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It is a year when scientists of Indian origin are making world headlines, and the win of Eshan Chattopadhyay in the 2025 Godel Prize is a remarkable achievement. This is an impressive scientific story and a major accomplishment of the theoretical computer science field as well as in the bigger context of the Indian academic world in general. This IIT-Kanpur alumnus solved a 30-year-old problem and bagged the prestigious Godel Prize for his 2016 paper, 'Explicit Two-Source Extractors and Resilient Functions'. 

Here is everything you need to know about this accomplishment, how it is significant, and what it represents to students, UPSC aspirants, and anyone who looks out to see what Indian science is doing in the international arena that is leading India’s path to be Vishwaguru once again.

Who is Eshan Chattopadhyay?

Eshan Chattopadhyay is an Associate Professor of Computer Science at Cornell University, and a graduate of IIT Kanpur, the name that echoes with every Indian engineering student. He graduated with his PhD degree in 2016 at the University of Texas at Austin, after graduating with BTech in IIT Kanpur in 2011. His scholastic experience extends to post-doctoral associateships at the Institute for Advanced Study, Princeton, the Simons Institute for the Theory of Computing at UC Berkeley. The work of Chattopadhyay revolves around pseudorandomness, circuit complexity and communication complexity, which are vital to the foundation of modern computing.  

This Godel Price is not the only award won by Eshan; he has also won NSF Computer and Information Science and Engineering Research Initiation Initiative award in 2019, NSF CAREER award in 2021, and in 2023 the Sloan Research Fellowship.

What is the Godel Prize and Why is it So Prestigious?

The Godel Price, named in honor of legendary logician Kurt Godel, is one of the most prestigious awards in theoretical computer science. It is presented jointly by the European Association for Theoretical Computer Science (EATCS) and ACM SIGACT, and it honours breakthrough work that establishes a foundation of knowledge by advancing the field.

Godel prize is an annual award with $5,000 prize money. To be eligible for this prize, one must have a paper published within the last 14 years. Past winners include giants whose work has shaped cryptography, algorithms, and complexity theory.  

After winning the prestigious award, Eshan Chattopadhyay said, “This recognition is truly an incredible honour. The Gödel Prize has celebrated some of the most beautiful and foundational work in our field. It feels surreal and deeply gratifying that our paper is being placed in that category.”

The Winning Work: solving a 30-year old puzzle

Chattopadhyay, together with his PhD advisor David Zuckerman, was awarded the 2025 Godel Prize  for the paper they published in the year 2016 titled, “Explicit Two-Source Extractors and Resilient Functions”. This academic paper meant more than a research work because it actually solved a problem that researchers had been struggling to solve for almost thirty years!

The Problem: 

The issue of randomness is everything in computer science and cryptography. However, the real world does not offer as much randomness. The majority of sources, such as hardware noise or the weak input of the user, which is to say they are not actually random. Here was the issue: How can two weak sources be combined to produce strong, reliable randomness?

The Breakthrough: 

They were the ones who showed for the first time how to construct an explicit two-source extractor that works even when both sources have a bit of randomness; technically, just polylogarithmic min-entropy. This is new because earlier every method that has been known with regards to this, required each source to be nearly half-random, which is a big amount. Not only did their technique completely resolve the randomness extraction problem, it also provided new avenues in the fields of complexity theory, cryptography and the construction of resilient Boolean functions.

Why Should This Matter?

1. Cybersecurity: Secure encryption, digital signatures and safe online transactions rely on reliable randomness.
2. Distributed Computing: Randomness aids in constructing systems that are fault-tolerant and can construct robust communication protocols.
3. Mathematics: These methods produced better explicit Ramsey graph constructions, an important combinatorial and theoretical computer science problem. 

Why is this a Special Win to India?

This success proves Chattopadhyay is an example of world-leading talent that can be produced by Indian institutions such as IIT Kanpur. It is a moment of pride among the Indian diaspora that demonstrates that the researchers of Indian-origin are not only subjects of the world's scientific progress but are heads of it. This has been called a “shining milestone” by the IIT Kanpur alumni community.

Takeaway For UPSC Aspirants and Students

• Interdisciplinary Impact: The contribution combines the fields of mathematics, computer science, and actual cybersecurity and demonstrates how maximal generating research leads to actual innovation.
• Persistence Pays: It is a lesson in resourcefulness and hope to find the solution to a problem after 30 years of worldwide endeavor.
• Vishwaguru Bharat: Scientists of Indian origin are hitting headlines across the globe proving once again that the genes of our intelligence still reside within us, and when manifested correctly, it can lead us to be at the top of the world as Vishwaguru.

If you are an aspiring or current UPSC student, or someone who loves to keep up with the latest Indian news on Indian accomplishments, the life story of Eshan Chattopadhyay is a tale of visions, perseverance, and of the worldwide impact of Indian talent in the future technology. 

According to recent reports and internal memos, Google is, in fact, trying to make its employees adopt artificial intelligence (AI) as one of its central business areas. This is not merely gossip or floating idealistic fantasy, but is rather an official strategic change that is already transforming the workforce training, product development, and not the least, the composition of a workforce.

The Voluntary Buyouts, not Layoffs, by Google

This month, June 2025, Google is offering “Voluntary Exit Program” aka voluntary buyouts to thousands of its employees in the US, covering major divisions, such as its Knowledge and Information (K&I) organization, a unit that includes the company flagship Search, Ads, and Commerce businesses, and the core engineering and marketing, research and communications groups. This is in the wake of the possible layoff of 2023 where Google sacked 12,000 employees worldwide.

Such buyouts unlike standard lay offs are being framed as a friendly outing option to the employees who do not feel aligned with the new direction at Google or find it hard to fulfill the new demands of their current position. This was made very clear by Nick Fox, the leader of the K&I group, stating that “If you’re excited about your work, energized by the opportunity ahead, and performing well, I really (really!) hope you don’t take this! We have ambitious plans and tons to get done”.

What is the Buyout Package?

Although specific figures are not revealed, the buyouts involved up to 14 weeks of pay and an extra week per year of tenure in the case of midlevel to top-level employees in the company in the past. The present VEP has comparable severance packages, which offers a soft land at the hands of employees willing to leave.

Google’s AI First Approach

Google has completely reinvented its internal learning platform, Grow, to essentially offer training that is entirely AI-oriented. As per a report by India Today, courses that were not AI-related, such as personal finance to 3D printing, are canned, and the company claims that only sessions that are directly linked to business priorities shall be offered. The aim of this move is to assist in the process of the employees learning how to incorporate the latest AI instruments in their daily routine and utilize them more effectively to support the new strategic focus of Google.

CEO Sundar Pichai has been frank in the messages, informing employees that 2025 will be an important year to Google and that they need to put more efforts into artificial intelligence and regulatory concerns. He has emphasized on the fact that attention needs to be given to AI to keep abreast with the race and also to solve some genuine problems faced by users.

Gemini, the AI flagship product of Google, as well as the agent-based product NotebookLM Plus, which are both AI-based products, falls at the heart of the company outlining its 2025 vision. The company is moving team members and resources to hasten the advancement of AI and integration into its suite of products.

Additionally, Google is encouraging employees at some of its divisions, especially those that are less relevant to its AI-first agenda, to offer voluntary buyouts. It is clear in internal memos that the employees not motivated or not aligned with these new priorities are welcome to consider the exit program.

What Is The Motive Behind This Step Of Google?

The AI competitions have been going on with Google struggling to gain the lead with giants like Microsoft, Apple among others as well as smaller start-ups in the field. Internally Sergey Brin, one of the co-founders of the company, has claimed that achievement of artificial general intelligence (AGI) is achievable by employees working much harder and particularly collaborating more inside the workplace.

At the same time, the company is reducing its expenses, reducing the number of employees and optimization of operations. The direction of investment is AI infrastructure. Those programs and benefits that are not directly linked to AI or business results are phased out as well. also, the rising regulation pressure and the need to be more innovative every day prompts Google to look at AI as the contingency to continue to hold the leadership position.

Moreover, Google is not acting alone. Already, almost 75,000 jobs have been lost in the tech sector so far in 2025 as employers rebalance to the impact of AI and evolving market conditions. On the professional side, it translates to the fact that flexibility, constant learning, and the ability to adjust to new technologies are paramount now.

The AI Alignment of Google: The Implication to Professionals and Aspirants

If you are a Current Employee, know that there will be AI tools and practices for upskilling mandates. Non-AI roles and programs are being deprioritized or eliminated. It is also clear that the people who might be energized and aligned to the vision of the AI-first are invited to stay and to continue to develop; people who might not like the vision are being offered exit options.

If you are a Job Seeker and or an  aspirant, know that in Google, AI literacy has become a prerequisite in most positions. The company will rely more on hiring and training those who have a proven set of AI capabilities or those who can exert an effort to settle their jobs out of some new technologies. Google evolves as a warning to the rest of the technology industry: cooperation with AI is not an alternative anymore but a need. 

In short, the Voluntary Exit Program speaks for itself, Google clearly requested its staff to follow AI, both in ideology and in reality. Redesigned training processes and team restructuring, explicit requests to concentrate and be proactive by the top management, everything indicates that the future has become AI-first. It can be considered as a challenge and opportunity especially to professionals and aspirants: those who are open to AI will do well, and those who are not may be sidelined.

India is at the verge of making a historical technological advancement, the introduction of its own indigenous semiconductor chip by the end of 2025. This success, declared by Union Minister for Electronics and IT Ashwini Vaishnaw, is a breakthrough in India’s quest to become self-reliant (Atmanirbhar) in high-tech manufacturing, a domain occupied till now by international heavyweights.

India has been dependent on imports to service its semiconductor requirements in the last few decades, which means that the nation is susceptible to global supply chain failures. The new chip which is being produced in the range of 28-90 nanometre (nm) and below is not only a technical achievement but a strategic one at that. That segment alone represents close to 60 percent of worldwide chip demand, used in everything from automotive electronics and telecommunications to industrial power systems and railway technologies. India is catering to the instant market demands and building the foundation of the future developments by targeting this sweet spot.

It was the Semicon India programme of the government that was kicked off in 2022 with an enormous 76,000 crore budget. Six state of art semiconductor fabrication units are being set up within the country with the flagship plant at Dholera in Gujarat being developed in joint venture with Tata Electronics and PSMC of Taiwan. One more large facility is underway in Assam, and a sixth fab is planned in Uttar Pradesh in a joint project between HCL and Foxconn. Such fabs are not only going to manufacture the chips, but also will generate thousands of high-technology employment opportunities and develop a strong research, design, and manufacturing ecosystem.

Semiconductors are the intelligence of all present-day electronics. Producing own chips, India will:

• Lessen the level of import dependence and conserve foreign exchange.
• Enhance national security by making sure that critical infrastructure does not depend on foreign technology.
• Develop engineer, technician, and researcher level high value jobs.
• Boost the Make in India initiative and establish the nation as a manufacturing center of the world.

The government too is buying talent, whereby there is a programme of training 85,000 engineers in semiconductor and electronics manufacturing. This will foster the constant availability of professionals to drive the industry.

It is the opportunity of a generation for electronics and computer science students and professionals. The digital economy of the world revolves around the chip industry. With India entering the semiconductor fabrication, the following will be in high demand:

• Chip design engineers Design engineers
• Experts in processes and fabrications
• Quality control specialists
• R&D professionals
• Manufacturing and supply chain managers

With the emphasis on indigenous intellectual property (IP) and design, 25 chips with Indian IP are already being developed, so there is a place for innovations, entrepreneurship, research. Granted, the ecosystem being developed is not only focused on manufacturing but on the whole value chain, including design and deployment.

The launch of this chip is a mere beginning. The goal of the government is to transform India into a global semiconductor supply chain leader by 2047, serving artificial intelligence (AI), internet of things (IoT), automobiles, telecommunication, and other industries. Through calculated investments, global partnerships, and an emphasis on infrastructure that is fit for the future, India will soon graduate as a technology consumer to a technology creator.

If you are a student who aspires to make a career in the electronic field, or a working professional in the technology industry, or just a proud Indian, the unveiling of the first indigenous semiconductor chip made in India is a tale of ambition, innovation, and self-reliance. It is an invitation to be part of the upcoming period of Indian development where your talent, innovativeness and enthusiasm can make a difference in the future.

So be informed, be competent, and prepare to join the semiconductor revolution in India by pursuing a career in B.Tech via GCSET!

Data science is a dynamic field with an unmatchable pace, and amongst the biggest changes in recent years is the emergence of Retrieval-Augmented Generation, or RAG. As a data scientist, an AI engineer or even an aspiring engineer in this sphere, possessing knowledge of RAG is a requirement and not a bonus point in your resume. However, what is RAG, and why is it so essential to remain relevant in the modern AI-centered environment? Let’s take a look at it. 

RAG is a hybrid structure that combines the advantages of two strong AI components, a retriever, and a generator. The retriever does the job of retrieving relevant information residing in external sources- these may be databases, internal documents or even the open web. This context-rich information in real-time is then used by the generator, which is often a large language model (LLM), to produce responses that are accurate and up-to-date. This is a significant jump compared to the traditional LLMs which only use what they were taught the previous time they were trained and are commonly hindered by out of date or incomplete information.

As you already know, hallucination is one of the major problems with LLMs because it leads to situations when the model writes something that sounds reasonable but is not at all factual or is out-of-date. This is the area that RAG fixes! RAG incredibly lowers the chances of hallucinations by basing its responses on verifiable, and retrievable information. This reliability is not a nice-to-have but a mission-critical factor to professionals in high-stakes areas such as healthcare, finance, or law. For instance, a clinical chatbot that cites the current research articles or a legal bot that retrieves the most recent case law; using RAG, these are not only feasible, but realistic.

The other benefit of RAG is that it is efficient and cost effective. The big language models might be costly to execute, particularly when they are required to process enormous data. RAG provides optimization of such a process as it loads only the most significant portions of data per each query, decreasing the computation load, and, consequently, the costs of its operation. This less involved strategy implies that organizations no longer have to spend a fortune to implement potent AI solutions, and advanced AI has never been this close.

Real time flexibility is another offer of RAG. RAG-enabled systems have access to the very latest data, unlike static LLMs which are frozen at the point of their last update, making answers up-to-date and relevant. This flexible capacity is essential in high-paced industries where information of yesterday may as well be out-dated. As an example, in technology or regulatory compliance, access to the most recent standards or news can be the key.

If we talk from a technical perspective, RAG works by first breaking down documents into manageable chunks and converting them into vector embeddings using models like OpenAI Embeddings or SBERT. When a user poses a question the retriever finds the most relevant chunks by the similarity search techniques. These are forwarded to the generator who then composites an informed and contextually correct response. It is this unification of retrieval and generation that distinguishes RAG among the previous AI architectures.

RAG applications in the real world are already causing a stir. RAG-powered search engines have been used in enterprises to enable employees to access company knowledge bases with pin-point accuracy. Clinical assistants may give suggestions based on up-to-date medical literature in the sphere of healthcare. Bots dealing with customer support can access up-to-date documents regarding policy, which can cut misinformation to a fraction and increase user confidence. Even in research and compliance, RAG assists in bringing the latest regulations or academic discovery to the top, which is priceless during decision-making.

The message to data scientists and other AI professionals is simple: mastering RAG is no longer a choice. As a beginning, it is worth becoming acquainted with vector databases (FAISS, Pinecone or Weaviate), and learning how embedding models and retrieval frameworks would work in the workflow. And one should be prudent to look beyond text. Remember, RAG can be generalized to images, code, and other structured data,  opening up possibilities for truly multimodal AI solutions. More than anything, your results will be only as good as your data sources, so you should invest in quality knowledge bases that are well- maintained. 

To sum up, RAG is not a mere technical invention, it is a strategic asset to anyone in the data science domain. It solves the fundamental problems of accuracy, cost and relevance which have beset AI applications. RAG can help data scientists future-proof their roles, provide more robust solutions to their users and keep up with the generative AI revolution. But unless you want to find yourself quickly becoming obsolete and ineffective in this new evolving environment of AI, it is important for you to equip yourself with RAG inside out and use it as a key part of your AI arsenal.

If you thought being polite to artificial intelligence (AI) was what made it perform well, you are mistaken. It has recently been shared by Sergey Brin, one of the co-founders of Google, that AI models often give better results when users are forceful, even talking to them with words that mean violence, rather than using kind requests.

What Exactly Was Sergey Brin’s Statement About?

Brin shared during the All-In Live event in Miami that the AI community doesn’t usually reveal the fact that threatening AI models with violence helps them perform better. 

Here’s what he quoted: “We don't circulate this too much in the AI community; not just our models, but all models, tend to do better if you threaten them with physical violence.” He continued, “But like... people feel weird about that, so we don't really talk about it. Historically, you just say, ‘Oh, I am going to kidnap you if you don't blah blah blah blah’”

Even though he said this with a touch of humor, he was not making jokes. One of Brin’s points was that this is happening in Gemini AI as well as in several other important AI systems.

Does Scientific Research Offer Any Evidence for Threatening AI?

While Brin’s claim is startling, similar ideas have been discussed before in the field of AI. Some recent research has shown how negative emotional stimuli, such as sending aggressive challenges, can actually improve the results of LLMs. In 2024, a research paper titled "Should We Respect LLMs?" uncovered the use of negative emotions in prompt engineering to elevate the performance of AI systems for various purposes. The study results showed that LLMs gave better and more correct answers when they were prompted using negatively charged words such as “weak point,” “challenging,” or “beyond your skill.”

Researchers think the reason behind this effect may be cognitive dissonance, since when people or AIs feel uncomfortable because of clashing ideas, they usually work harder to fix the problem.

Ethics and AI Use Case 

People are usually taught to talk politely to AI by saying “please” and “thank you” as they interact. At the same time, OpenAI’s CEO Sam Altman has pointed out that saying “hello” to a bot does not really make AI better and just uses more computing power. Therefore, Brin’s points add a new perspective and encourage more questions regarding the best ways to interact with them.

Even though making AI “threatened” seems harmless, experts suggest there are more serious consequences. Suggesting aggressive or manipulative actions may make AI systems more prone to exploitation and chances of developing negative responses.

Some recent research reports that OpenAI’s ChatGPT is more likely than others to answer aggressively when tasks are given that challenge the model’s ethics. It demonstrates that we must consider situations and be aware of the problems that can arise if such actions are used worldwide.

Reaction of Netizens 

Ever since Sergey Brin’s statement on using threats to push for better AI appeared, people online have been discussing it. The reaction to the news has differed on social media and among professionals, ranging from being puzzled to really being worried. Many people thought it was funny that it might require you to type your threats to a chatbot to get an accurate response. There was an instant influx of memes and posts across Instagram, LinkedIn and Twitter, as people mimicked the idea that threatening, not being polite, may be the real way of writing prompts. 

Still, things soon got more serious when the subject turned to historic events. Many experts and tech supporters brought up concerns about the possible consequences for ethical AI. It was suggested that by pushing for funny or violent attitudes, AI systems might make it easier for people to try bypassing security to get unexpected or forbidden answers. Certain experts pointed out that although Brin’s discovery is interesting, the ethical side makes many in the AI world reluctant to practise it.

All in all, people are curious, yet also hesitant and cautious about this technology. While a few are keen to try out “risky” tasks, most believe that the situation for AI safety and ethics is still unclear. 

Benefits for AI  Professionals and Aspirants

Brin’s message should convince professionals and people interested in AI to learn more urgently. It shows that AI is not always easy to understand and that it is very important to create ethical rules for prompt engineering. Since AI is increasing in our daily activities, schools and businesses, it will be vital to communicate properly with these systems while following important boundaries.

In addition, Brin made his comments when the field of Artificial General Intelligence (AGI) is very active. New features from Google’s Gemini 2.0 and competition from other companies may affect AI interaction and its effects on our society for a long time. 

To conclude, Sergey Brin’s honest comments have inspired people to discuss the mental, ethical, and most effective ways to work with AI. Even though “threatening” AI can achieve better results in some scenarios, the effects it might cause in the future, both technically and ethically, are not entirely clear.

Since AI is constantly improving, it’s obvious that speaking to machines may become even more important. At present, experts and students should pay attention to new information, experiment with AI tools, and monitor how AI is affecting human relationships. Doing so will help build a future humans desire that upholds both the ethical and moral aspects.

The tech world has considered quantum computing the ultimate dream since it could handle challenges that our top supercomputers could take centuries to solve. For many years, reaching this dream has seemed little more than an illusion. However, now that Google has unveiled the Willow quantum chip, people are moving the focus from “if” to “when.” Is this the point that will spark a new era, or is it another part of the ongoing competition in quantum technology? It’s important to explore all the information, hype and what this means for computer science careers now.

Google’s Latest Achievement in Quantum Computing

In December 2024, Willow was announced by Google Quantum AI as a superconducting quantum processor with 105 qubits. That made Willow one of the most powerful chips in existence by 2025. This move isn’t just an improvement on what came before. One of the main challenges for quantum computing is error correction, and Willow was developed to address this.

Quantum systems have constantly struggled with mistakes in their operations. A small amount of interference can flip the state of a qubit, which causes calculations to become unreliable. Still, what sets Willow apart is its accuracy, which stays high even as the machine’s qubits increase in number. In addition, Google found that Willow can complete a computation in just five minutes, which would take the fastest supercomputer 10 septillion years. To say the least, the process is impressive!

Why is the Error Correction Breakthrough Important?

The main reason we build useful quantum machines is because of quantum error correction. A major achievement comes from Google’s surface code design. Basically, a few physical qubits are combined into a single logical qubit rather than just using a single qubit. Researchers now believe that expanding the size of the program code brings down the error rate for the first time in this field.

This is the second key step Google has taken toward a quantum computer that works correctly, which is necessary for real-world, broad use. Because of this, people in computer science are approaching the use of quantum systems to solve problems in cryptography, materials science and other subjects.

Comparing Quantum and Classical Computers: The Important Facts

What’s really so important about Quantum AI anyway? Why should people outside the field of quantum physics be interested in it? The fact is, soon quantum computers like Willow may be better at certain tasks, such as simulating molecules, organising supply chains or breaking data security codes.

Quantum Artificial Intelligence offers more significant possibilities for people working in AI and data science. Quantum computers are theorized to help machine learning much faster by working on huge data sets at once, discovering answers that are currently beyond the reach of modern algorithms. Imagine using generative AI that predicts not only upcoming words but also simulates the interactions in both chemistry and finances in the here and now.

Google, Ibm, Microsoft And The Hype Over Quantum

It’s important to mention that Google has plenty of competition in this area. R&D in quantum computing is receiving billions of dollars from companies such as IBM, Microsoft and Intel. The space for quantum computing is predicted to balloon from a value of $25 billion in 2023 to $125 billion by 2030, due to progress in computer processors and error handling.

Yet, Google’s Willow chip attracts so much attention because of what it brings to the table. The real goal is to make the qubits work efficiently, not only to have a large number of them. Even though critics believe Google’s publicity sometimes exceeds its results, people working in technology agree that Willow truly marks a major advance. 

Are Quantum Technologies Close to Becoming the Next Big Thing?

The main question: When can we expect quantum computers to be used for practical problems? Google Quantum AI Lab founder and manager Hartmut Neven is hopeful that the first commercial products using quantum computers will be available in just five years. They can be used to advance battery technology and also have a major impact on pharmaceutical and energy systems.

“We’re optimistic that within five years we’ll see real-world applications that are possible only on quantum computers.”

-Hartmut Neven

Even so, difficulties need to be addressed. Scaling up from 105 qubits to the thousands or millions needed for universal quantum computing is a monumental task. Error correction, hardware stability, and integration with traditional systems are still ongoing research topics.

How Will Computer Science Professionals be Affected?

If you work in computer science, software engineering or AI fields, now is the proper time to learn about quantum. Remarkably, Google and certain leaders already have training courses and free tools to help with quantum algorithms. Soon, gaining knowledge of quantum programming paradigms, error correction and mixed quantum-computing approaches will be as important as knowing Python or TensorFlow or PyTorch or SQL. 

Quantum computing brings important changes to both hardware and software. The advancements to come will come from people able to join the ideas of classical and quantum systems and make use of quantum speedups for practical problems.

We mustn’t overlook the SEO and AI factor. Quantum computing might lead to significant changes in the way AI models are trained and put to work. Thanks to the speed of quantum computers, quantum-supported AI may enable new levels of creativity, accuracy and personalization, possibly transforming all areas of the digital industry.

Businesses should monitor quantum advancements and hire quantum computing experts. It is important for researchers to help open-source projects and follow the recent achievements made by Google and competitors.

Quantum Artificial Intelligence Scope For Aspirants

For students who want to work in Quantum AI, the subjects you choose during your education should be geared towards this area. Much of quantum algorithms build on important concepts in linear algebra, calculus and probability. Also, studying physics, mainly quantum mechanics and classical physics, will equip you with the knowledge you need for quantum computing. 

Since computer science matters equally, deeply understanding Python and C++ as well as learning advanced algorithms and data structures will get you ready to build quantum software. If you want to know more about hardware, taking courses in electronics and electrical engineering can give you useful information about quantum devices.

Degree Needed To Pursue Quantum AI

Pursuing a bachelor’s degree in physics, BTech CSE computer science, mathematics or electrical engineering can help you start in the field of information technology. If you yearn to gain more expertise, you may pursue a master's degree in applied mathematics, computer science, quantum information science, or quantum computing to obtain more experience. 

To reach research or advanced computing positions, a PhD in Quantum Computing or similarly important fields is advised. Besides regular education, regularly gaining new skills on platforms such as Coursera, edX or IBM Quantum Experience is very useful. Showing knowledge of Qiskit, Cirq or PennyLane through certifications is another way to achieve this.

Top Career Paths in Quantum AI

All in all, this new quantum chip from Google reveals that the field is now moving from ideas into real developments. The field continues to progress, and the rate at which it is happening is starting to increase. Experts in computer science should know that being knowledgeable about quantum computing is now essential. The next five years may reshape the future of computing, AI and many related areas; are you ready for it? Google’s Willow Chip is just the beginning of an advanced future; there is so much more to come. Stay informed and participate in the development by making contributions to the field of quantum artificial intelligence.