UPSC Prelims 2025Question 47Science & Technology

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?

Options

Correct answer: c

Decision rule

Attribution statements pair a real technology with the wrong company; verify who built it separately from what it is expected to do.

(c) I and III only

Statement I: It is expected that Majorana 1 chip will enable quantum computing
- Correct. The Majorana 1 chip is based on Majorana zero modes (MZMs), which are theorized to help create topological qubits — a more stable form of quantum bits.
- Microsoft (not AWS) is leading this research via its Azure Quantum team, and the Majorana 1 chip is expected to play a key role in future fault-tolerant quantum computers.
Statement II: Majorana 1 chip has been introduced by Amazon Web Services (AWS)
- Incorrect. The chip has been developed by Microsoft, not AWS.
- AWS does have its own Braket quantum computing service but is not associated with the Majorana 1 chip.
Statement III: Deep learning is a subset of machine learning
- Correct. Deep learning is a specialized subset of machine learning that uses artificial neural networks with multiple layers to model complex patterns in data.
- It is used in applications such as image recognition, language processing, and more.

Statement	Claim	Status	Explanation
I	Majorana 1 chip will enable quantum computing	Correct	Based on Majorana zero modes for topological qubits (Microsoft’s research)
II	Introduced by Amazon Web Services (AWS)	Incorrect	AWS has no such chip; Majorana 1 is from Microsoft
III	Deep learning is a subset of machine learning	Correct	Deep learning uses neural networks; part of the ML hierarchy

Concept	Explanation
Majorana Zero Modes	Quasiparticles that may enable fault-tolerant qubits in quantum computers
Topological Qubits	Quantum bits that are more stable due to their encoding in non-local quantum states
Deep Learning	ML technique with deep neural networks used in tasks like speech/image recognition
Machine Learning	Field of AI where systems learn from data without explicit programming

Quantum Computing is being rapidly pursued by companies like Microsoft, IBM, Google, and AWS, but with different technological approaches.
Deep learning continues to dominate fields such as generative AI, autonomous vehicles, and healthcare diagnostics.

Concept	Description	Key Developer / Proponent	Field	Relevance
Majorana 1 Chip	A chip based on Majorana zero modes (MZMs) for topological quantum computing	Microsoft (Azure Quantum)	Quantum Computing	Expected to enable fault-tolerant quantum computing through stable qubits
Majorana Zero Modes	Quasiparticles theorized to exist in topological superconductors	Physics / Quantum Information	Quantum Physics	Enable non-Abelian statistics crucial for topological qubits
Topological Qubits	Qubits encoded in topological states to resist decoherence	Microsoft	Quantum Computing	More stable than conventional qubits; core idea behind Majorana chip
Quantum Computing	Computing based on quantum states (superposition, entanglement)	IBM, Google, Microsoft, AWS	Quantum Technologies	Promises exponential speedups for some classes of problems
Deep Learning	Subset of ML using multi-layered neural networks to learn complex patterns	Popularized by Google, Facebook	Artificial Intelligence	Powers image recognition, LLMs, speech, autonomous systems
Machine Learning	Subset of AI where systems learn from data to improve performance	Academic + Industry-wide	Artificial Intelligence	Encompasses supervised, unsupervised, and reinforcement learning
Artificial Intelligence	Broad field aiming to mimic human cognitive functions with machines	Field origin in 1956 (Dartmouth)	Computer Science	Umbrella for ML, DL, expert systems, robotics, etc.

Level	Includes	Example
Artificial Intelligence	All intelligent systems	Chatbots, game-playing AI, robotics
→ Machine Learning	Learning from data	Fraud detection, recommendation systems
→→ Deep Learning	Neural networks with multiple layers	Language models (GPT), facial recognition