Sean Greenaway will present a tutorial Sunday demonstrating the PsiQuantum Development Kit, an open-access toolkit supporting algorithm development, validation, and analysis, the company says. Attendees, and those following along remotely, can access tutorial materials through the GitHub repository, allowing researchers to immediately utilize and build upon PsiQuantum’s resources. The company is also presenting research detailing quantum resource estimation for simulating complex physical models, like the Sachdev-Ye-Kitaev model, using its Construct platform. PsiQuantum Development Kit Enables Fault-Tolerant Algorithm Design PsiQuantum’s Development Kit tutorial, scheduled for Sunday, September 13th from 1 pm to 4:30 pm in room 605, will center on the practical application of fault-tolerant quantum algorithms, a critical area for advancing quantum computing beyond its current limitations. The kit supports development, validation, and analysis of these algorithms, offering researchers a means to move beyond theoretical models and address the challenges of real-world quantum hardware. Attendees will gain hands-on experience implementing algorithms and validating them using the PsiQDK tools, focusing on the SYK model and resource analysis techniques. The workshop extends beyond simple implementation, introducing concepts essential for quantum resource estimation and demonstrating tools for numeric and symbolic analysis of program requirements. PsiQuantum intends these implementations and estimates to be valuable for continued research into the SYK model, allowing researchers to study how techniques and resource needs vary with different approaches. A preprint detailing the underlying research is available on arXiv, and the associated code, including the PsiQDK implementation of the algorithms

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Second IQM quantum computer heads to TOYO’s Tokyo R&D centerquantum-computing

Second IQM quantum computer heads to TOYO’s Tokyo R&D center

TOYO Corporation is significantly expanding its quantum computing capacity with a second full-stack system from IQM Quantum Computers, an IQM Spark, to be operational in early 2027 at its Tokyo research and development center. This purchase, following an earlier acquisition of an IQM Radiance 20-qubit computer for AIST’s G-QuAT facility, demonstrates a concrete investment in building Japan’s quantum ecosystem beyond internal research. “What makes this purchase matter isn’t that TOYO bought a second system, it’s what they’re choosing to do with them,” explains IQM CEO Jan Goetz, noting TOYO’s commitment to sharing access, startups, and researchers. The combined systems will support talent development, technology validation, and advanced research across Japan. TOYO’s IQM Spark Expands Quantum Access for Japanese Ecosystem Early 2027 will see the operational launch of TOYO Corporation’s second IQM Spark quantum computer at its Kiba, Tokyo research and development center, specifically designed to bridge the gap between foundational quantum education and practical algorithm validation. This dual focus distinguishes the deployment, extending beyond purely academic research to encompass hands-on learning and rigorous testing of quantum algorithms in a real-world setting. The system’s installation underscores TOYO’s commitment to fostering a robust quantum computing skillset within Japan, preparing a workforce capable of using emerging technologies. TOYO’s acquisition of the IQM Spark represents a substantial investment exceeding a single quantum computer purchase, signaling a long-term strategy to build comprehensive quantum capacity within the Japanese ecosystem. Complementing the previously announced IQM Radiance 20-qubit system slated for installation at AIST’s G-QuAT facility by the end of 2026, the combined systems will provide a versatile platform for diverse quantum computing needs.

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IonQ Demonstrates Hybrid HPC and Quantum-AI Workflows Across Nine Peer-Reviewed Papers at IEEE Quantum Week 2026quantum-computing

IonQ Demonstrates Hybrid HPC and Quantum-AI Workflows Across Nine Peer-Reviewed Papers at IEEE Quantum Week 2026

IonQ Demonstrates Hybrid HPC and Quantum-AI Workflows Across Nine Peer-Reviewed Papers at IEEE Quantum Week 2026 Trapped-ion quantum hardware developer IonQ (NYSE: IONQ) has presented nine peer-reviewed research papers at the 2026 IEEE International Conference on Quantum Computing and Engineering (QCE26) in Toronto, four of which received QCE26 Best Paper Awards. The body of work showcases application-level deployment, error mitigation, and hybrid classical-quantum solver integrations executed across IonQ’s Forte, Forte Enterprise, and 64-qubit Barium development systems (precursor to the IonQ Tempo line) co-processed with NVIDIA CUDA-Q and cuTensorNet software stacks. The technical publications focus on three functional pillars: enterprise engineering optimization, quantum-accelerated AI architectures, and dynamic error mitigation. Among the award-winning papers, IonQ and Synopsys integrated an Iterative-QAOA Graph Partitioning Problem (GPP) solver into LS-DYNA multiphysics finite element analysis (FEA) software, accelerating 35-million-element mesh simulations by up to 14.6%. In computational biology, IonQ and Kipu Quantum executed bias-field digitized counterdiabatic quantum optimization (BF-DCQO) across 46-to-61-qubit instances to solve 3D lattice protein folding for 14-to-16-amino-acid peptides. In quantum AI, IonQ, QuantumBasel, and the University of Basel measured a 24% reduction in classification error alongside a physical QPU energy-to-solution (ETS) break-even crossover against classical simulation at 34 qubits. [ IonQ IEEE QCE26 Award-Winning Papers & Hardware Benchmarks ]Research Project & PartnersAlgorithmic Implementation & Hardware TargetPerformance Metrics & Operational ImpactFEA Linear Algebra Workflows(with Synopsys) [Best Paper]• Iterative-QAOA Graph Partitioning Solver• CUDA-Q (150 Qubits) & IonQ Forte (36 Qubits)• 14.6% Wall-Clock Time Reduction on 35M-Element Meshes• Solved Sedan Car & Rolls-Royce Engine ModelsQuantum AI

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Fujitsu Releases Open Quantum Application Research Package as Open Sourcequantum-computing

Fujitsu Releases Open Quantum Application Research Package as Open Source

Insider Brief PRESS RELEASE — Fujitsu Limited today announced the global open-source release of its quantum application development software, Open Quantum Application Research Package (OpenQARP (code name)). The software offers over 100 software components, including Fujitsu-developed quantum algorithms. These components can be combined to significantly streamline quantum application development. The software is compatible with various […]

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Entrust Turns CBOM Data Into Action With Expanded Cryptographic Security Platformquantum-computing

Entrust Turns CBOM Data Into Action With Expanded Cryptographic Security Platform

Insider Brief PRESS RELEASE — Entrust today announced new capabilities for its Cryptographic Security Platform (CSP) that help organizations turn Cryptographic Bill of Materials (CBOMs) data into action. Government agencies, financial institutions, healthcare organizations, and other critical infrastructure operators are navigating increased cyber threats, shorter certificate lifecycles, the rapid growth of machine and AI identities, […]

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QCentroid Integrates QuantumOps Platform with NVIDIA CUDA-Q for Enterprise Hybrid Application Workflowsquantum-computing

QCentroid Integrates QuantumOps Platform with NVIDIA CUDA-Q for Enterprise Hybrid Application Workflows

QCentroid Integrates QuantumOps Platform with NVIDIA CUDA-Q for Enterprise Hybrid Application Workflows Quantum software and orchestration provider QCentroid has introduced an enterprise hybrid application design framework within its QuantumOps platform, integrating NVIDIA CUDA-Q to automate the placement of quantum components within existing classical high-performance computing (HPC) software stacks. Implemented in partnership with Gradiant and the Galician Supercomputing Center (CESGA) under the QATALIZE project, the workflow establishes an evidence-driven methodology to define, benchmark, and optimize classical-quantum boundaries across enterprise AI, simulation, and generative modeling applications. The operational framework structures hybrid integration into three granular decision levels: component selection (identifying target modules within complex software pipelines), boundary placement (determining the precise insertion layer for parameterized quantum circuits within deep neural architectures), and execution resource assignment. Demonstrated on a conditional Generative Adversarial Network (cGAN) targeting catalyst materials discovery, the platform evaluates alternative hybrid Generator designs—ranging from early latent space quantum transformations to compressed quantum bottlenecks—against a classical PyTorch baseline running on CESGA’s HPC clusters and 32-qubit Qmio superconducting QPU infrastructure. [ QCentroid QuantumOps & NVIDIA CUDA-Q Enterprise Hybrid Cycle ]Architectural StageSoftware & Infrastructure StackOperational Metrics & OutputBaseline & Modeling• Classical PyTorch & HPC Solvers• QuantumOps Expert AI Agents• Candidate Validity & Novelty Baselines• Standardized Use-Case Pack GenerationBoundary Engineering• Interleaved Parametrized Quantum Circuits (PQCs)• NVIDIA CUDA-Q & PyTorch Bindings• Latent Space & Bottleneck Layer Routing• Multi-Level Architecture ComparisonExecution & Validation• GPU-Accelerated cuQu

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BlueQubit Launches $150,000 “Quantum Flywheel” Compute Grant Program Supported by AWS, IBM, and NVIDIA
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quantum-computing

BlueQubit Launches $150,000 “Quantum Flywheel” Compute Grant Program Supported by AWS, IBM, and NVIDIA

BlueQubit Launches $150,000 “Quantum Flywheel” Compute Grant Program Supported by AWS, IBM, and NVIDIA Quantum software developer BlueQubit has launched the “Quantum Flywheel” grant program, a $150,000 compute allocation initiative designed to support quantum algorithm discovery, adversarial classical simulation, and quantum error correction (QEC) research. Supported by IBM, Amazon Web Services (AWS), and NVIDIA, the program provides selected research teams with three months of continuous cloud compute credits spanning QPU, GPU, and CPU hardware clusters alongside BlueQubit’s quantum-native development environment. The grant framework focuses on three research vectors. First, teams will deploy target algorithms onto cloud-accessible IBM quantum hardware to evaluate quantum advantage boundaries in simulation and optimization. Second, projects will perform adversarial classical simulations utilizing tensor networks, Pauli-path methods, and state-vector heuristics on NVIDIA GPU instances on AWS to benchmark and stress-test quantum advantage claims. Third, research teams will utilize frontier AI models to discover, decode, and analyze novel QEC codes to improve hardware-level noise suppression. [ BlueQubit Quantum Flywheel Program & Compute Infrastructure Scope ]Program ComponentCompute Infrastructure & ToolingTarget Research Track & OutputFunding & Duration• $150,000 Total Cloud Compute Pool• 3-Month Continuous Execution Window• Open-Source Circuit Repositories• Peer-Reviewed Preprints & BenchmarksHardware Ecosystem• IBM Cloud Quantum Processors (QPUs)• NVIDIA GPU Clusters on AWS• Large-Core CPU Simulation Nodes• QPU Execution & Algorithm Validation• Adversarial Tensor-Network Verification• AI-Driven QEC Code Discovery & DecodingExecution Stack• BlueQubit Quantum-Native Platform• AI Code Generation & Circuit Synthesis• End-to-End Workflow Pipeline Integration• Reproducible Cross-Platform Baselines The program features a five-week proposa

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Quandela and NVIDIA Outline Photonic QPU Integration Architecture via NVQLinkquantum-computing

Quandela and NVIDIA Outline Photonic QPU Integration Architecture via NVQLink

Quandela and NVIDIA Outline Photonic QPU Integration Architecture via NVQLink Photonic quantum computing developer Quandela and NVIDIA have published a joint technical white paper outlining an architectural framework to integrate photonic Quantum Processing Units (QPUs) into classical AI and High-Performance Computing (HPC) environments using NVIDIA NVQLink. Presented at IEEE Quantum Week 2026 in Toronto, the paper defines a progressive operational model for co-locating photonic QPUs alongside GPU and CPU supercomputing nodes within data centers. The technical integration links Quandela’s proprietary Quantum System Controller (QSC)—which governs real-time FPGA pulse control and optoelectronic routing for its MosaiQ QPU—directly to NVIDIA GPU nodes using NVQLink’s low-latency interconnect architecture. Built on Remote Direct Memory Access over Converged Ethernet (RoCE) operating at sub-4-microsecond round-trip latency budgets, the setup permits microsecond-scale execution loops between GPU classical nodes and QPU control electronics. This enables GPU-accelerated state-vector and tensor-network dynamics simulations, real-time quantum error correction (QEC) decoding, and automated QPU pulse calibration to execute within a unified host process running NVIDIA CUDA-Q and the open-source MerLin Quantum Machine Learning (QML) framework. [ Quandela & NVIDIA Hybrid Photonic Architecture Parameters ]System LayerHardware & Transport StackSoftware & Algorithmic FrameworkPhotonic QPU Control• Quandela Quantum System Controller (QSC)• FPGA Real-Time Optoelectronic Control• Direct Hardware Driver Execution• Scalable Spin-Optical QPU IntegrationInterconnect Infrastructure• NVIDIA NVQLink (RoCE Transport Protocol)• Sub-4 Microsecond Round-Trip Latency• Microsecond GPU-QSC Real-Time Callbacks• RDMA Low-Latency Network CouplingHybrid Compute Stack• NVIDIA Hopper / Blackwell GPU Nodes• MosaiQ & SPOQC Photonic Hardware• NVIDIA CUDA-Q & cuQuantum SDKs• MerLin Photonic

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Heuristic Quantum Amplitude Amplification: A Traffic QUBO Case Studyquantum-computing

Heuristic Quantum Amplitude Amplification: A Traffic QUBO Case Study

--> Quantum Physics arXiv:2609.13505 (quant-ph) [Submitted on 11 Sep 2026] Title:Heuristic Quantum Amplitude Amplification: A Traffic QUBO Case Study Authors:Kip Nieman, Daniel Koch View a PDF of the paper titled Heuristic Quantum Amplitude Amplification: A Traffic QUBO Case Study, by Kip Nieman and Daniel Koch View PDF HTML (experimental) Abstract:Quantum Amplitude Amplification (QAA), the generalization of Grover's algorithm, is well-positioned for combinatorial optimization and is particularly promising for Quadratic Unconstrained Binary Optimization (QUBO) problems. QAA is appealing due to its ability to drive the quantum system to a target state, yielding the globally optimal solution with probability over $90$+%. However, realizing QAA for application-scale optimization currently exceeds quantum hardware capacity, which is further compounded by unresolved algorithmic challenges regarding the choice of free parameters. In this study, we address these issues by utilizing a realistic traffic flow QUBO problem to investigate the implementation of QAA as a heuristic solver. Specifically, allowing the diffusion operator parameter to take non-$\pi$ values expands the capabilities of QAA. This unlocks a new multiple-shot, low-iteration strategy that aims for a high cumulative probability rather than maximizing the probability of a single basis state. Using $25$-qubit simulations, our results demonstrate that heuristic QAA addresses two of the main challenges cited in previous studies. Firstly, heuristic QAA works even at a single iteration, reducing circuit depth by orders of magnitude compared to standard QAA. And secondly, we show that the problem-dependent parameters necessary for reaching optimal algorithmic performance can be reliably approximated with minimal upfront classical computing overhead. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.13505 [quant-ph]   (or arXiv:2609.13505v1 [quant-ph] for this version)   https://doi.org/10.48550/arX

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Energy Is Not Enough: Leakage-Aware Ansatz Criterion for Variational Quantum Eigensolversquantum-computing

Energy Is Not Enough: Leakage-Aware Ansatz Criterion for Variational Quantum Eigensolvers

--> Quantum Physics arXiv:2609.13521 (quant-ph) [Submitted on 11 Sep 2026] Title:Energy Is Not Enough: Leakage-Aware Ansatz Criterion for Variational Quantum Eigensolvers Authors:Yuan-Chieh Chen View a PDF of the paper titled Energy Is Not Enough: Leakage-Aware Ansatz Criterion for Variational Quantum Eigensolvers, by Yuan-Chieh Chen View PDF HTML (experimental) Abstract:In molecular variational quantum eigensolver (VQE) calculations for a fixed electron-number sector, the projected-sector energy alone does not certify that a prepared state is physically valid. We show that a state can have zero projected-sector error while almost all of its probability mass lies outside the target particle-number sector. We therefore evaluate ansatz families using three quantities: projected-sector error, leakage outside the physical sector and parameter count. Our noiseless experiments show that particle-preserving UCC-style families eliminate leakage induced by ansatze, but do not guarantee ground-state accuracy. In the parameter-budget comparison experiments, the one-shot ranking rules select identical generator sequences and yield identical errors. The adaptive hybrid procedure achieves lower mean projected-sector errors at selected budgets. Using established Kraus invariant-subspace criteria, we also express channel-induced leakage through a positive operator and discuss global and local depolarizing noise analytically. This gives a practical diagnostic framework for comparing ansatze beyond energy alone. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.13521 [quant-ph]   (or arXiv:2609.13521v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.13521 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuan-Chieh Chen [view email] [v1] Fri, 11 Sep 2026 20:44:06 UTC (96 KB) Full-text links: Access Paper: View a PDF of the paper titled Energy Is Not Enough: Leakage-Aware Ansatz Criterion for Variation

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Batched pattern search for QAOA parameter optimization on cloud-accessed quantum hardwarequantum-computing

Batched pattern search for QAOA parameter optimization on cloud-accessed quantum hardware

--> Quantum Physics arXiv:2609.13669 (quant-ph) [Submitted on 12 Sep 2026] Title:Batched pattern search for QAOA parameter optimization on cloud-accessed quantum hardware Authors:Muhammad Faryad View a PDF of the paper titled Batched pattern search for QAOA parameter optimization on cloud-accessed quantum hardware, by Muhammad Faryad View PDF HTML (experimental) Abstract:On a cloud-accessed quantum processor the classical optimizer of a variational algorithm communicates with the device through submitted jobs, and each job carries a queueing and turnaround overhead that does not depend on how many circuits it contains. We study a simple consequence of this for the quantum approximate optimization algorithm (QAOA). An optimizer whose next set of trial parameters is known before any result returns can evaluate the whole set in one job, whereas a sequential optimizer such as COBYLA spends one job per function evaluation. We compare a batched pattern search with COBYLA on IBM's \ibmfez\ processor for cardinality-constrained portfolio selection with six to twelve assets, giving both optimizers the same number of jobs and the same number of shots. At every size the batched search reaches, after its first job, a parameter quality that the serial optimizer takes several jobs to match, and the two methods converge to comparable final values; repeating the eight-asset comparison from four starting points gives the same ordering each time. The instances are small enough to be solved exactly, which lets us check that the device's output distribution is correlated with the true ranking of portfolios and is clearly separated from a control circuit of the same depth with the cost layer removed. A short scan over circuit depth at classically optimal parameters shows that on this device the measured solution quality peaks at two or three QAOA layers. The protocol is described in enough detail to be reproduced, and the notebooks and raw counts are released. Comments: Subjects: Quantu

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Building or Buying Access to Quantum Computingquantum-computing

Building or Buying Access to Quantum Computing

Building or Buying Access to Quantum Computing Guest post by Dr. Leandro Aolita, Chief Researcher, Quantum Research Centre, Technology Innovation Institute Whoever fabricates, tests and refines physical qubits now is helping to determine what the standard hardware of the next decade looks like, rather than adopting a standard that already exists. Organizations around the world now have access to quantum computing but almost all of them simply rent time on someone else’s machine, typically through a cloud account with one of a handful of hardware providers. Far fewer have built one from chip design through fabrication to the software layer that turns a physical device into something a researcher can run. The capacity to build it determines whether an institution, or a country, participates in shaping how the technology develops or remains a customer of it. In February 2026, my team at the Technology Innovation Institute’s Quantum Research Centre opened cloud access to superconducting quantum processing units (QPUs), including QPUs we designed and fabricated in-house at our Quantum Computing Hardware Lab in Abu Dhabi. The systems available through the platform range from 5 to 25 qubits. Our newest in-house chips hold their quantum state up to ten times longer than our first-generation prototypes did. A few dozen qubits is certainly small by the standards of the largest global players, but the number is not the interesting part. What matters is that every stage of the chain sits in-house: the physical chip design, the fabrication, the control electronics, and Qibo, the open-source software framework our quantum middleware team built to let a researcher submit a job and run it seamlessly on either a simulator or the physical hardware. That full chain, from design through fabrication to cloud operation, is held by a comparatively short list of organizations worldwide. The software layer is also released as open source. Qibo lets researchers outside TII build quantum circ

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Phasecraft and NVIDIA Benchmark 3,000+ VQE Molecular Emulations Under Wellcome Leap Q4Bio Programquantum-computing

Phasecraft and NVIDIA Benchmark 3,000+ VQE Molecular Emulations Under Wellcome Leap Q4Bio Program

Phasecraft and NVIDIA Benchmark 3,000+ VQE Molecular Emulations Under Wellcome Leap Q4Bio Program Quantum algorithm developer Phasecraft has partnered with NVIDIA to construct the largest-known Variational Quantum Eigensolver (VQE) emulated molecular database, leveraging accelerated computing infrastructure under the Wellcome Leap Quantum for Bio (Q4Bio) program. Hosted on an NVIDIA Hopper architecture cluster at the University of Nottingham, the initiative benchmarked over 3,000 unique VQE circuit emulations across 13 distinct molecular systems relevant to biological and materials science applications. The technical integration paired Phasecraft’s hardware-adaptive quantum algorithms with the NVIDIA cuQuantum software development kit (SDK) to execute large-scale classical-quantum hybrid simulations spanning 4 to 32 qubits, with primary circuit density concentrated in the 24-to-28 qubit regime. By coupling cuQuantum-accelerated state-vector and tensor-network emulations with Phasecraft’s proprietary quantum-enhanced Density Functional Theory (DFT) functionals, the combined computing framework achieved a 15-fold execution speedup over prior electronic structure baseline models. [ Phasecraft & NVIDIA VQE Emulation Performance & Technical Scope ]Program FrameworkCompute & Simulation InfrastructureAlgorithmic & Benchmark Outputs• Wellcome Leap Q4Bio Program• NVIDIA Hopper GPU Cluster (Univ. of Nottingham)• 3,000+ Unique VQE Emulated Circuits• Life Sciences & Health Focus• NVIDIA cuQuantum SDK Parallelization• 13 Molecular Systems Modeled• Target: DFT Functional Training• Qubit Emulation Scale: 4 to 32 Qubits (Core: 24–28Q)• 15x Execution Speedup vs. Prior Baselines The resulting dataset provides a computational baseline to train quantum-enhanced DFT functionals, improving electronic ground-state calculation accuracy for many-body biological systems. Under CEO Ashley Montanaro and NVIDIA Director of Quantum Product Sam Stanwyck, the collaboration demo

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Kvantify workshop explores tools for efficient quantum programmingquantum-computing

Kvantify workshop explores tools for efficient quantum programming

Researchers from quantum computing and programming languages will convene this October to address a central challenge in the field: efficiently implementing complex algorithms on current quantum hardware. The workshop, informed by results from the ODAQS project, will showcase emerging tools designed to make quantum software development more scalable and reliable. Kvantify co-founder and CSO Nikolaj Thomas Zinner will open the event, which aims to bridge the gap between theoretical quantum concepts and practical application. Participants will gain insight into optimization and verification methodologies, with presentations from Aarhus University, University of Southern Denmark, and Aalborg University researchers. ODAQS Project Insights into Efficient Quantum Implementations Kvantify will demonstrate its Qrunch software at a workshop on October 14, 2026, showcasing a tool designed to optimize quantum applications for current hardware. Lead Quantum Applications & Research Scientist Patrick Ettenhuber will lead the demonstration, providing attendees with a practical view of optimization techniques informed by the ODAQS project. Professor Jaco van de Pol of Aarhus University will present quantum circuit optimization procedures utilizing Q-Synth, building on research originating from the ODAQS project. Van de Pol will also deliver an introductory overview of the ODAQS project itself, outlining its goals and key findings. Irfansha Shaik, a Quantum Research Engineer at Kvantify will detail agentic and symbolic tools aimed at optimizing hardware-aligned circuit primitives, addressing a critical bottleneck in quantum computation. The workshop’s scope extends beyond circuit optimization; Principal Quantum Research Engineer Søren Fuglede Jørgensen will present Clifford circuit synthesis for quantum chemistry applications, while Assistant Professor Erik Kjellgren from the University of Southern Denmark will discuss quantum circuits for spin-adapted fermionic operators. Profe

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