Quantum information is so fragile that a stray flicker of light can wreck it. So a team of physicists pushed it through a cable roaring with real internet traffic, just to see what would happen. Picture a strand of glass thinner than a human hair, buried beneath the streets between Evanston and downtown Chicago. Through it roars a flood of ordinary internet traffic: video calls, streaming movies, cloud backups. Each pulse of light contains millions of photons. Now imagine slipping a single, lonely photon instead – one carrying quantum information so delicate that a whisper of stray light can destroy it. Would anything survive the trip? Researchers at Northwestern University set out to answer exactly that. And the result could shape how the quantum internet – long promised, rarely seen outside a lab – actually gets built. Today’s internet stores information as bits, 0s and 1s. A future quantum internet would trade in quantum states instead, including entanglement – a strange link between two particles that holds true no matter how far apart they travel. Entangled photons could one day enable ultra-secure communication, connect quantum computers across cities, and even transfer quantum information without physically carrying it, through a process called teleportation. But there’s a catch, and it’s a big one. Quantum signals ride on single particles of light. Regular internet traffic blasts through fiber with overwhelming force. Study senior author Prem Kumar is a professor of electrical and computer engineering at Northwestern’s McCormick School of Engineering. Kumar directs the Center for Photonic Communication and Computing. Gina Talcott, a graduate student in his group, is the study’s first author. “Quantum signals are very, very tiny compared to classical signals,” said Kumar. “It’s like an ant traveling through a path filled with elephants. Our results show that photons can survive the
Aug 2, 2026 · via earth.com
Researchers have devised a new method for stabilizing magnetic fields in ultracold atom experiments by utilizing the atoms themselves as a magnetometer. The team, including scientists from Vilnius University and the National Institute of Standards and Technology, overcame limitations of conventional sensors, typically positioned several centimeters away from atomic systems, by employing a pair of measurements to determine magnetic field strength directly within the experiment. This procedure, demonstrated with rubidium 87, incorporates a Kalman filter that reduced long-term drift as high as approximately 70 nanotesla per hour, exchanging it for a slight increase in shot-to-shot variability. A technique allows for magnetic field stabilization within ultracold atom experiments, bypassing limitations of conventional sensors. Traditional magnetic field sensors, such as Hall probes, are typically positioned at least several centimeters away from the atomic system due to the magnetic fields they generate and physical limitations of the vacuum apparatus. This direct approach utilizes the ultracold atoms themselves as a magnetometer, employing a pair of measurements to determine the Zeeman splitting, and thus the magnetic field, of rubidium 87. The team developed expressions to quantify the balance between measurement noise, dynamic range, and potential atom loss during the process. This innovative method was demonstrated using partial-transfer absorption imaging, allowing for precise monitoring of the magnetic environment surrounding the atoms. This stabilization was achieved with a minimal increase in shot-to-shot variability, moving from 1.8(2) to 2.0(2) nanotesla. See today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals.
Aug 1, 2026 · via quantumzeitgeist.com
A 54-qubit IQM Emerald quantum device has validated a new optimization framework that achieves competitive solutions using a surprisingly shallow circuit depth. Researchers Elisabeth Wybo and a colleague developed Quantum-Informed Surrogate Sampling, or QISS, which leverages a quantum computer to generate “informative statistics for scalable classical sampling” rather than directly solving problems. The work demonstrates that QISS, using only O(N) low-order correlators from shallow circuits, can outperform the widely studied QAOA algorithm; specifically, on MaxCut problems with 3-regular graphs, QISS from p=3 QAOA correlators outperforms vanilla QAOA at p=17 on average. This approach, validated through experiments, suggests a path toward noise-resilient near-term quantum optimization. This performance is notable because a shallow circuit’s capacity can exceed a much deeper one through effective post-processing. The framework does not directly sample solutions using the quantum computer, but instead generates “informative statistics for scalable classical sampling,” a shift that may prove crucial for near-term quantum optimization. Validation on the IQM Emerald device further confirms QISS’s noise resilience, suggesting a viable path forward for practical quantum optimization strategies. The researchers report that further improvements are possible by using QISS to warm-start QAOA, potentially unlocking even greater performance gains. Researchers are shifting strategies in the pursuit of near-term quantum optimization, focusing on leveraging shallow circuits to enhance classical algorithms instead of relying on direct quantum sampling. The efficiency of QISS stems from its reliance on only O(N) low-order correlators, allowing it to achieve competitive results on problems like MaxCut and Maximum Independent Set. This approach, detailed in their recent paper, supports a model where shallow quantum circuits generate data for classical processing, offering a viable path toward scalable and noise-resilient optimization. Source: https://arxiv.org/abs/2607.22372 See today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals.
Aug 1, 2026 · via quantumzeitgeist.com
SINGAPORE — Tech giant IBM outlined its strategy to help organizations become AI-first enterprises while advancing practical quantum computing during THINK on Tour Singapore on July 21, company executives told Philippine journalists in separate media roundtable discussions at the Marina Bay Sands Expo and Convention Center. The discussions expanded on announcements made during IBM’s annual Asia Pacific flagship conference, where the company unveiled its blueprint for helping organizations adopt artificial intelligence (AI) at scale through trusted AI, hybrid cloud infrastructure and digital sovereignty. The event also featured new AI technologies, enterprise software and strategic collaborations across the region. The first roundtable focused on quantum computing, with IBM Fellow and Chief Technology Officer for IBM Quantum Oliver Dial discussing the industry’s progress toward practical enterprise applications and Southeast Asia’s growing participation in quantum research. Dial said IBM remained on track to demonstrate verified quantum advantage before the end of the year, referring to computing problems in which quantum systems outperform classical computers under conditions that can be independently verified. “We’re still confident we will see verified quantum advantage ... before the end of this year,” Dial said. He said current demonstrations continue to focus on specially designed scientific problems because researchers must be able to verify the correctness of the results independently before they can establish a quantum advantage over conventional computing. Dial also highlighted IBM’s work with the Cleveland Clinic using sample-based quantum diagonalization (SQD), an algorithm developed for molecular simulations. Researchers recently simulated a protein system containing more than 12,000 atoms, producing results that were about 200 times more accurate than previous quantum-centric molecular simulations, although the best classical computing methods continue to outperform quantum systems for such workloads. IBM executives also pointed to Southeast Asia as one of the company’s earliest regions for quantum collaboration. Regional officials said
Aug 1, 2026 · via manilatimes.net
Researchers demonstrated that braiding and fusing particles known as non-Abelian anyons can perform every operation required by a quantum computer. A quantum computer becomes broadly useful only when it can perform any computation rather than a limited set of specialized tasks. Physicists have now demonstrated that unusual quantum objects called non-Abelian anyons can provide that versatility by supporting the full range of operations required for universal quantum computing. Researchers from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), Harvard, Stony Brook University and Quantinuum constructed and tested a complete computational toolkit based on non-Abelian anyons. Their experiments offer the first demonstration that this approach can support universal quantum operations. “We demonstrated a so-called universal gate set—meaning that if you store information in these emergent versions of quarks, and you move them around, you can do any quantum computation you might want to do,” said Ruben Verresen, assistant professor of molecular engineering at UChicago PME and a co-author of the new study published in Nature. The method could support both general-purpose quantum computing and more reliable machines. Quantum computers ordinarily protect information by distributing it across many physical qubits through error correction. However, those codes usually cannot perform every required operation directly on the protected information. Engineers often overcome that limitation with specially prepared resources called “magic states.” Producing them requires a demanding distillation process that can consume a substantial portion of a quantum computer’s available qubits. The findings indicate that non-Abelian anyons may provide a way around that expensive step. “Non-Abelian codes are a dark horse in the race to quantum error correction,” said Henrik Dreyer, managing director and scientific lead at Quantinuum’s Munich office and a co-author of the study. “In this work we show the first universal gate set in a non-Abelian code, which demonstrates
Aug 1, 2026 · via scitechdaily.com
He turned searching into a quantum advantage, giving the field one of its two founding algorithms. Who Lov Grover is Lov Grover is an Indian-American computer scientist whose 1996 quantum search algorithm became one of the defining results of the field. He is remembered above all for a single, deceptively simple idea: that a quantum computer can find a marked item in an unsorted collection far faster than any classical machine can. That idea has echoed through nearly every part of quantum computing since. Born in 1961 in Meerut, India, Grover trained as an electrical engineer before turning his attention to the strange logic of quantum information. His work sits alongside the achievements of researchers like Peter Shor, and the two of them are routinely named as the authors of the quantum algorithms that started everything. Where Shor showed quantum machines could break cryptography, Grover showed they could search. A quiet revolution in search What makes Grover distinctive is that his contribution did not require exotic structure in the problem being solved. Many quantum speedups depend on hidden periodicity or algebraic patterns, and they vanish the moment those patterns are absent. Grover’s result applied to the most generic task imaginable, finding a needle in an unstructured haystack, and that generality is exactly why it has proven so durable. Education and early career Grover earned his bachelor’s degree in electrical engineering from the Indian Institute of Technology Delhi in 1981. From there he crossed to the United States for graduate study, taking master’s degrees in electrical engineering at Caltech and in physics at Stanford. He completed his doctorate in electrical engineering at Stanford in 1984, with a thesis on new concepts in free-electron lasers. His early training was rooted in circuits and engineering rather than in pure physics, and that practical
Aug 1, 2026 · via quantumzeitgeist.com
Rohan N Rajmohan of Northwestern University and colleagues from University of Chicago, Oak Ridge National Laboratory and IBM Quantum have developed a new framework for analyzing quantum noise that moves beyond standard models, revealing that the induced channel depends on which stabilizer eigenspace is chosen as the codespace. The researchers derive a tractable expression for the noise-averaged logical infidelity, accurately modeling error even when noise levels are high. This work reveals that, unlike traditional stochastic Pauli error models, the induced channel is affected by the selected codespace for encoding quantum information. Exploiting this discovery, the team introduces “PROSE” (Protected Stabilizer Eigenspace) encoding, a strategy for actively selecting the optimal codespace to suppress errors, and demonstrates that this eigenspace can be efficiently identified in many relevant situations; the results offer a new, broadly applicable lens on correlated coherent noise in stabilizer codes. Accurately predicting quantum error rates, even with substantial noise, represents a major step forward in building practical quantum computers. Researchers affiliated with the Department of Physics and Astronomy at Northwestern University, the University of Chicago, and IBM Quantum have derived a tractable expression for characterizing how correlated coherent errors impact stabilizer codes, offering a means to assess logical infidelity, a measure of how faithfully quantum information is preserved, without relying on approximations valid only for weak noise. This expression is non-perturbative, remaining accurate even as noise levels increase, a significant improvement over existing models. Demonstrating the practicality of PROSE, the team showed that this eigenspace can be efficiently identified in many relevant situations. Further analysis revealed that noise correlations, often assumed to be detrimental, can actually be harnessed; with the right encoding, even positive correlations reduce the logical infidelity below the uncorrelated baseline. This suggests a potential pathway for mitigating noise by strategically leveraging its characteristics, rather than simply
Aug 1, 2026 · via quantumzeitgeist.com
Solana Foundation CISO: AI and Deepfake Scams Now a Bigger Threat Than Smart Contract Bugs The cryptocurrency industry's biggest security threat is rapidly shifting from technical vulnerabilities in smart contracts to "social engineering attacks" that deceive people, according to a stark warning. As artificial intelligence and deepfake technology become more sophisticated, fake identities and AI-powered scams are surging, making the construction of multi-layered security systems an urgent priority. According to CoinDesk on the 1st, Michael Coates, Chief Information Security Officer at the Solana Foundation, said in an interview, "A significant number of major security incidents in recent months originated not from smart contract vulnerabilities, but from more sophisticated breaches involving fake identities and AI-driven scams." He added, "These types of threats will be the core challenge for blockchain security going forward." Coates previously served as CISO of Twitter and led security at Mozilla during the browser wars. Having joined the Solana Foundation earlier this year, he is responsible for introducing robust security practices across the Solana ecosystem's projects, as well as the foundation's own security, and for discussing cybersecurity standards with regulatory authorities. He emphasized that while cryptocurrency hacks garner attention for large-scale fund theft, they often stem from operational security failures or vulnerabilities in the Web2 domain rather than flaws in the blockchain itself. "Attackers are highly motivated by the ability to irreversibly drain funds and will exploit any mistake," Coates said. "Web3 companies must implement all the security measures that Web2 companies need, with the addition of threats unique to Web3." He repeatedly stressed the dangers of AI-assisted social engineering hacks. Social engineering techniques involve approaching victims to steal critical information such as private keys or seed phrases, rather than technically hacking the blockchain. Prime examples include romance scams, commonly known as "pig butchering" schemes. "With advances in
Aug 1, 2026 · via finance.biggo.com
What is a zero-knowledge proof? Privacy and scaling explained A zero-knowledge proof lets one party prove to another that a statement is true without revealing any information beyond the truth of the statement itself. It is the cryptographic technique behind blockchain privacy, scalable rollups, and a growing number of identity verification systems. - Zero-knowledge proofs allow a prover to convince a verifier that a computation was performed correctly without revealing the underlying data, enabling both privacy and scalability on blockchains. - The two main families of zero-knowledge proofs used in blockchain are zk-SNARKs, which require an initial trusted setup ceremony, and zk-STARKs, which do not require trusted setup but produce larger proofs. - Ethereum layer 2 rollups like zkSync, Scroll, and Polygon zkEVM use zero-knowledge proofs to compress thousands of transactions into a single proof verified on the main chain, reducing gas costs by 90 percent or more. - Vitalik Buterin introduced the GKR protocol in late 2025 as a way to accelerate Ethereum zero-knowledge proof verification, aiming to make the technology practical for everyday use at scale. - Zero-knowledge proofs are mathematically sound but not magic. They depend on specific cryptographic assumptions, require significant computational resources to generate, and have been deployed in production for less than three years at scale. The standard explanation of zero-knowledge proofs uses the cave analogy. Ali Baba knows the secret word to open a door inside a circular cave. He can prove he knows the word by entering from one side and exiting from the other, on demand, without ever saying the word out loud. After enough successful demonstrations, the verifier becomes statistically certain Ali Baba knows the secret. This analogy is correct but incomplete. It captures the intuition but misses the machinery. In practice, zero-knowledge proofs are not about caves or doors.
Aug 1, 2026 · via crypto.news
Australia’s oldest computer could show us how quantum might become a reality Sun 2 Aug 2026 at 5:00am Peter Thorne still regularly thinks about a weekend job he had almost 70 years ago, running a computer the size of a garage. "They needed somebody to turn it on, run the test programs and make sure it worked for people at the weekend," he says. The device Dr Thorne was in charge of — called the CSIRAC — is the fifth automatic digital computer ever built, and the first in Australia. "It was … probably the most complicated electronic machine in Australia at that time," he says. Loading... The computer — parts of which are still on display — is a behemoth. It weighs close to two tonnes, and has the energy requirements of a cul-de-sac worth of homes. Heading inside the belly of the machine, he points out the dozens of vacuum tubes, fans and a large hard drive that once upon a time would spin. "Every component had to be hand-soldered into place," he explains. "It was grey, it was dominating, it was undoubtedly powerful." A complicated machine that could answer complicated questions. In the late 1940s and early 1950s, CSIRAC was in high demand, doing difficult calculations in a few hours that would take a person months of work. But despite an early lead in computing science research, Australia soon took its foot off the accelerator, leaving it to other countries to develop the sector. Today, CSIRAC sits in a museum in Melbourne as a symbol of Australia’s early innovation in classical computer technology. Decades after this homegrown computer was retired, Australia is aiming to once again be a global innovator in the field. Start-ups and governments are making a big bet on a new technology that may
Aug 1, 2026 · via abc.net.au
Superconducting quantum computer developer IQM Quantum Computers (Nasdaq: IQMX) and European rail operator Deutsche Bahn have published joint research demonstrating the execution of a hybrid quantum-classical optimization algorithm on real-world operational railway data. Executed end-to-end on IQM’s Emerald quantum processor, the study addresses the complex challenge of rolling stock planning—assigning physical train units to scheduled trips while minimizing operational costs and adhering to strict maintenance constraints. The collaboration evaluated a real operational dataset provided by Deutsche Bahn’s IT subsidiary, DB Systel, consisting of 190 scheduled trips across five major German cities (Cologne, Munich, Berlin, Frankfurt, and Hamburg) over a two-day planning window. To translate the scheduling problem into a form suitable for quantum execution, IQM mapped the constraints into a Maximum-Weight Independent Set (MWIS) problem on a conflict graph. In this formulation, graph nodes represent feasible, closed train cycles (incorporating mandatory two-hour maintenance stops in Hamburg and a 4,000 km distance cap), while edges connect incompatible cycles that service the same scheduled trip. [ IQM & Deutsche Bahn Hybrid Scheduling Architecture ] Operational Data Input ──► 190 Trips / 5 Cities / 2-Day Timetable │ ▼ Conflict Graph Generation ──► ~98,500 Feasible Train Cycles (MWIS Formulation) │ ▼ Divide-and-Conquer Framework──► Iterative Subgraph Extraction (e.g., k = 20 Nodes) │ ▼ Quantum Execution (IQM QPU)──► QAOA (p = 1) Solves Subgraph MWIS + Pruning │ ▼ Global Graph Update ──► Selected Cycles Removed; Unserviced Trips Re-iterated Because full-scale cycle generation yielded an MWIS graph containing approximately 98,500 feasible cycles—a search space too large for direct processing on present-day QPUs—the researchers engineered a quantum divide-and-conquer framework. The classical outer loop iteratively extracts manageable subgraphs (e.g., 20 nodes) prioritized by passenger-carrying trip density. The quantum subroutine then executes the Quantum Approximate Optimization Algorithm (QAOA) at depth p=1 to select partial solutions. A classical
Aug 1, 2026 · via quantumcomputingreport.com
IQM Quantum Computers Oyj Warrants to purchase American IQMXW Real Time Price USD Recent trades of IQMXW by members of U.S. Congress No Congress Trading data for this ticker Congress Trading Dashboard --- | Name | Type | Shares | Price | Shares Held | Date | Reported | |---|---|---|---|---|---|---| | Investor | Shares | Change in Shares | Market Value | Date | Reported | |---|---|---|---|---|---| | Investor | Type | Shares | Change in Shares | Market Value | Date | Reported | |---|---|---|---|---|---|---| Recently reported changes in IQMXW holdings by institutional investors No Whale Activity for this ticker Institutional Holdings Dashboard Quarterly net insider trading by IQMXW's directors and management No recent Insider Trading for this ticker Insider Trading Dashboard * Insider trading data parsed from SEC Form 4 filings by Quiver Quantitative. Sign up for the Quiver API for real-time access. - 1M - 3M - 6M - YTD - 1Y - 2Y - 5Y - MAX About Key Metrics Return (1d) Return (30d) Return (1Y) CAGR (Total) Max Drawdown Beta Alpha Sharpe Ratio Win Rate Average Win Average Loss Annual Volatility Annual Std Dev Information Ratio Treynor Ratio Total Trades Metrics Definitions Disclaimer: The performance results shown are based on historical backtesting and are hypothetical in nature. Backtested performance does not represent actual trading and does not account for all market factors that may affect execution, such as liquidity, slippage, and changing market conditions. Past performance is not necessarily indicative of future results. There is no guarantee that any trading strategy will be profitable or avoid losses. - AlphaMeasures a portfolio's risk-adjusted performance against that of its benchmark Learn More about Alpha - Annual Standard DeviationMeasures how much the portfolio's total return varies from its mean or average. Learn More about Annual Standard Deviation -
Aug 1, 2026 · via quiverquant.com
Quantum computing stocks have not been spared in the recent tech sell-off. While the technology holds promise to be the next big breakthrough after AI, it is still very much in its infancy. Various techniques and companies are vying to crack the quantum code, and while there is no guarantee which one will come out on top, some certainly look better positioned than others. Let's look at one quantum computing stock to buy, one to sell, and one to hold on this recent dip. IonQ If there were one quantum computing stock I'd buy and tuck away over the next decade, it would be IonQ (IONQ +1.87%). One of the biggest obstacles the industry faces today is that quantum computers are currently very error-prone. Because quantum computing uses qubits, which are in a state of superposition, instead of traditional bits, they are very sensitive to failing due to outside forces such as vibrations or temperature changes. While the industry still needs to make big strides, IonQ is currently the accuracy leader, achieving 99.99% 2-qubit gate fidelity. NYSE: IONQ Key Data Points IonQ's accuracy lead can be largely attributed to its trapped-ion approach. Instead of using fabricated qubits, it starts by using actual charged atoms (ions), which are identical in nature and less fragile. While traditional trapped-ion systems use electric fields to suspend the ions and rely on complex laser setups to control and entangle them, IonQ embeds microwave antennas directly into its chips to manipulate the qubits electronically, dramatically improving stability. This on-chip technique also reduces the system's size, which will be important later when the company looks to commercialize and scale quantum computers. In addition to its accuracy edge, IonQ is also looking to control the entire quantum ecosystem. It has made acquisitions to get into the areas of
Aug 1, 2026 · via fool.com
Rigetti Computing (NASDAQ:RGTI) has cooled hard from its highs, trading at $14.86 after a 32.9% year-to-date decline, yet the stock is up 4.9% over one year and 53.0% over five years. With a market cap around $4.9 billion, $569 million in cash and zero debt, a proprietary chiplet architecture, in-house Fab-1, and integrations with hyperscaler clouds, Rigetti is a rare full-stack superconducting quantum asset. No deal talks have been reported, so the following is a strategic thought exercise. CEO Subodh Kulkarni framed the case for the platform on the Q1 call: “We believe Cepheus-1-108Q is one of the most powerful generally available gate-based quantum computers in the world, and as the largest modular system on the market today, it is an important validation of our chiplet-based architecture in a production setting.” Q1 2026 revenue nearly tripled to $4.4 million. Ranking the Plausible Buyers 5. Alphabet (NASDAQ: GOOGL | GOOGL Price Prediction). This is the longest shot. Google runs its own Willow superconducting program, and Alphabet shares posted 69.8% one-year gains. Its strong not-invented-here culture makes a Rigetti tuck-in unlikely. 4. Nvidia (NASDAQ:NVDA). With a $4.7 trillion market cap and CUDA-Q anchoring the hybrid stack, Nvidia has the balance sheet. CEO Jensen Huang has favored partnering with QPU makers over owning them. 3. IonQ (NYSE:IONQ). This is the sector’s aggressive consolidator, fresh off a $1.8 billion SkyWater acquisition. The modality is different (trapped ion), but CEO Niccolo de Masi has been buying scale. This would be a merger of pure-plays rather than a strategic tuck-in. 2. Amazon (NASDAQ:AMZN). AWS Braket already hosts Rigetti. AWS grew 36.7% in Q2, its fastest in 18 quarters, and Amazon has the financial firepower. The catch is that AWS is also building its own quantum hardware. 1. Microsoft (NASDAQ:MSFT). This is the cleanest fit. Azure Quantum
Jul 31, 2026 · via 247wallst.com
IBM Predicts Quantum Computing Revenue Impact by 2028-2029 IBM expects quantum computing to impact revenue by 2028. IBM CEO Arvind Krishna told CNBC that investments in quantum computing will begin to significantly impact the company’s revenue and profits by 2028 or 2029. The executive also estimated the potential value created by the technology by the end of the 2030s at $1 trillion. On June 30, IBM, in collaboration with Qedma and Algorithmiq, unveiled research results that the company described as demonstrating quantum advantage. They claim that a quantum computer can solve certain computational tasks more efficiently than leading classical systems and verify the results. Krishna noted that IBM’s system helped identify material behaviors that researchers could not observe with conventional computing. He linked potential applications to batteries, new materials, fusion energy solutions, and pharmaceuticals. The IBM chief acknowledged that the pace of commercializing the technology remains contentious due to high error rates, hardware complexity, and scaling issues. However, he stated that the company is already seeing progress toward practical application. In May, IBM announced plans to establish a separate quantum chip manufacturing facility. The project includes $1 billion in support from the U.S. Department of Commerce under the CHIPS program and an additional $1 billion investment from the company itself. Krishna also commented on the market’s weak reaction to IBM’s recent earnings report. According to CNBC, on July 14, the company’s shares fell by 25% following the release of quarterly results, then rebounded by only 2%. IBM reported that some clients had postponed capital projects. Krishna stated that the deals were not canceled but delayed, with about 40% closing within three to four weeks. Earlier, in May, IBM Quantum’s Global Sales Director Petra Florisun declared the beginning of the practical era of quantum computing.
Jul 31, 2026 · via forklog.com
IBM: Three Demonstrations Prove Quantum Advantage Has Been Reached There are a number of ways to measure the growing maturity of quantum computing, from qubit counts – both physical and logical – and fault tolerant thresholds to the speed in terms of CLOPS (circuit layer operations per second) and reliable operations executed with QuOps, or quantum operations. Then there is quantum advantage. There are slight differences in the definition depending on who you’re talking to, but the gist is that quantum advantage occurs when a quantum system can solve a practical and real-world problem more quickly, cheaper, or more accurately than a classical supercomputer. Some vendors have claimed to have reached quantum advantage – from Google’s announcement last year of its Google Echoes Algorithm running on its Willow chip to quantum infrastructure software maker Q-CTRL in May saying it “achieved evidence of practical quantum advantage” in material science running its software on IBM’s Quantum Platform – but as seen here, there is plenty of debate within the scientific community whether quantum advantage actually has been reached just yet. There also have been claims of quantum supremacy, including Google’s assertion in 2019 regarding its 53-qubit Sycamore quantum processor and D-Wave last year touting a version of its Advantage 2 quantum annealing system, which also has been challenged. The usefulness of the problem solved is the difference between advantage and supremacy. For quantum advantage, the problem needs to be practical and real-world; with supremacy, it’s any task that can be done faster by a quantum system than classical computer, even if the job itself is useless IBM and quantum startup Pasqal last year laid out what they said are the requirements that need to be met to declare quantum advantage. Big Blue also has its Quantum Advantage Tracker, a platform-agnostic framework for
Jul 31, 2026 · via nextplatform.com
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Jul 31, 2026 · via youtube.com
IBM Predicts Quantum Computing’s Impact on Earnings by 2029 IBM anticipates a tangible financial return from quantum computing within the next few years, a swift timeline given the technology’s developmental stage. CEO Arvind Krishna stated, “I think that in 2028 or 2029, you’ll see it have a measurable impact on our top line and bottom line,” during a recent appearance on CNBC’s “Mad Money.” This prediction positions IBM as more optimistic about near-term commercial viability than other major players investing in the field, such as Alphabet and Rigetti Computing. The company’s confidence stems from recent demonstrations of quantum advantage, where its systems outperformed classical computers on specific tasks. Recent collaborative research with startup Algorithmiq revealed a quantum computer’s ability to solve complex computational problems with greater efficiency and verified those results, a crucial step beyond theoretical potential. Krishna explained that IBM’s quantum computer identified behaviors in materials previously undetectable using conventional computing methods; these insights could accelerate advancements in areas like battery technology, materials science, fusion energy, and pharmaceutical development. He further clarified that “A quantum computer can do things better, faster, cheaper, in a way that normal classical computers cannot do at this time.” This capability, according to IBM, justifies a substantial $1 billion investment in a dedicated quantum chip foundry, matched by a commitment from the U.S. Department of Commerce through the CHIPS incentive. Looking further ahead, Krishna envisions a significantly larger economic impact. Despite recent market fluctuations following IBM’s earnings report, shares dropped 25 percent on July 14th before partially recovering, Krishna sought to reassure investors that delayed capital spending projects were postponements, not cancellations. He reported that approximately 40 percent of those delayed projects had already been finalized within three to four weeks, signaling continued demand. This positive outlook suggests IBM remains committed to quantum computing
Jul 31, 2026 · via quantumzeitgeist.com
IBM CEO Arvind Krishna said Thursday that investors will not have to wait much longer for quantum computing to become a meaningful business for the company. “I think that in 2028 or 2029, you’ll see it have a measurable impact on our top line and bottom line,” Krishna said on CNBC’s Mad Money. “By the end of the 2030s, we are now pretty convinced this is a trillion dollars of value.” Krishna’s comments came as IBM and startup AlgorithmQ unveiled new research describing what they called a quantum computing advantage. “A quantum computer can do things better, faster, cheaper, in a way that normal classical computers cannot do at this time,” Krishna said. The companies said a quantum computer can solve certain computational problems more efficiently than today’s most advanced classical computers while also verifying the results. Krishna said IBM’s quantum computers have uncovered behaviors in materials that researchers were unable to observe using conventional computing. He said those insights could eventually lead to better batteries, more advanced materials, improved fusion energy, and smarter medicines. READ: IBM launches AI agents for its Power servers following worst stock day (July 16, 2026) “A quantum computer can do things better, faster, cheaper, in a way that normal classical computers cannot do at this time,” Krishna said. While some have shown skepticism about quantum computing being of practical use in the near future, Krishna said IBM is already seeing meaningful progress toward real-world applications. Earlier this year, the Trump administration announced plans to take $2 billion in equity stakes across nine quantum computing companies, including a new IBM venture, according to Reuters. The initiative is part of a broader effort to strengthen the domestic supply chain and counter China’s advances in critical technologies. The U.S. Department of Commerce said IBM would receive $1
Jul 31, 2026 · via americanbazaaronline.com