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How Can Cryptography Protect Data, Verify Computation and Prepare Security for the Quantum Era? 28 NTT Research Papers Accepted at CRYPTO 2026 Offer New Insightsquantum-computing

How Can Cryptography Protect Data, Verify Computation and Prepare Security for the Quantum Era? 28 NTT Research Papers Accepted at CRYPTO 2026 Offer New Insights

NTT Research’s Cryptography & Information Security (CIS) Lab and NTT Social Informatics Laboratories in collaboration with Carnegie Mellon, Stanford, and others advance research in post-quantum cryptography, zero-knowledge proofs, secure multiparty computation, attribute-based encryption and quantum security. SUNNYVALE, Calif. and TOKYO, August 17, 2026 — NTT, Inc., a global technology and business solutions provider serving more […] The post How Can Cryptography Protect Data, Verify Computation and Prepare Security for the Quantum Era? 28 NTT Research Papers Accepted at CRYPTO 2026 Offer New Insights appeared first on NTT Research, Inc..

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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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A finance expert joins D-Wave as it scales quantum computingquantum-computing

A finance expert joins D-Wave as it scales quantum computing

D-Wave Quantum Inc. has appointed Kevan P. Krysler, formerly CFO of NYSE-listed Everpure, Inc., to its Board of Directors and Audit Committee, signaling a sharpened focus on financial rigor as the publicly traded quantum computing firm (NASDAQ: QBTS) scales. Krysler currently leads finance at Carbon Robotics, a company building AI-powered agricultural robots, which parallels D-Wave’s increasing emphasis on practical quantum applications beyond research, the company says. “D-Wave is at an exciting stage of growth, with a differentiated technology portfolio and accelerating commercial traction,” said Krysler, adding that his experience will support the company’s continued execution and growth strategy. Kevan Krysler Joins D-Wave Board Amidst Company Scaling Kevan P. Krysler’s appointment signals a heightened focus on financial rigor for D-Wave (NASDAQ: QBTS), a strategy less common among early-stage quantum technology companies that often remain private. Prior to Carbon Robotics, Krysler served as senior vice president of finance and chief accounting officer at VMware, Inc., a major player in cloud infrastructure; this background is increasingly relevant as quantum computing transitions toward delivery as a service. Dr. Alan Baratz, CEO of D-Wave, stated that as adoption of their production-grade annealing quantum computing technology scales and their dual-platform strategy advances, Krysler will be a valuable addition. Sharon Holt, chair of the D-Wave board, emphasized Krysler’s value, stating that his perspectives on capital allocation, operating discipline and navigating complex growth environments will complement the board’s existing expertise. Krysler himself expressed enthusiasm, saying. D-Wave is at an exciting stage of growth, with a differentiated technology portfolio and accelerating commercial traction, as quantum computing becomes an important part of the enterprise technology landscape. Kevan P. Krysler, CFO at Carbon Robotics Source: https://www.business

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Got $200? 1 Quantum Computing ETF to Buy Right Now.quantum-computing

Got $200? 1 Quantum Computing ETF to Buy Right Now.

If you have $200 and want to point it at something that feels like the future, Defiance Quantum ETF (QTUM +0.36%) is a serious contender instead of a science project. When you buy this ETF, you're buying a basket of companies that are already building, selling, and using the hardware and software that could redefine what "computing power" means over the next couple of decades. ExpandNASDAQ: QTUMDefiance Quantum ETFToday's Change(0.36%) $0.57Current Price$159.25Key Data Points*:nth-last-child(-n+2)]:border-b-0">AUM$5.9BDividend Yield0.74%Expense Ratio0.40%*:nth-last-child(-n+2)]:border-b-0">Top HoldingsARQQ2.06%NET1.59%ESTC1.58% What QTUM owns QTUM tracks the BlueStar Machine Learning and Quantum Computing Index, which sounds abstract until you look at what's inside. The fund holds around 80 to 90 stocks tied to quantum computing and advanced machine learning, with most of the weight in technology names that already ship products and services. You get pure play quantum companies like D-Wave Quantum (QBTS -0.35%), IonQ (IONQ +1.23%), and Rigetti (RGTI -0.05%), which are building different kinds of quantum machines and cloud services, alongside more established names that embed quantum and AI capabilities into chips, data platforms, and security tools. Also, companies in that index generally need to generate at least 50% of their revenue or operating activity from quantum-computing-related products or activities. Owning a single early-stage quantum stock is like betting your $200 on one lab's approach to physics. QTUM lets you spread that bet across multiple hardware and software paths, as well as larger firms that can absorb setbacks and keep funding research. You are buying the ecosystem, not one experiment. ExpandNYSE: IONQIonQToday's Change(1.23%) $0.57Current Price$46.83Key Data Points*:nth-last-child(-n+2)]:border-b-0">Market Cap$18BMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded sh

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Researchers Simulate Fluid Dynamics on Quantum Processorquantum-computing

Researchers Simulate Fluid Dynamics on Quantum Processor

Scientists José Diogo da Costa Jesus and colleagues at the Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, Hamburg D-22761, Germany, and the University of Oxford, have demonstrated a new method realising the time evolution of nonlinear fluid dynamics on a quantum processor. Numerical simulation of nonlinear partial differential equations underpins modern scientific computing, spanning areas from fluid flow and transport to collective dynamics. Extending this capability to quantum computers represents a longstanding challenge because nonlinear and non-Hermitian evolution is fundamentally incompatible with conventional Hamiltonian-based quantum simulation. The difficulty arises because quantum mechanics, at its core, describes systems evolving according to the Schrödinger equation, which is linear and governed by Hermitian operators; representing dissipative or nonlinear forces requires fundamentally different approaches. Quantum simulation accurately models high Reynolds number fluid convection Error rates in reconstructing the time evolution of fluid dynamics dropped to 0.3 per cent for the first timestep, a substantial improvement over previous methods limited by shallower circuits and susceptibility to hardware noise. This reduction in error is critical, as quantum systems are inherently prone to decoherence and gate errors, which rapidly degrade the accuracy of computations. The team achieved this by implementing a hybrid quantum-classical variational framework, directly encoding nonlinear dynamics and circumventing the need for complex linear approximations. Traditional quantum algorithms often rely on mapping the problem onto a Hamiltonian and then evolving it using unitary transformations; however, this approach struggles with nonlinear terms. The variational framework instead uses a parameterised quantum circuit, where the parameters are optimised classically to minimise the difference between the quantum simulation and the desired solution. This

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Researchers Bound Quantum Circuit Complexity with New Witnessesquantum-computing

Researchers Bound Quantum Circuit Complexity with New Witnesses

A new framework has been developed defining quantum circuit architecture witnesses, certifying the incompatibility of a unitary transformation with a specified quantum circuit architecture. Methods for determining the feasibility of implementing quantum operations are primarily constructive, meaning they attempt to build a circuit to achieve a desired transformation. However, these constructive methods generally do not provide rigorous certificates that a unitary cannot be realised using given implementation resources, leaving open the possibility that a circuit might be fundamentally impossible to construct within the constraints of a particular hardware platform. Raphaël Mothe and Otfried Gühne, at the Institute for Scientific Computing and the Technische Universität Hannover respectively, formulate the witness construction as a semidefinite program by maximising the fidelity between the Choi state of the target unitary and those of tested circuits. The resulting witnesses provide practical and quantitative certificates of incompatibility, implying lower bounds on implementation resources such as the number of gates required and the circuit’s depth. Rigorous certification of seven two-qubit gate quantum circuits using incompatibility witnesses For Clifford unitaries, a specific class of unitary transformations crucial in quantum error correction and measurement-based quantum computation, a new framework enables efficient numerical certification for circuits containing approximately seven two-qubit gates, a substantial improvement over previous methods. Clifford unitaries possess the property that they map qubit states to other qubit states, and are fundamental to many quantum algorithms. Prior approaches lacked rigorous proof of impossibility; they could suggest difficulty in implementation but not definitively prove it, leaving a gap in verifying quantum computation feasibility. This work definitively establishes whether a given quantum operation can be performed

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Researchers Concentrate Magic States from Any Unknown Qubitquantum-computing

Researchers Concentrate Magic States from Any Unknown Qubit

Six copies of an input qubit state are both necessary and sufficient to create one exact CCZ state, advancing magic state distillation. Jacopo Rizzo and Lorenzo Leone at Berlin demonstrated this universal magic state concentration using only stabilizer operations, a fixed protocol applicable to any unknown pure qubit state. Previously, such distillation methods required assumptions about the input state or the noise affecting it; now, exact distillation is achievable without prior knowledge. The team achieved universal magic state concentration, a fixed process using only stabilizer operations, transformations that do not introduce new types of quantum errors, to convert several unknown qubit states into a single, precise target state. This process identifies the stabilizer Rényi entropy as a key measure for optimising the creation of these quantum resources, regardless of the initial input state’s properties. This is vital because previous methods often required prior knowledge of the input state or the type of noise affecting it; this new approach works regardless of these factors. The team identified the stabilizer Rényi entropy as a key measure of entanglement during the concentration process. Six-copy CCZ state distillation and universal magic state concentration via stabilizer Rényi entropy An exact distillation of a CCZ state from six input copies has been achieved, a substantial improvement over prior protocols that demanded assumptions about input state or noise. Previously, creating an exact CCZ state from an unknown qubit required specifying the input’s characteristics, limiting practical application. The team refined multiple unknown qubit states into a single, highly accurate target state using only stabilizer operations, regardless of initial conditions. The stabilizer Rényi entropy identifies itself as a fundamental measure for optimising magic state distillation, governing performance up to nine input copies and enabling scalable distillation rates.

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Researchers Simulate Systems with Memory Using Quantum Algorithmsquantum-computing

Researchers Simulate Systems with Memory Using Quantum Algorithms

Until now, quantum algorithms have efficiently simulated Markovian dynamical systems, where a system’s future depends only on its current state. IBM Research has, for the first time, developed quantum algorithms to efficiently simulate non-Markovian systems, where future evolution depends on past history. These algorithms provide an exponential speedup in system size compared to existing classical methods when the strength of the memory term, denoted as M, is less than one. Researchers have created new quantum algorithms that model systems influenced by their past states, a characteristic called non-Markovian dynamics. Previously, quantum algorithms could only efficiently simulate systems where only the present state mattered; this work expands those capabilities to a broader range of complex phenomena. These algorithms efficiently simulate linear Volterra integro-differential equations, which describe systems with ‘memory effects’ that are challenging for standard computers to handle. The significance of this advancement lies in its potential to model a wider array of physical and chemical processes accurately, as many real-world systems exhibit non-Markovian behaviour. Classical simulations of such systems often require immense computational resources, scaling polynomially with system size, making them intractable for all but the simplest cases. Researchers at IBM Research have developed new quantum algorithms capable of simulating systems where the future state depends not only on the present, but also on a ‘memory’ of the past. However, simulating these systems becomes computationally difficult when the memory effect is strong, prompting the researchers to explore techniques for converting complex problems into simpler forms, a process they term Markovianization. The ability to accurately model non-Markovian dynamics is crucial in fields like quantum chemistry, where the interactions between electrons can exhibit memory effects, and in materials science, where t

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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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Bluefors dilution refrigerator market expands with quantum computer scalingquantum-computing

Bluefors dilution refrigerator market expands with quantum computer scaling

Quantum computers aiming for 1,000 qubits will demand between 3,000 and 5,000 individual cryogenic connections, creating a significant challenge and spurring innovation in how signals reach quantum processors. Superconducting quantum computers from companies like IBM, Google, and Rigetti rely on operating temperatures below 10 millikelvin, establishing a critical dependence on dilution refrigerators and their associated cryogenic infrastructure. A new market study details these growth trends, analyzing technologies and companies involved in supplying cryogenic solutions for quantum computing through 2036. The report provides intelligence for those evaluating opportunities in this rapidly expanding segment of quantum technology. Demand for dilution refrigerators is increasing alongside the ambitious scaling plans of superconducting quantum computer developers. This dependence extends beyond simply achieving low temperatures; it also encompasses maintaining the integrity of the quantum states within these processors. This is not just a logistical hurdle, but a catalyst for innovation in high-density cryogenic interconnects and integrated assemblies. Researchers are actively exploring alternative control architectures, including cryogenic CMOS and Single Flux Quantum electronics, to manage this increasing complexity and minimize signal degradation. These approaches aim to move control and readout functions closer to the qubits themselves, reducing the number of physical connections required and improving overall system performance. The report profiles 54 companies, including BlueFors, Oxford Instruments NanoScience, and Delft Circuits, assessing their funding history, technology, and competitive advantages. The report states that “the global cryogenic solutions market for quantum computing represents one of the fastest-growing segments in quantum technology infrastructure.” This expansion is driven not only by the need for more dilution refrigerators but also for speci

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Researchers Bound Eigenstate Filtering Complexity with Logarithmic Factorsquantum-computing

Researchers Bound Eigenstate Filtering Complexity with Logarithmic Factors

Until now, preparing excited or general eigenstates of a quantum system has been a central challenge, particularly when the desired energy level is unknown or the initial state lacks sufficient overlap with the target eigenstate. Po-Wei Huang of the University of Oxford, Bence Bakó of the Wigner Research Centre for Physics, and colleagues have introduced the Dominant Eigenstate Filtering via Eigenprobability Amplification and Thresholding, or DEFEAT, algorithm to address this. Researchers have created a new computational technique, named DEFEAT, which enhances the efficiency of identifying specific energy states within quantum simulations. This algorithm overcomes a significant hurdle in utilising quantum computers to solve complex problems in fields such as chemistry and materials science by isolating the desired states even when limited initial information is available. By minimising the need for extra quantum bits and improving performance with weak signals, DEFEAT facilitates more practical quantum simulations. The researchers have developed a new algorithm, DEFEAT, Dominant Eigenstate Filtering via Eigenprobability Amplification and Thresholding, to improve quantum simulations. Quantum simulation holds immense promise for advancements in chemistry and materials science, but a major obstacle has been efficiently preparing specific energy states, or eigenstates, within a quantum system; imagine these eigenstates as rungs on a ladder, each representing a possible energy level. The team’s innovation lies in a technique called ‘twirling superoperators’, a mathematical ‘blurring’ that averages out unwanted noise and focuses on the essential information about these energy levels, allowing them to construct an eigenprobability matrix. This matrix encodes how strongly an initial state overlaps with each eigenstate, and crucially, the algorithm doesn’t require prior knowledge of the target energy. Logarithmic complexity scaling unlocks efficient eigenstate isolation with

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