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Quantum Computing Market Analysis: Industry Trends & Investment

Quantum computing market news: market size, industry analysis, quantum investment, market forecast. Quantum computing stocks & funding.

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The quantum computing market is transitioning from research to commercial reality, with projections ranging from $1 billion (2024) to $125 billion by 2032 depending on fault-tolerant system development.

Market segmentation by offering type includes quantum hardware (30%), quantum software (25%), and quantum services (45%). By application: optimization (35%), simulation (30%), machine learning (20%), and cryptography (15%).

India's Quantum Market Landscape

India's National Quantum Mission represents a ₹6,003.65 crore ($720 million) government investment through 2030-31, making it one of the top 5 government quantum programs globally. The mission aims to capture a significant share of the growing quantum market by developing indigenous capabilities across computing, communication, sensing, and materials.

India's quantum startup ecosystem received government support through NQM and NM-ICPS (National Mission on Interdisciplinary Cyber-Physical Systems). Eight startups selected in November 2024 include: QNu Labs (Bengaluru): Quantum-safe networks and QKD systems; QpiAI India (Bengaluru): Superconducting quantum computer development; Dimira Technologies (IIT Mumbai): Cryogenic cables for quantum computing; Prenishq (IIT Delhi): Precision diode-laser systems; QuPrayog (Pune): Optical atomic clocks; Quanastra (Delhi): Advanced cryogenics and superconducting detectors; Pristine Diamonds (Ahmedabad): Diamond materials for quantum sensing; Quan2D Technologies (Bengaluru): Superconducting nanowire single-photon detectors.

Tata Consultancy Services (TCS) partners with IBM on quantum computing with significant investment in quantum algorithm development. The Quantum Valley Tech Park in Andhra Pradesh represents a major public-private quantum computing investment.

A gauge-invariant theory of small Markovian errors in quantum gate setsquantum-computing

A gauge-invariant theory of small Markovian errors in quantum gate sets

--> Quantum Physics arXiv:2608.14891 (quant-ph) [Submitted on 14 Aug 2026] Title:A gauge-invariant theory of small Markovian errors in quantum gate sets Authors:Juan Gonzalez De Mendoza, Corey Ostrove, Timothy Proctor, Kevin Young, Erik Nielsen, Robin Blume-Kohout View a PDF of the paper titled A gauge-invariant theory of small Markovian errors in quantum gate sets, by Juan Gonzalez De Mendoza and 5 other authors View PDF HTML (experimental) Abstract:Noisy logic operations on a quantum computational register -- e.g., one or more qubits -- can be described by transfer matrices (a.k.a. CPTP maps or superoperators) that act linearly on the density matrix representing the register's quantum state. Collectively, these operations form a gate set. Gate sets have a gauge freedom; many gate sets that appear different actually predict the same experimental outcomes. A property of a gate set can be observable (and thus physically relevant) only if it is gauge-invariant. Unfortunately, no good gauge-invariant parameterizations of gate sets are known. We introduce the next best thing, a perturbative gauge-invariant parameterization of small Markovian errors in gate sets. We construct vector spaces of properties that are first-order gauge-invariant (FOGI). We show how to construct and understand FOGI properties, how to use them as coordinates to parameterize gate sets without gauge freedom, and how to extract approximately gauge-invariant error metrics. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.14891 [quant-ph]   (or arXiv:2608.14891v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.14891 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Juan Gonzalez De Mendoza [view email] [v1] Fri, 14 Aug 2026 21:00:06 UTC (1,002 KB) Full-text links: Access Paper: View a PDF of the paper titled A gauge-invariant theory of small Markovian errors in quantum gate sets, by Juan Gonzalez De Mendoza

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Floquet-Liouville Theory for Strongly Driven Open Quantum Systemsquantum-computing

Floquet-Liouville Theory for Strongly Driven Open Quantum Systems

--> Quantum Physics arXiv:2608.14966 (quant-ph) [Submitted on 15 Aug 2026] Title:Floquet-Liouville Theory for Strongly Driven Open Quantum Systems Authors:Kamran Akbari, Stephen Hughes View a PDF of the paper titled Floquet-Liouville Theory for Strongly Driven Open Quantum Systems, by Kamran Akbari and Stephen Hughes View PDF HTML (experimental) Abstract:Periodically driven quantum systems are commonly modeled using master equations constructed in the eigenbasis of an undriven Hamiltonian, implicitly assuming that environmental dissipation couples to static energy transitions even under strong time-periodic driving. The validity of this approximation beyond weak or near-resonant driving remains poorly understood. To address the need for a more self-consistent quantum theory approach, we formulate a nonsecular Floquet--Markov generalized master equation (F-GME) in the quasienergy basis, treating interaction-induced (internal) and drive-induced (external) nonperturbative dressing on an equal footing. We subsequently investigate dissipation in two minimal driven open quantum systems---a harmonically driven two-level system and a harmonically driven coupled-two-level-system---each weakly coupled to a Markovian bath. Comparing the F-GME to a time-independent dressed-basis master equation, we show that even for a flat-bath spectral density and weak dissipation, the two approaches can yield qualitatively different steady-state populations and emission spectra. We resolve dissipation into drive-assisted sideband processes decaying via Floquet extended-space quasienergy channels, and show these channels can hybridize through nonsecular couplings into collective Floquet--Liouville modes governing observable spectral resonances. This analysis demonstrates that time-independent dissipative descriptions can incorrectly weight multiphoton Floquet transitions by collapsing quasienergy-resolved decay pathways into static energy gaps. The F-GME framework provides a systematic diagno

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Infleqtion: The Execution Phase (Rating Downgrade)quantum-computing

Infleqtion: The Execution Phase (Rating Downgrade)

Sean Daly2.24K FollowersFollowSummaryInfleqtion, Inc. reported 116% YoY revenue growth to $12.6M, raised FY guidance, and maintains $582M in cash with a quarterly burn rate of $14M.INFQ's neutral atom quantum technology underpins unique defense and commercial products, driving government contracts and strategic partnerships with entities like Nvidia, NASA, and Safran.Despite strong execution and a robust patent portfolio, insider selling and SPAC-related risks, plus near-term revenue headwinds, temper immediate upside.I rate INFQ stock a Hold due to recent stock gains, looming Q3 weakness, and a seasonally challenging market backdrop. gorodenkoff/iStock via Getty Images The hype giveth, and the hype taketh away. Since my last report on Infleqtion, Inc. (INFQ), the company has been on a wild ride. New U.S. mandates for quantum investment and a series of newThis article was written bySean Daly2.24K FollowersFollowSean Daly writes on ETFs, biotech and FINTECH solutions in the banking space.  He teaches international finance and financial risk management at Pace University and was a visiting lecturer at Princeton University from 2005 to 2009.  He was educated at Columbia University.  He has also written extensively on real estate and  economic development, exploring issues as diverse as Chinese urbanization, CMI multilateral currency swap arrangements, energy geopolitics, and Asia's sovereign wealth funds.    Global strategy and private equity background. Equity Approach: long/short, event-driven, with a focus on small cap biotech and the emerging markets.Analyst’s Disclosure: I/we have a beneficial long position in the shares of INFQ either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Seeking

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Hybrid quantum-neural network beats classical machine learningquantum-computing

Hybrid quantum-neural network beats classical machine learning

Researchers at the Institute of Fundamental and Frontier Sciences, University of Electronic Sciences and Technology of China have combined boson sampling, a quantum process with experimentally verified advantage over classical computers, with neural networks to improve machine learning classification. The team developed a hybrid framework where a neural network compresses data features onto a boson sampling circuit, generating quantum states that enhance support vector machine performance. Using four datasets with various classes, the model outperformed classical linear and sigmoid kernels, demonstrating the potential of boson sampling-based quantum kernels for practical quantum-enhanced machine learning. Hybrid Boson Sampling-Neural Network Architecture for Enhanced Classification The core innovation lies in a neural network’s ability to compress complex data features, preparing them for processing by a programmable boson sampling circuit. This approach addresses a significant hurdle in quantum machine learning: the high dimensionality of practical datasets. The team’s framework utilizes the neural network to reduce the number of features needed for analysis, bridging the gap between large, complex data and the limitations of current quantum hardware. The resulting quantum states, generated by the boson sampling circuit, span a high-dimensional space, enabling improved classification performance. The researchers tested their model against four distinct datasets, Ionosphere, Spambase, MNIST, and Fashion-MNIST, each containing various classes of data, and the hybrid model outperformed classical linear and sigmoid kernels in these tests. The researchers found that achieving enhanced accuracy depended on utilizing a sufficiently expressive boson sampling circuit, with expressivity controlled by both the number of modes and injected photons. This suggests a pathway to optimize the quantum component for specific classification tasks. Mohammad Sharifian explained in their

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Quanta Computer Will Scale Up Quantinuum’s Quantum Systemsquantum-computing

Quanta Computer Will Scale Up Quantinuum’s Quantum Systems

Quanta Computer, a Fortune Global 500 manufacturer, is partnering with quantum computing company Quantinuum to address the challenges of scaling up quantum systems for commercial use. Joint engineering work is already underway between the companies to design hardware infrastructure focused on making future quantum computers more modular, manufacturable, and scalable. “It is time for quantum computing to transition from breakthroughs in physics achieved in the lab to breakthroughs in system manufacturing that can be deployed and operated at scale,” said Dr. Rajeeb Hazra, President and CEO of Quantinuum. This collaboration aims to establish an industrial foundation for large-scale quantum computing, moving beyond theoretical advancements to practical production. Quantinuum’s QCCD Architecture Drives Scalable System Development Central to plans for scaling quantum systems is Quantinuum’s established QCCD architecture. The company is collaborating with manufacturing giant Quanta Computer, and this partnership prioritizes practical deployment of existing technology rather than focusing solely on qubit development; Quantinuum has already commercially released multiple generations of trapped-ion systems built on this architecture. Quanta Computer’s involvement signals a shift toward industrializing quantum computing beyond startup ventures, leveraging their experience with advanced computing platforms, Quantinuum says. Dr. Hazra stated, “Quanta has earned a global reputation for industrializing some of the most advanced computing technologies in the world.” The collaboration aims to ensure supply chains and engineering expertise develop alongside the quantum technology itself. This isn’t merely a research agreement; the companies are co-developing hardware infrastructure to support future generations of Quantinuum’s quantum systems, creating a pathway to commercially viable, large-scale fault-tolerant quantum computers capable of wider adoption. The focus on manufacturabil

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Researchers link quantum data limits to geometry and measurementquantum-computing

Researchers link quantum data limits to geometry and measurement

Researchers at the Technical University of Denmark’s bigQ center, working with collaborators from Finland, Germany, Korea, and Israel, have linked limits on quantum data precision to symplectic geometry, a mathematical branch typically used to study shapes and spaces. The study focuses on the Gaussian quantum Fisher information, revealing this pattern isn’t random but dictated by the underlying geometry of a quantum system. This connection builds a bridge between theoretical symplectic geometry and metrology, potentially impacting the development of more precise quantum sensors and technologies. Gaussian Quantum Fisher Information’s Even-Odd Decomposition This connection, published in Quantum Science and Technology, offers a novel approach to understanding and potentially improving data limitations in quantum technologies. The study specifically focuses on splitting the Gaussian quantum Fisher information into “even” and “odd” components; this division isn’t arbitrary, but reflects fundamental geometric properties. On pure-state manifolds, the researchers found the even contribution vanishes entirely, while the odd component aligns with the quantum Fisher information derived from the natural metric on the Siegel upper half-space, directly revealing a geometric basis for pure-Gaussian metrology. This also provides a way to express the quantum Fisher information using the graphical representation of pure Gaussian states and its parameters. The research clarifies how different types of quantum operations impact these components; for evolutions generated by passive Gaussian unitaries, specifically orthogonal symplectics, the odd quantum Fisher information disappears, with thermometric parameters contributing solely to the even sector in a predictable spectral form. The team also derived a state-dependent lower bound on the even quantum Fisher information, linked to the rate of purity change within the system. Applications to unitary sensing, comparing beam splitters to

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AMD filing reveals unexpected SpaceX and Nutanix betquantum-computing

AMD filing reveals unexpected SpaceX and Nutanix bet

Most investors watch what AMD builds. Fewer watch what it buys. A quarterly filing with the Securities and Exchange Commission just gave them a reason to look. Advanced Micro Devices (AMD) disclosed large equity stakes in two very different companies: Elon Musk's SpaceX (SPCX) and enterprise ...

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Boulder Team Finds Type III Algebras Need Infinite Magicquantum-computing

Boulder Team Finds Type III Algebras Need Infinite Magic

Mudassir Moosa of the University of Colorado investigated statistical mechanics in the setting of Type III von Neumann algebras, where concepts like density matrices and traces are inapplicable, are under investigation. The algebras fundamentally require an infinite amount of magic. Finite-dimensional quantum systems, such as lattice systems, in the thermodynamic limit are considered. States in the thermodynamic limit possessing only a bounded amount of magic result in a local von Neumann algebra that cannot be of Type III. This result has direct implications for quantum simulations of quantum field theories, where the algebra of a local subregion is known to be of this type. Quantifying magic state resources for simulating strongly correlated quantum systems Scientists are exploring quantum simulations of strongly coupled quantum field theories and quantum gravity models to gain novel insights into the physics of black holes and the emergence of spacetime. Understanding the resource requirements for these simulations presents a fundamental problem. In fault-tolerant quantum computing, the number of non-Clifford gates needed to perform a task is usually a suitable metric for the required resources, due to the Gottesman-Knill theorem, which states that a quantum circuit involving only Clifford gates can be simulated on a classical computer in polynomial time. Distilling magic states is highly resource-intensive and typically dominates the overhead of the overall algorithm. Quantifying the number of non-Clifford gates, or simply magic, required to simulate strongly coupled quantum systems is therefore an important problem. This line of study began in a previous work, where the magic in the ground states of the Z3 Potts model at its critical point scales extensively with the system size. Recent work has explored the role of magic in the AdS/CFT correspondence and holographic error-correcting codes, revealing that if the holographic code is an exact stabilizer code or a

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Global Photonics Economic Forum gathers 400+ CEOs to discuss photonics future.quantum-computing

Global Photonics Economic Forum gathers 400+ CEOs to discuss photonics future.

More than 400 executives and decision-makers will convene in Málaga, Spain, September 24-25 for Optica’s Global Photonics Economic Forum, indicating substantial industry investment in the future of photonics. The forum will bring together leaders examining how photonics drives advances in areas from artificial intelligence to aerospace; early bird registration closes September 4. “Photonics is increasingly recognized as a strategic technology that underpins economic growth, national competitiveness and technological leadership,” said José Pozo, Chief Technology Officer at Optica. The event will also recognize Lumentum’s Michael Hurlston and TRUMPF with the 2026 Optica i4 Prizes for leadership and innovation. Optica’s Global Photonics Economic Forum: Industry Leaders Converge in Málaga The forum, organized by Optica, will take place September 24-25 and focuses on the business strategies needed to translate photonics innovation into marketable products. This event concentrates on the economic and strategic future of optics and photonics, assembling leaders who drive growth within the global ecosystem. The forum’s agenda includes discussions on critical areas such as artificial intelligence infrastructure and the development of resilient supply chains, reflecting the increasing importance of photonics across multiple sectors. Leaders will also address challenges related to industrial policy and maintaining global competitiveness in a rapidly evolving technological landscape. The Global Photonics Economic Forum is the only international forum exclusively dedicated to the economic future of optics and photonics. Attendees will have opportunities to network with CEOs of billion-dollar companies, technology pioneers, investors, and policymakers, fostering collaborations and translating ideas into action through exclusive receptions and an industry exhibition. Registration rates will increase after September 4, with standard rates varying based on membership status and VAT

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QpiAI Inaugurates 8-Inch Quantum Chip Foundry in Bengaluru Targeting 10,000-Qubit QPUsquantum-computing

QpiAI Inaugurates 8-Inch Quantum Chip Foundry in Bengaluru Targeting 10,000-Qubit QPUs

QpiAI Inaugurates 8-Inch Quantum Chip Foundry in Bengaluru Targeting 10,000-Qubit QPUs Full-stack quantum and AI startup QpiAI has inaugurated an 8-inch quantum processing unit (QPU) manufacturing facility in Jakkur, Bengaluru. Formally designated as Phase 2 of its 70,000-square-foot R&D center, the quantum foundry can fabricate flip-chip superconducting quantum processors with up to 128 physical qubits. The company plans to complete Phase 3 by 2027, expanding the cleanroom infrastructure to fabricate single QPUs containing up to 10,000 physical qubits. [ QpiAI Vertically Integrated Quantum Foundry Stack ] │ ┌────────────────────────────────────────┼────────────────────────────────────────┐ ▼ ▼ ▼ Cleanroom Lithography & Packaging Transmon & Fluxonium QPU Lineup Quantum Supremacy Centres (QSCs) • Class 100 & Class 1,000 Cleanrooms. • QVidya (8-qubit Transmon). • 10-Acre Campus in India. • 8-Inch Wafer Flip-Chip Assembly. • Indus (25-qubit Transmon). • 4 International QSC Sites Planned. • End-to-End Etching & 3D Stacking. • Kaveri (64-qubit) / Yukti (9-qubit). • Hybrid QPU-AI Data Infrastructure. Foundry Capabilities and Processor Roadmap The facility handles the full device manufacturing lifecycle on-site—including electron-beam lithography, wet/dry etching, patterning, Josephson-junction fabrication, 3D flip-chip assembly, and cryogenic packaging. Operating with Class 100 and Class 1,000 cleanroom specifications, the foundry manufactures superconducting QPUs along with peripheral control chips and sensors, while supporting R&D into photonic and semiconductor spin qubits. QpiAI has already fabricated four primary quantum processors at the site: QVidya: An 8-qubit superconducting transmon processor. Indus: A 25-qubit transmon QPU integrated into hybrid classical HPC data centers. Kaveri: A 64-qubit superconducting transmon chip using proprietary low-loss flip-chip interconnects. Yukti: A 9-qubit processor based on a fluxonium qubit variant, des

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Researchers Bound Cryptographic Leakage in GKP State Aggregationquantum-computing

Researchers Bound Cryptographic Leakage in GKP State Aggregation

Nilesh Vyas, Airbus Central R&T, and colleagues have created an active, measurement-based framework for aggregating multiple Gottesman-Kitaev-Preskill (GKP) states, overcoming limitations of passive linear optics that compressed the phase-space lattice and caused quantum data loss. The method preserves the code space geometry up to correctable deformations, achieving a lattice spacing of √π, an improvement on the √2π resulting from previous passive approaches. A new technique combines quantum information across a network, resolving a key limitation in continuous-variable quantum computing systems. The team addressed issues stemming from signal loss and distortion when employing conventional optical methods, enabling more dependable and geometrically-precise aggregation of quantum states. This provides a theoretical basis for constructing secure and strong quantum networks utilising this approach, allowing for the preservation of the structure of quantum data during aggregation. The researchers R&T developed a new method for combining quantum information across a network, addressing a key obstacle in continuous-variable quantum computing. They tackled the problem of signal loss and distortion that occurs when using traditional optical techniques to merge quantum states, enabling more reliable and geometrically-accurate aggregation. This is achieved using Gottesman-Kitaev-Preskill (GKP) coding, which encodes quantum information using the position and momentum of light, similar to how a vinyl record stores information in its grooves. The new framework preserves the structure of quantum data during aggregation, allowing for the construction of secure and strong quantum networks, though scaling this approach requires careful consideration of error accumulation during merging. Optimised quantum aggregation via active error correction and GKP states Airbus Central R&T personnel achieved a lattice spacing of √π in aggregated quantum states, a substantial improve

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Researchers Cut Quantum Resource Demand for Power Grid Islandingquantum-computing

Researchers Cut Quantum Resource Demand for Power Grid Islanding

A new method limits the spread of disturbances in electrical grids through controlled islanding, partitioning a compromised grid into connected, electrically sustainable islands. Classical methods face sharply growing computational costs as network size and island count increase. Quantum optimisation offers an alternative for exploring this combinatorial partition space. However, monolithic quantum formulations encode all assignment decisions in one circuit, causing qubit demand and circuit complexity to scale with network size. In this study, Yuqi Jiang of Tsinghua University and colleagues propose a qubit-bounded sequential distributed quantum approximate optimisation algorithm (QAOA) framework to tackle coherent controlled islanding under limited quantum resources. It formulates the optimal islanding strategy through a series of sequential QAOA optimisations. Distributed quantum algorithm tackles large-scale power grid partitioning with fewer qubits A five-fold reduction in qubits needed for controlled islanding has been achieved, resolving problems with 300 buses, a scale previously inaccessible to monolithic quantum approximate optimisation algorithm (QAOA) approaches. Modern power systems are undergoing a significant transformation with the increasing integration of distributed energy resources (DERs) such as solar photovoltaic arrays, wind turbines, and energy storage systems. While these DERs offer numerous benefits, including increased resilience and reduced carbon emissions, they also introduce substantial variability and uncertainty into the power grid. This variability stems from the intermittent nature of renewable energy sources and the decentralised control of these resources. During disturbances, such as faults or sudden load changes, these effects can intensify generation-load imbalances and potentially lead to cascading failures, resulting in widespread blackouts. Controlled islanding, a proactive grid management technique, aims to mitigate these r

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Researchers Cut Signal-Learning Measurements by Seven Million-Foldquantum-computing

Researchers Cut Signal-Learning Measurements by Seven Million-Fold

Researchers demonstrate that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals. These rigorous quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations. Quantum feature sensing accelerates signal learning and dark matter simulations A tenfold million-fold reduction in measurements needed for learning both Fourier amplitudes and time-varying signals has been achieved, a feat previously unattainable with conventional sensors. This substantial decrease, demonstrated using a superconducting cavity-qubit architecture, unlocks the potential for practical quantum advantages with minimal hardware requirements. The quantum feature sensing algorithms streamline signal learning and also deliver significant improvements in simulations important for detecting elusive dark matter and enhancing wireless communication systems. Wireless communication system simulations benefited from orders-of-magnitude improvements in performance. The theoretical underpinnings of this improvement are Quantum Phase-Space Inference, a framework establishing lower bounds and optimal algorithms for quantum-enhanced learning, alongside a certificate verifying quantum advantage. Utilising these algorithms, experiments observed a seven-fold increase in the speed of simulations used for detecting weakly interacting massive particles, known as dark matter. Although scaling to complex, real-world scenarios presents an engineering challenge, experiments showed 10^7-fold reductions in measurements for learning signals. Quantum Phase-Space Inference for enhanced sensing and learning Experiments demonstrated a superconducting cavity-qubit architecture achieving 10^7-fold reductions in the number of measurements required for Fourier-amplitude and time-varying signal learning.

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Queensland Team Improves Quantum Simulation Sampling Efficiencyquantum-computing

Queensland Team Improves Quantum Simulation Sampling Efficiency

Accurately simulating molecular structures using quantum computers was previously hampered by uncontrolled growth in the size of classical calculations needed for result interpretation. A breakthrough in sample efficiency has now been achieved via a new measurement protocol founded on non-orthogonal configuration interaction. Connor van Rossum of QueenslandRE Corporation and colleagues have enabled improved simulations of protein-ligand complexes, reaching up to 12,000 atoms, even with limited computational resources, yielding higher quality configurations rather than increasing their number. This improvement allows characterisation of larger protein-ligand complexes, containing up to 12,000 atoms, with greater efficiency and precision utilising current computer resources through enhanced sampling methods that prioritise high-quality configurations over quantity. The team refined a computational technique within quantum computing, specifically Quantum Selected Configuration Interaction (QSCI), to more effectively simulate molecules by addressing limitations where processing demands unexpectedly increased during calculations. QSCI methods leverage the capabilities of quantum computers to identify dominant electronic configurations describing the ground state energy of a molecule; these identified configurations are then used as input for classical computations which determine precise energies. They tackled a key obstacle to wider application by improving candidate molecular characteristic selection during computation, akin to prioritising vital information when describing electron behaviour. This new method efficiently identifies relevant configurations in molecules containing up to 12,000 atoms, even with limited power, prioritising quality over sheer volume. It establishes measurement-basis engineering as a promising route towards improved sampling methods and raises questions about the adequacy of existing benchmarking protocols for assessing performance in these

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QpiAI Opens Quantum Chip Foundry in India, Targets 10,000-Qubit Processorsquantum-computing

QpiAI Opens Quantum Chip Foundry in India, Targets 10,000-Qubit Processors

Insider Brief Quantum computing startup QpiAI has opened a manufacturing facility in Bengaluru capable of fabricating superconducting quantum processors, part of an effort to bring more of its quantum hardware development and production under one roof. The Bengaluru-based company on Monday inaugurated the second phase of its quantum processing unit, or QPU, manufacturing facility in […]

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Researchers Bound Quantum Eigensolver Shots to Linear Scalingquantum-computing

Researchers Bound Quantum Eigensolver Shots to Linear Scaling

Scientists are developing quantum subspace diagonalization methods as promising algorithms for quantum chemistry on near-term quantum computers. These methods can estimate low-lying energies of molecular systems using shallow quantum circuits. This estimation requires many circuit repetitions to determine the projection. Scalable thresholding reduces quantum computational cost for molecular energy simulations A significant reduction in the per-matrix-element shot count needed for accurate quantum calculations has been achieved, improving the scaling from O(M³) to O(M), where M represents the number of reference states. This represents an advancement in the field of quantum computational chemistry, as the computational cost associated with determining molecular energies has historically been a major impediment to simulating larger systems. The improvement hinges on a scalable thresholding scheme that selectively removes poorly overlapping reference states from the calculation. Previously, identifying and discarding these states was computationally expensive, often negating any potential benefits. The core principle behind this technique lies in recognising that reference states exhibiting minimal overlap contribute disproportionately to the noise and instability of the calculation, without significantly impacting the accuracy of the final energy estimate. By judiciously eliminating these states, the computational effort can be dramatically reduced. Establishing that the sensitivity of eigenvalues is now governed by the condition number of the remaining overlap matrix, rather than the initial, larger dimension, represents a step forward in controlling the error propagation within the algorithm. The condition number of a matrix is a measure of its sensitivity to perturbations; a high condition number indicates that even small errors in the input data can lead to large errors in the output. By ensuring that the condition number remains manageable, researchers can effect

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Researchers Build Integrated Waveguide for Ion Trapsquantum-computing

Researchers Build Integrated Waveguide for Ion Traps

Until now, delivering light to trapped ions required complex free-space optics that become impractical as the number of qubits increases. Now, the researchers have developed an ion-trap platform on borosilicate glass with an integrated femtosecond-laser-written waveguide for on-chip light delivery. This system achieves low-loss curved waveguides down to a radius of curvature of 6mm, and successfully demonstrated trapping, ion shuttling, and coherent operations using 729nm light guided through the integrated waveguide. Researchers have engineered a new ion trap using glass channels to deliver light to individual, electrically charged atoms, known as ions. This system uses femtosecond-laser-written waveguides, tiny pathways created with a laser, integrated directly into the trap’s structure; this separates the light delivery from the electrical controls. The design allows for curved light paths and is compatible with standard manufacturing processes, offering a potential route to building more complex quantum computing devices. Researchers have created a new platform for quantum computing using electrically charged atoms, or ions, held in place by electric fields, a microscopic holding pen for single atoms. Current systems rely on bulky free-space optics to deliver the light needed to control these ions, a method that becomes increasingly difficult as the number of qubits grows. The team’s innovation integrates light delivery directly into the ion trap using microscopic glass tunnels, created with incredibly short pulses of laser light, that guide light like fibre optics. This approach physically separates the light paths from the electrical controls, enabling curved light delivery and compatibility with existing manufacturing techniques. Reduced waveguide curvature facilitates miniaturised ion trap optical circuits Low-loss curved waveguides now operate at radii down to 6mm, previously limited to 8mm, a key threshold for miniaturising complex optical circuits within

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Researchers Verify and Repair Quantum Ancilla Safety Efficientlyquantum-computing

Researchers Verify and Repair Quantum Ancilla Safety Efficiently

A new framework addresses quantum compilation challenges, utilising ancilla qubits to implement complex operations with fewer gates and reduced depth. Formal verification of this property is computationally key due to state-space explosion with increasing qubit numbers, especially for dirty ancillae which carry unknown initial states and require restoration after use. Jiqi Li of the University of Edinburgh and colleagues propose an end-to-end verification-and-repair framework that rigorously addresses both clean and dirty ancilla safety. Their core contribution is a two-step reduction strategy; they first prove that verifying an m-qubit dirty ancilla register decomposes into 2m independent clean ancilla safety checks, subsequently reducing each clean ancilla safety check. Efficient ancilla verification enables quantum circuits exceeding two thousand qubits Scalability to over two thousand qubits is now possible thanks to a new verification-and-repair framework for ancilla safety in quantum circuits, a strong improvement over prior methods that struggled with even a fraction of this scale. This reduction to algebraic commutativity tests against Pauli-Z and Pauli-X operators enables efficient, parallel verification and actionable diagnosis of errors, classifying them as either logic or phase errors. The significance of this lies in the exponential growth of computational complexity with qubit number; traditional verification methods quickly become intractable as circuit size increases, hindering the development of larger, more powerful quantum computers. Lightweight repair routines, involving single-qubit rotations, were then applied to correct a broad class of local ancilla faults, maintaining circuit functionality in the tested circuits, including benchmarks and Grover’s algorithm. These single-qubit rotations are carefully calibrated to reverse the effects of identified errors without disrupting the overall quantum computation. The application to established benchm

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Tracking real-space quantum state breathing through Floquet-projector geometryquantum-computing

Tracking real-space quantum state breathing through Floquet-projector geometry

--> Quantum Physics arXiv:2608.13649 (quant-ph) [Submitted on 13 Aug 2026] Title:Tracking real-space quantum state breathing through Floquet-projector geometry Authors:Arpit Raj, Johannes Mitscherling, Björn Trauzettel View a PDF of the paper titled Tracking real-space quantum state breathing through Floquet-projector geometry, by Arpit Raj and 2 other authors View PDF HTML (experimental) Abstract:Periodic driving of spatially periodic quantum systems generates band structures that are absent in static crystals. We present a quantum geometric theory to characterize the Floquet-Bloch states at stroboscopic times and during micromotion on equal footing. Our framework builds upon time-evolved Floquet projectors that connect static quantum geometry, micromotion-operator geometry, and Floquet topology. To illustrate the formalism, we introduce the Floquet-projector quantum metric, which we employ to characterize the real-space breathing of localized states in a driven chiral-symmetric integrable spin chain. The Floquet-projector quantum metric, integrated over the Brillouin zone, captures the oscillatory variance during micromotion and, at symmetry-selected times, is bounded below by Floquet topological invariants. We further describe how the Floquet projector geometry enables a systematic investigation of micromotion dynamics in periodically driven lattice systems. Comments: Subjects: Quantum Physics (quant-ph); Mesoscale and Nanoscale Physics (cond-mat.mes-hall) Cite as: arXiv:2608.13649 [quant-ph]   (or arXiv:2608.13649v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.13649 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arpit Raj [view email] [v1] Thu, 13 Aug 2026 18:00:02 UTC (858 KB) Full-text links: Access Paper: View a PDF of the paper titled Tracking real-space quantum state breathing through Floquet-projector geometry, by Arpit Raj and 2 other authorsView PDFHTML (experimental)TeX Source view license Curr

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Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalizationquantum-computing

Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization

--> Quantum Physics arXiv:2608.13691 (quant-ph) [Submitted on 13 Aug 2026] Title:Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization Authors:Rachel Houtz, Marco Knipfer, Konstantin Matchev, Alexander Roman, Mia West View a PDF of the paper titled Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization, by Rachel Houtz and 4 other authors View PDF HTML (experimental) Abstract:Hamiltonian truncation offers a nonperturbative route to quantum field theory, yet its accuracy is limited by the rapid expansion of the truncated Hilbert space, which drives up computational cost. We tackle this bottleneck with a hybrid strategy that pairs classical and quantum algorithms: 1) we develop an efficient basis-generation scheme built on integer partitions; 2) we speed up the construction of the sparse Hamiltonian matrix using symmetry-aware algorithms; and 3) we explore quantum Krylov diagonalization as a route to the low-lying spectrum. Benchmarking against the free massive scalar and $\phi^4$ theories in two spacetime dimensions, we achieve substantial gains in the computational efficiency of Hamiltonian truncation and chart a path toward future quantum implementations. Comments: Subjects: Quantum Physics (quant-ph); High Energy Physics - Lattice (hep-lat); High Energy Physics - Phenomenology (hep-ph); High Energy Physics - Theory (hep-th) Report number: KA-TP-19-2026 Cite as: arXiv:2608.13691 [quant-ph]   (or arXiv:2608.13691v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.13691 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mia West [view email] [v1] Thu, 13 Aug 2026 18:37:19 UTC (5,055 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization, by Rachel Houtz and 4 other authorsView PDFHTML (experimental)TeX Sourc

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