MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum Machine Learning
SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the 'Company' or 'MicroAlgo') (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This technology significantly reduces the complexity of parameter updates during training through deep optimization of the core circuit, markedly improving computational efficiency. Compared to other quantum classifiers, this optimized model has lower complexity and incorporates advanced regularization techniques, effectively preventing model overfitting and enhancing the classifier's generalization capability. The introduction of this technology marks a significant step forward in the practical application of quantum machine learning.
Traditional quantum classifiers can theoretically leverage the advantages of quantum computing to accelerate machine learning tasks, but they still face numerous challenges in practical applications. Firstly, current mainstream quantum classifiers often require deep quantum circuits to achieve efficient feature mapping, which results in high optimization complexity for quantum parameters during training. Additionally, as the volume of training data increases, the computational load for parameter updates grows rapidly, leading to prolonged training times and impacting the model's practicality.
MicroAlgo's classifier auto-optimization technology significantly reduces computational complexity through deep optimization of the core circuit. This approach improves upon two key aspects: circuit design and optimization algorithms. In terms of circuit design, the technology adopts a streamlined quantum circuit structure, reducing the number of quantum gates and thereby lowering the consumption of computational resources. On the optimization algorithm front, this classifier auto-optimization model employs an innovative parameter update strategy, making parameter adjustments more efficient and substantially accelerating training speed.
In the training process of classifiers based on variational quantum algorithms (VQA), parameter optimization is one of the most critical steps. Generally, VQA classifiers rely on Parameterized Quantum Circuits (PQC), where updating each parameter requires computing gradients to adjust the circuit structure and minimize the loss function. However, the deeper the quantum circuit, the more complex the parameter space becomes, requiring optimization algorithms to perform more iterations to achieve convergence. Furthermore, uncertainties and noise in quantum measurements can also affect the training process, making it difficult for the model to optimize stably.
Traditional optimization methods often employ strategies such as Stochastic Gradient Descent (SGD) or Variational Quantum Natural Gradient (VQNG) to find optimal parameters. However, these methods still face challenges such as high computational complexity, slow convergence rates, and a tendency to get trapped in local optima. Therefore, reducing the computational burden of parameter updates and improving training stability have become key factors in enhancing the performance of VQA classifiers.
MicroAlgo's classifier auto-optimization technology, based on variational quantum algorithms, significantly reduces the computational complexity of parameter updates through deep optimization of the core circuit. It also incorporates innovative regularization techniques to enhance the stability and generalization capability of the training process. The core breakthroughs of this technology include the following aspects:
Depth Optimization of Quantum Circuits to Reduce Computational Complexity: In traditional VQA classifier designs, the number of layers in the quantum circuit directly impacts computational complexity. To lower computational costs, MicroAlgo employs an Adaptive Circuit Pruning (ACP) method during optimization. This approach dynamically adjusts the circuit structure, eliminating redundant parameters while preserving the classifier's expressive power. As a result, the number of parameters required during training is significantly reduced, leading to a substantial decrease in computational complexity.
Hamiltonian Transformation Optimization (HTO): Additionally, MicroAlgo introduces an optimization method based on Hamiltonian transformations. By altering the Hamiltonian representation of the variational quantum circuit, this technique shortens the search path within the parameter space, thereby improving optimization efficiency. Experimental results demonstrate that this method can reduce computational complexity by at least an order of magnitude while maintaining classification accuracy.
Novel Regularization Strategy to Enhance Training Stability and Generalization Capability: In classical machine learning, regularization methods are widely used to prevent model overfitting. In the realm of quantum machine learning, MicroAlgo introduces a novel quantum regularization strategy called Quantum Entanglement Regularization (QER). This method dynamically adjusts the strength of quantum entanglement during training, preventing the model from overfitting the training data and thereby improving the classifier's generalization ability on unseen data.
Additionally, an optimization strategy based on the Energy Landscape is incorporated, which adjusts the shape of the loss function during training. This enables the optimization algorithm to more quickly identify the global optimum, reducing the impact of local optima.
Enhanced Noise Robustness for Real Quantum Computing Environments: Given that current Noisy Intermediate-Scale Quantum (NISQ) devices still exhibit significant noise levels, a model's noise resilience is critical. To improve the classifier's robustness, MicroAlgo proposes a technique based on Variational Quantum Error Correction (VQEC). This method actively learns noise patterns during training and adjusts circuit parameters to mitigate noise effects. This strategy markedly enhances the classifier's stability in noisy environments, making its performance on real quantum devices more reliable.
MicroAlgo's classifier auto-optimization technology, based on variational quantum algorithms, successfully reduces the computational complexity of parameter updates through deep optimization of the core circuit and the introduction of novel regularization methods. This approach significantly boosts training speed and generalization capability. This breakthrough technology not only demonstrates its effectiveness in theory but also exhibits superior performance in simulation experiments, laying a crucial foundation for the advancement of quantum machine learning.
As quantum computing hardware continues to advance, this technology will further expand its application domains in the future, accelerating the practical implementation of quantum intelligent computing and propelling quantum computing into a new stage of real-world utility. In an era where quantum computing and artificial intelligence converge, this innovation will undoubtedly serve as a significant milestone in advancing the frontiers of technology.
About MicroAlgo Inc.
MicroAlgo Inc. (the 'MicroAlgo'), a Cayman Islands exempted company, is dedicated to the development and application of bespoke central processing algorithms. MicroAlgo provides comprehensive solutions to customers by integrating central processing algorithms with software or hardware, or both, thereby helping them to increase the number of customers, improve end-user satisfaction, achieve direct cost savings, reduce power consumption, and achieve technical goals. The range of MicroAlgo's services includes algorithm optimization, accelerating computing power without the need for hardware upgrades, lightweight data processing, and data intelligence services. MicroAlgo's ability to efficiently deliver software and hardware optimization to customers through bespoke central processing algorithms serves as a driving force for MicroAlgo's long-term development.
Forward-Looking Statements
This press release contains statements that may constitute 'forward-looking statements.' Forward-looking statements are subject to numerous conditions, many of which are beyond the control of MicroAlgo, including those set forth in the Risk Factors section of MicroAlgo's periodic reports on Forms 10-K and 8-K filed with the SEC. Copies are available on the SEC's website, www.sec.gov. Words such as 'expect,' 'estimate,' 'project,' 'budget,' 'forecast,' 'anticipate,' 'intend,' 'plan,' 'may,' 'will,' 'could,' 'should,' 'believes,' 'predicts,' 'potential,' 'continue,' and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, without limitation, MicroAlgo's expectations with respect to future performance and anticipated financial impacts of the business transaction.
MicroAlgo undertakes no obligation to update these statements for revisions or changes after the date of this release, except as may be required by law.
View original content: https://www.prnewswire.com/news-releases/microalgo-inc-develops-classifier-auto-optimization-technology-based-on-variational-quantum-algorithms-accelerating-the-advancement-of-quantum-machine-learning-302445099.html
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Economic data: Chicago Fed activity index (February); S&P Global US Manufacturing PMI (March preliminary); S&P Global US services PMI (March preliminary); S&P Global US Composite PMI (March preliminary) Earnings: FactSet (FDS), KB Home (KBH) Here are some of the biggest stories you may have missed overnight and early this morning: Trump just made the Fed's rate call even more complicated Opinion: Trump wages 2 wars — one with trade partners, one with Iran Why Iran could hold off blocking the Strait of Hormuz Oil erases spike in gains in wait for Iran's response Morgan Stanley: Geopolitical selloffs tend to fade fast Analysts react as markets brace for Iran's next move Dollar advances as investors brace for Iran response to US attacks BNY Mellon approached Northern Trust for merger: WSJ Here are some top stocks trending on Yahoo Finance in premarket trading: Tesla (TSLA) stock rose over 1% in premarket trading after rolling out its driverless taxi service to riders on Sunday. The debut of the robotaxi was introduced to a handful of riders, which included retail investors and social-media influencers in Tesla's hometown of Austin. Wolfspeed (WOLF) stock fell 11% in premarket trading on Monday after announcing it plans to file for bankruptcy in the US under a new restructuring agreement with its creditors. The agreement would provide fresh financing and slash debt by nearly 70%. Northern Trust Corporation (NTRS) shares rose 4% before the bell after a report from The Wall Street Journal said that Bank of New York Mellon Corp had reached out to the asset and wealth manager and expressed interest in a merger. Most investors will awaken today searching online for "Strait of Hormuz" after the weekend attacks from the US on Iran. For speed of analysis purposes, if this key oil shipping hub closes down (seems like it won't happen, based on everything I am seeing this morning), it could really send oil (CL=F, BZ=F) prices skyrocketing. Here's what Goldman's team estimates: "If oil flows through the Strait of Hormuz were to drop by 50% for one month and then were to remain down 10% for another 11 months, we estimate that Brent would briefly jump to a peak of around $110." Read more here on Goldman's scenarios. Gold pushed higher with the world in limbo as the US joined Israel's attack on Iran over the weekend. No formal response has been issued by Iran, with wider fallout expected. Spot gold climbed 0.2% to $3,375.04 an ounce taking it to within $125 of its record high as investors sought safe-haven assets in a tumultuous economic situation. Gold then sank 0.5% despite broader haven demand. Bloomberg reports: Read more here. Wall Street is closely watching escalating tensions in the Middle East after President Trump confirmed that the US launched a surprise strike on Iran's nuclear sites late Saturday, marking the country's official entry into the two-week-old conflict. Markets have held mostly steady in the aftermath of the escalation, although US stock futures fell across the board when trading opened Sunday evening. Additionally, bitcoin (BTC-USD) prices, often viewed as a barometer of risk appetite, dropped over 1.6% to trade around $100,500 a coin. WTI crude (CL=F) and Brent (BZ=F) futures jumped, trading near $76 and $79 a barrel, respectively, as uncertainty looms over the potential closure of the critical Strait of Hormuz despite ongoing threats from Iran. The latest surge follows oil's third consecutive week of gains on Friday. "We wouldn't be surprised to see this spark a risk-off reaction in US equities and will be watching the futures closely on Sunday evening and Monday morning," Lori Calvasina, head of US equity strategy research at RBC Capital Markets, wrote in a Sunday evening note to clients. "It has been and remains our belief that the longer and broader the conflict becomes, the more challenging it could be for US equities," Calvasina added. "These escalations come at a tricky time for US equities, as the S&P 500 has looked fairly valued to us (perhaps a bit overvalued) from a fundamental perspective, with more room to run from a sentiment perspective." The analyst said her three main concerns include: first, the risk that rising national security uncertainty could weigh on equity valuations; second, the possibility that renewed geopolitical tensions could stall the recovery in sentiment that began after the early April tariff lows; and third, the potential for a spike in oil prices, which could fuel inflation concerns. In terms of sectors, Energy (XLE) tends to outperform when oil prices rise, while Consumer Discretionary (XLY) and Communication Services (XLC), along with Entertainment, Media, and Interactive Media, tend to lag behind the broader market, Calvasina noted. Citi analyst Stuart Kaiser agreed that sharply higher oil prices remain "the channel for geopolitical risks to impact stock markets," identifying crude prices "well above $80 a barrel" as a critical threshold for concern. Kaiser added that options markets are now pricing in a 10% chance that oil surges 20% over the next month, up from just 2.5% two weeks ago, reflecting mounting tail risks as the conflict deepens. Still, the analyst pointed to resiliency in stocks amid the volatility, saying, "Markets powered through extreme oil volatility and unstable geopolitical headlines to post a risk-on week."
Associated Press
32 minutes ago
- Associated Press
Deadline Alert: Krispy Kreme, Inc. (DNUT) Investors Who Lost Money Urged To Contact Glancy Prongay & Murray LLP About Securities Fraud Lawsuit
LOS ANGELES, June 23, 2025 (GLOBE NEWSWIRE) -- Glancy Prongay & Murray LLP reminds investors of the upcoming July 15, 2025 deadline to file a lead plaintiff motion in the class action filed on behalf of investors who purchased or otherwise acquired Krispy Kreme, Inc. ('Krispy Kreme' or the 'Company') (NASDAQ: DNUT ) securities between February 25, 2025 and May 7, 2025, inclusive (the 'Class Period'). IF YOU SUFFERED A LOSS ON YOUR KRISPY KREME INVESTMENTS, CLICK HERE TO INQUIRE ABOUT POTENTIALLY PURSUING CLAIMS TO RECOVER YOUR LOSS UNDER THE FEDERAL SECURITIES LAWS. What Happened? On May 8, 2025, before the market opened, Krispy Kreme released its first quarter 2025 financial results, reporting its 'net revenue was $375.2 million…a decline of 15.3%' and a 'net loss of $33.4 million, compared to prior year net loss of $6.7 million.' Additionally, the Company announced that it is 'reassessing [its] deployment schedule together with McDonald's' and 'withdrawing [its] prior full year outlook and not updating it' due in part to 'uncertainty around the McDonald's deployment schedule.' On this news, the price of Krispy Kreme shares fell 24.71%, or $1.07 per share, to close at $3.26 per share on May 8, 2025, on unusually heavy trading volume. What Is The Lawsuit About? The complaint filed in this class action alleges that throughout the Class Period, Defendants made materially false and/or misleading statements, as well as failed to disclose material adverse facts about the Company's business, operations, and prospects. Specifically, Defendants failed to disclose to investors: (1) that demand for Krispy Kreme products declined materially at McDonald's locations after the initial marketing launch; (2) that demand at McDonald's locations was a driver of declining average sales per door per week; (3) that the partnership with McDonald's was not profitable; (4) that the foregoing posed a substantial risk to maintaining the partnership with McDonald's; (5) that, as a result, the Company would pause expansion into new McDonald's locations; and (6) that, as a result of the foregoing, Defendants' positive statements about the Company's business, operations, and prospects were materially misleading and/or lacked a reasonable basis. If you purchased or otherwise acquired Krispy Kreme securities during the Class Period, you may move the Court no later than July 15, 2025 to request appointment as lead plaintiff in this putative class action lawsuit. Contact Us To Participate or Learn More: If you wish to learn more about this action, or if you have any questions concerning this announcement or your rights or interests with respect to these matters, please contact us: Charles Linehan, Esq., Glancy Prongay & Murray LLP, 1925 Century Park East, Suite 2100, Los Angeles California 90067 Email: [email protected] Telephone: 310-201-9150, Toll-Free: 888-773-9224 Visit our website at Follow us for updates on LinkedIn, Twitter, or Facebook. If you inquire by email, please include your mailing address, telephone number and number of shares purchased. To be a member of the class action you need not take any action at this time; you may retain counsel of your choice or take no action and remain an absent member of the class action. This press release may be considered Attorney Advertising in some jurisdictions under the applicable law and ethical rules. Contact Us: Glancy Prongay & Murray LLP, 1925 Century Park East, Suite 2100 Los Angeles, CA 90067 Charles Linehan Email: [email protected] Telephone: 310-201-9150 Toll-Free: 888-773-9224 Visit our website at:
Yahoo
42 minutes ago
- Yahoo
I Predict This "Magnificent Seven" Stock Will Crush Expectations
Nvidia has grown like a weed over the past decade. It continues to dominate the booming AI chip market. It still looks reasonably valued relative to its growth potential. 10 stocks we like better than Nvidia › The "Magnificent Seven" stocks -- Apple, Amazon, Meta Platforms, Alphabet, Microsoft, Nvidia (NASDAQ: NVDA), and Tesla -- have all generated impressive returns for their long-term investors. All seven stocks are included in the S&P 500 and Nasdaq-100 indexes, and they often drive the market's performance with their size, growth, and influence. But after years of big gains, most of the Magnificent Seven stocks are losing their luster. Apple is still overwhelmingly dependent on the iPhone, which is vulnerable to high tariffs, competition, and supply chain constraints. Amazon faces stiff competition from cheaper cross-border competitors, Alphabet's Google is being slammed by antitrust regulators and struggling to keep pace with OpenAI's ChatGPT and other generative artificial intelligence (AI) services, and Microsoft might be headed for an ugly breakup with OpenAI as the AI start-up openly rebels against its top investor. As for Tesla, Elon Musk's divisive actions are likely driving away its potential customers in the saturated EV market. Meta and Nvidia face fewer near-term headwinds than the other five stocks. However, Meta generates nearly all of its revenue from ads, and its growth could slow down as the global economy cools off. Nvidia certainly isn't immune to economic downturns and recessions, but the secular expansion of its AI chipmaking business could offset a lot of that pressure. So looking ahead, I believe Nvidia will continue to shatter Wall Street's expectations and outperform the other Magnificent Seven stocks for the foreseeable future. Nvidia is the world's largest producer of discrete GPUs. It designs its own chips, but it outsources its manufacturing to third-party foundries like Taiwan Semiconductor Manufacturing. With that "fabless" model, Nvidia doesn't need to spend billions of dollars to upgrade its own foundries or develop the smallest, densest, and most power-efficient manufacturing nodes. Nvidia once generated most of its revenue from its gaming GPUs, which can also be used to mine certain cryptocurrencies. But in its latest quarter, it generated less than 9% of its revenue from its gaming GPUs. A whopping 89% came from its data center GPUs -- which include its older A100 chips and current-gen H100 and H200 chips. Nvidia launched its first data center GPUs back in 2008. Unlike traditional CPUs, which only process a single piece of data at a time through scalar processing, GPUs use vector processing to process a broad range of integers and floating-point numbers simultaneously. That makes them better suited for processing complex AI tasks than stand-alone CPUs. Nvidia's sales of data center GPUs rose from 2016 to 2022 as the cloud and AI markets expanded, but they didn't explode until 2023 (fiscal 2024) -- when OpenAI's launch of ChatGPT in late 2022 sparked a global AI infrastructure race. Here's how rapidly that growth engine expanded from fiscal 2022 to fiscal 2025 (which ended this January). Metric FY 2022 FY 2023 FY 2024 FY 2025 Data center revenue growth 58% 41% 217% 142% Data center as percentage of total revenue 39% 56% 78% 88% Total revenue growth 61% 0% 126% 114% Data source: Nvidia. From fiscal 2025 to fiscal 2028, analysts expect Nvidia's revenue and earnings per share to grow at compound annual growth rates of 30% and 28%, respectively, as the AI market continues to expand. Its stock still looks reasonably valued relative to those estimates at 36 times forward earnings. But those estimates could be too conservative -- since Nvidia has comfortably beat Wall Street's top- and bottom-line expectations for nine consecutive quarters. So as the top seller of the picks and shovels for the AI gold rush, Nvidia's revenues and profits should keep crushing analysts' expectations. Nvidia controls about 98% of the data center GPU market, according to TechInsights. It locks in those customers with its proprietary Compute Unified Device Architecture (CUDA) programming platform. When developers write their AI applications on CUDA, they become optimized for Nvidia's GPUs and can only be executed on its chips. If they want to run that same application on another GPU, they need to be rewritten in other frameworks. That stickiness widens its moat against AMD and other challengers. Nvidia's sales in China are being throttled by the U.S. export curbs, but it can easily offset that pressure with its stronger chip sales in other markets. It can also keep selling less powerful variants of its flagship chips (like its modified H20 chip) to its Chinese customers. Simply put, Nvidia has plenty of ways to keep growing. It's already had a great run over the past decade, but it has an easy path toward outperforming the market and its Magnificent Seven peers. Before you buy stock in Nvidia, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Nvidia wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $664,089!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $881,731!* Now, it's worth noting Stock Advisor's total average return is 994% — a market-crushing outperformance compared to 172% for the S&P 500. Don't miss out on the latest top 10 list, available when you join . See the 10 stocks » *Stock Advisor returns as of June 23, 2025 John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Leo Sun has positions in Amazon, Apple, and Meta Platforms. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Apple, Meta Platforms, Microsoft, Nvidia, Taiwan Semiconductor Manufacturing, and Tesla. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy. I Predict This "Magnificent Seven" Stock Will Crush Expectations was originally published by The Motley Fool Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
