Question map
Consider the following statements : I. It is expected that Majorana 1 chip will enable quantum computing. II. Majorana 1 chip has been introduced by Amazon Web Services (AWS). III. Deep learning is a subset of machine learning. Which of the statements given above are correct?
Explanation
**Explanation:**
**Statement I is correct.** Microsoft's Majorana 1 represents a significant moment in computing history, as topological quantum computing has become a physical reality.[1] The Majorana 1 chip brings us one step closer to practical applications that could transform industries.[2] While practical, large-scale quantum computing remains years away, this achievement accelerates the timeline.[1]
**Statement II is incorrect.** Microsoft's Majorana 1 represents a company milestone[1], clearly indicating that the chip was introduced by Microsoft, not Amazon Web Services (AWS).
**Statement III is correct.** Deep learning is indeed a subset of machine learning. This is a fundamental concept in artificial intelligence—machine learning encompasses various techniques for computers to learn from data, while deep learning specifically uses neural networks with multiple layers to learn hierarchical representations.
Therefore, only statements I and III are correct, making option C the right answer.
SourcesPROVENANCE & STUDY PATTERN
Full viewThis is a classic 'Entity Swap' trap mixed with a fundamental definition. The difficulty lies entirely in knowing which tech giant owns the 'Majorana' brand. Strategy: For every major tech breakthrough (Quantum, AI models), memorize the 'Parent Company' and the 'Underlying Physics/Method' (e.g., Topological vs. Superconducting).
This question can be broken into the following sub-statements. Tap a statement sentence to jump into its detailed analysis.
- Explicitly states the Majorana 1 chip 'brings us one step closer to practical applications', tying the chip to enabling usable quantum computing.
- Lists potential quantum computing applications (materials science, chemistry, finance), implying the chip is expected to advance quantum capability toward real-world use.
- Describes Majorana 1 as a milestone that makes 'Topological quantum computing' a physical reality, indicating it moves the field toward functioning quantum computers.
- Says the achievement 'accelerates the timeline' for practical, large-scale quantum computing, implying expectation that the chip will enable progress.
- Provides a timeline entry: '2024: Majorana 1 chip unveiled with functional topological qubits', which supports that the chip is a functioning step toward quantum computing.
- Mentions planned follow-up milestones (e.g., 2025 commercial applications), indicating expectation that Majorana 1 will enable further quantum developments.
Mentions that technology has enabled scholars to handle large quantities of data and that tools (GIS, internet) expanded analytical capacity.
A student could combine this with the fact that advanced processors/chips need large-scale data handling and specialised software ecosystems to be useful, so the existence of a chip alone may not guarantee practical quantum computing without ecosystem support.
Explains that optic fibre and digitisation enabled rapid, secure, high-volume data transmission and integration into the internet.
A student might infer that deployment of novel computing hardware (quantum or otherwise) depends on high-speed communications and data infrastructure to connect, control, and use such devices in practice.
Describes a large-scale effort to build scalable broadband infrastructure (mix of fibre, radio, satellite) for widespread connectivity.
One could use this to reason that even if a new chip enables quantum computations in principle, widespread impact requires network and infrastructure readiness to integrate and distribute such capabilities.
Uses the example of 'chips' being packaged with an inert gas to maintain quality—illustrates that certain technologies require specific physical/environmental controls.
A student could analogise that enabling quantum chips may similarly require specialised physical conditions (e.g., controlled environments), so the mere announcement of a chip doesn't ensure operational quantum computing without these conditions.
- Explicitly states that Microsoft introduced the Majorana 1 chip on the day of the dispute.
- Directly attributes the introduction of the Majorana 1 to Microsoft, which contradicts AWS being the introducer.
- Describes the Majorana 1 as representing Microsoft's distinctive vision, linking the chip to Microsoft.
- Frames Majorana 1 as 'Microsoft's' chip rather than an AWS product.
This snippet shows the acronym 'AWS' being used to mean 'Automatic Weather Station' in official contexts, indicating 'AWS' is ambiguous and not always 'Amazon Web Services'.
A student could check whether references to 'Majorana 1' occur alongside computing/cloud contexts (typical for Amazon Web Services) versus meteorological or other domains to disambiguate 'AWS'.
This snippet discusses 'Amazon' as an e‑commerce company and tax treatments, illustrating use of the word 'Amazon' in a corporate/commercial sense distinct from other uses of 'Amazon' or 'AWS'.
Use this to remind a student to look for corporate press releases or product pages from Amazon (the company) when testing whether Amazon Web Services introduced a chip.
Also discusses Amazon in the context of e‑commerce and legal/tax distinctions, reinforcing that 'Amazon' appears in business/economic texts rather than as a hardware chip vendor in these snippets.
Compare this business-context usage with the domain where 'Majorana 1' appears (e.g., tech announcements, semiconductor trade press) to assess plausibility of AWS involvement.
This snippet shows 'Amazon' can refer to geographic/biome-related institutions (Amazon Fund), highlighting multiple unrelated uses of the word 'Amazon'.
A student should be cautious: finding the word 'Amazon' nearby 'Majorana 1' doesn't prove Amazon Web Services created the chip — check the specific organizational owner or context.
Mentions systems 'equipped with the latest technologies like artificial intelligence and deep learning software for building and deploying deep learning-based applications', showing deep learning presented as a specific software/technique alongside AI.
A student could infer a taxonomy where deep learning is a specific technology used within the broader set of AI tools and compare that to standard definitions of machine learning to judge subset relationships.
Describes AI and machine learning used together for image recognition and decision-making, implying machine learning is a category of techniques applied in practical AI systems.
Combine this with the observation that deep learning commonly powers image recognition to hypothesize that deep learning is one technique within the machine-learning toolkit.
Specifically pairs 'Image processing combined with machine learning' for tasks like detection and grading, indicating machine learning is the applied method for such tasks.
A student could note that deep learning is a dominant approach for image tasks and therefore plausibly a specialized form of the broader 'machine learning' methods mentioned here.
Speaks generally of machines that 'can learn from the large amount of data generated and then make autonomous decisions', establishing a pattern of 'learning from data' as the core function of these technologies.
Use the common rule that 'learning from data' defines machine learning; since deep learning is known to be a data-driven method, a student could place it under that category.
- [THE VERDICT]: Trap + Sitter. Statement II is the trap (Microsoft, not AWS); Statement III is a conceptual sitter (Standard AI hierarchy).
- [THE CONCEPTUAL TRIGGER]: Science & Tech > Awareness in IT & Computers > Quantum Computing & Artificial Intelligence.
- [THE HORIZONTAL EXPANSION]: Map the Quantum Rivals: Google = Sycamore (Superconducting); IBM = Eagle/Osprey (Superconducting); Microsoft = Majorana (Topological Qubits); Intel = Tunnel Falls (Silicon Spin). Know the AI Hierarchy: AI (Umbrella) > Machine Learning (Statistical methods) > Deep Learning (Neural Networks).
- [THE STRATEGIC METACOGNITION]: When UPSC names a specific proprietary chip or model (e.g., 'Majorana 1', 'Gemini', 'Q*'), the most common error introduced is the 'Owner Swap'. Do not assume the company mentioned is correct just because the technology sounds plausible.
AWS can denote an Automatic Weather Station as well as Amazon Web Services, so acronym confusion can lead to wrong attributions about who introduced hardware.
High-yield for UPSC because recognising acronym ambiguity prevents misattribution across topics (technology, meteorology, corporations). It connects to questions that cross sectors (infrastructure, disaster management, IT) and helps eliminate distractors in multiple-choice and comprehension items.
- Exploring Society:India and Beyond ,Social Science-Class VII . NCERT(Revised ed 2025) > Chapter 2: Understanding the Weather > DON'T MISS OUT > p. 39
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 7: Indian Economy after 2014 > E-Commerce and the MSME Sector > p. 242
Tax rules like the Equalisation Levy apply to non-resident e-commerce firms (example: Amazon) and help distinguish revenue/taxation issues from product development or hardware introductions.
Important for public finance and economy sections of UPSC: explains how digital firms are taxed, links to international taxation and permanent establishment concepts, and aids answers on digital economy regulation and policy.
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 4: Government Budgeting > Following are certain basic features of the above taxes: - > p. 171
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 4: Government Budgeting > Following are certain basic features of the above taxes: - > p. 170
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 7: Indian Economy after 2014 > E-Commerce and the MSME Sector > p. 242
The term Amazon appears in corporate, environmental fund, and geographic contexts, so clarity about the referent is essential before attributing actions like introducing a chip.
Useful across geography, environment, and economy papers: helps distinguish questions about corporate initiatives from conservation funds or the Amazon biome, enabling accurate cross-disciplinary answers.
- FUNDAMENTALS OF PHYSICAL GEOGRAPHY, Geography Class XI (NCERT 2025 ed.) > Chapter 11: World Climate and Climate Change > Tropical Wet and Dry Climate (Aw) > p. 92
- Environment, Shankar IAS Acedemy .(ed 10th) > Chapter 24: Climate Change Organizations > Amazon Fund (Fundo Amaz6nia) > p. 347
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 7: Indian Economy after 2014 > E-Commerce and the MSME Sector > p. 242
Both machine learning and deep learning are used to analyse images for crop quality, weed detection and disease identification in agriculture.
High-yield: Understanding image-driven AI applications links technology to agricultural productivity, pest/disease management and cost savings — frequent UPSC themes on tech for development. It connects to topics like digital agriculture, extension services and public investment in agri-tech, and enables questions on benefits, constraints and policy responses.
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 11: Agriculture - Part II > Application of Technology in Agriculture: > p. 358
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 11: Agriculture - Part II > Smart Farming > p. 360
- Indian Economy, Nitin Singhania .(ed 2nd 2021-22) > Chapter 9: Agriculture > X Krishi Megh > p. 332
Industry 4.0 describes machines that are digitally connected and can learn from large amounts of data, implying use of learning algorithms.
High-yield: Grasping how learning systems operate within smart factories helps answer questions on manufacturing modernization, automation and employment impacts. It links to industrial policy, technology adoption and skilling—topics commonly tested in GS papers and essays.
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 7: Indian Economy after 2014 > Fourth Industrial Revolution (Industry 4.0): Present > p. 233
Dedicated data centres and platforms are used to build and deploy deep learning applications for image analysis and disease identification in agriculture.
High-yield: Knowing the role of infrastructure (data centres, AI/deep learning tools) is useful for policy questions on digital public goods, rural tech deployment and scalability of innovations. It enables evaluation of implementation challenges and public provisioning of tech services.
- Indian Economy, Nitin Singhania .(ed 2nd 2021-22) > Chapter 9: Agriculture > X Krishi Megh > p. 332
The 'Next Logical Question' is the *type* of qubit. Microsoft's Majorana chip uses 'Topological Qubits' (more stable, less error-prone), whereas Google and IBM primarily use 'Superconducting Qubits'. Expect a question comparing these qubit technologies.
The 'Corporate Branding' Heuristic: AWS typically names products with descriptive or cloud-centric terms (e.g., Braket, Graviton, Trainium). 'Majorana' is a fundamental physics particle name, which aligns more with Microsoft's long-term 'Station Q' research branding. If a product name sounds deeply theoretical/scientific, question if it belongs to a service-provider like AWS.
Link Quantum Computing to GS-3 Internal Security: 'Post-Quantum Cryptography'. Quantum computers (like Majorana) threaten current RSA encryption standards. This connects the chip to national cyber-security policies.