Indian Scientists Discover New Quantum Phenomenon

Indian researchers have discovered that preparing two quantum bits with opposite spins (antiparallel) can reveal more information than two identical copies, a finding that could improve how we test quantum devices and strengthen quantum cryptography.

The study result touches the very heart of quantum theory.

In normal physics, you can measure many things at once — like the speed and weight of a car — and the only limits are practical ones, such as how good your tools are.

But in the quantum world, nature itself sets strict boundaries. Rules like the Heisenberg uncertainty principle and Bohr’s complementarity principle say you can’t know certain pairs of properties perfectly at the same time.

The new research shows something surprising: if you prepare particles in a clever way, you can get around some of these restrictions. In other words, by flipping how the particles are set up, scientists can sometimes learn more than the usual limits would allow.

What the discovery is

• Scientists from S. N. Bose National Centre for Basic Sciences, Balagarh Bijoy Krishna Mahavidyalaya, and Indian Statistical Institute, Kolkata showed that a pair of qubits prepared with antiparallel spins can be used to predict three mutually incompatible spin components simultaneously — something not possible with two identical (parallel) qubits.
• This result was reported in a recent paper and highlighted by an official press release.

Why this is surprising (in simple words)

Simultaneous measurement of spin properties along three mutually orthogonal space directions becomes possible on antiparallel qubit-pair.

• In everyday life, two identical copies usually give you more confidence about something.
• In quantum physics, measuring one property can disturb another (think of trying to measure both the exact position and speed of a tiny particle). This is known as complementarity and is related to the Heisenberg uncertainty principle.
• The new work shows that flipping one qubit against the other can, in some tasks, beat identical copies — letting you extract information that seemed forbidden before.

How it works (brief, non–technical)

• Qubits have a property called spin, which can point in different directions.
• Some spin measurements are incompatible: knowing one precisely prevents knowing another.
• By preparing two qubits in an antiparallel arrangement, researchers found measurement strategies that make three such incompatible spin components effectively compatible for that pair — enabling richer joint measurements.

Practical implications

• Device characterization: Better ways to probe unknown quantum devices using fewer resources, speeding up testing and calibration.
• Quantum cryptography: Protocols that rely on extracting or hiding information from qubits may be redesigned to use antiparallel pairs for improved performance or security.

Big picture

• This result touches the foundations of quantum theory: it shows that how you prepare quantum systems matters as much as what you measure.
• It also echoes famous quantum puzzles (like the Mean King’s problem) where clever preparation and measurement strategies unlock surprising capabilities.

Quick takeaway for non‑experts

• Opposites can be more revealing than twins in the quantum world. By preparing qubits in opposite states, scientists can sometimes get around measurement limits that once seemed absolute — a small twist with potentially big technological payoff.

Simple Analogy

Think of two clues in a mystery: if both clues are identical, you don’t learn much. But if one clue is the opposite of the other, suddenly the full picture becomes clearer. That’s what these scientists found in the quantum world.

For the official announcement and institutional details, see the published paper in Physical Review Letters.

Quantum measurement limits are the fundamental restrictions in physics that prevent us from knowing all properties of a quantum system with perfect precision at the same time. These limits arise from the Heisenberg uncertainty principle and the unavoidable “back‑action” of measurement itself.

Core Principles

• Heisenberg uncertainty principle: You cannot simultaneously measure certain pairs of properties (like position and momentum, or spin along different axes) with unlimited accuracy.
• Bohr’s complementarity: Some properties are mutually exclusive — observing one prevents full knowledge of the other.
• Measurement back‑action: The act of measuring disturbs the system. For example, shining light to detect a particle’s position changes its momentum.

Types of Quantum Limits

Limit	Definition	Example
Heisenberg Limit	Absolute bound on precision due to uncertainty principle.	Position vs. momentum trade‑off.
Standard Quantum Limit (SQL)	Practical bound in interferometry and sensing with natural quantum states.	Laser interferometers in gravitational wave detectors.
Quantum Noise Limit	Added noise from amplifiers or detectors that cannot be eliminated.	Photon shot noise in optical measurements.

Everyday Analogy

Imagine trying to photograph a moving car at night.

• A brighter flash gives you a clearer picture of its position but blurs its speed.
• A longer exposure shows speed streaks but loses exact position.

This trade‑off mirrors quantum limits: you can’t have both perfectly at once.

Why It Matters

• Quantum computing: Easier testing and calibration of new technologies. Limits affect how qubits can be read without destroying information.
• Quantum cryptography: Stronger security for communication, since more information can be extracted from fewer resources.  
• Quantum metrology: Advanced techniques like entanglement and squeezed states help push beyond the standard quantum limit for ultra‑precise sensors.

Key Takeaway

Quantum measurement limits are not technological flaws — they are built into nature itself. By preparing states cleverly (like antiparallel spins), scientists can sometimes circumvent practical limits and extract more information than intuition suggests.

This discovery highlights a deep truth: in quantum physics, contrast can be more powerful than sameness. It could speed up progress in quantum computing, secure communication, and even reshape how we understand the limits of nature.

Kolkata Scientists at SNBNCBS Develop Unique Transistor for Faster, Greener Electronics

Scientists at the S. N. Bose National Centre for Basic Sciences (SNBNCBS), Kolkata, which is Under Department of Science and Technology (DST), Govt. of India, have developed a unique transistor using single molecules, controlled by mechanical forces rather than traditional electrical signals. This innovative approach, known as the mechanically controllable break junction (MCBJ), involves creating a sub-nanometer gap in a metal wire to accommodate a single molecule like ferrocene.

A transistor is a fundamental component in modern electronics, used to amplify or switch electronic signals and electrical power. It is used in various devices, including computers, smartphones, and amplifiers, making them crucial for modern technology.

The transistor's performance is influenced by the orientation of the ferrocene molecules between electrodes, which can either enhance or diminish electrical conductivity. This breakthrough could lead to advancements in quantum information processing, ultra-compact electronics, and low-power molecular devices.

Dr. Atindra Nath Pal and Biswajit Pabi, in collaboration with their team, conducted experiments and discovered that the orientation of ferrocene molecules between silver electrodes significantly affects the transistor's performance. Depending on the molecular orientation, the device can either enhance or diminish electrical conductivity through the junction, underscoring the importance of molecular geometry in transistor design.

Going forward, the scientists at SNBNCBS explored gold electrodes with ferrocene at room temperature. This combination resulted in a surprisingly low resistance, nearly five times the quantum of resistance (around 12.9 kΩ), but significantly lower than the typical resistance of a molecular junction (around 1 MΩ). This suggests the possibility of creating low-power molecular devices.

It's a breakthrough development for the future of electronics, making them faster and more energy-efficient.

How does the mechanically gated transistor work?

Mechnical gating response of Ferrocene molecule connected between two silver electrodes

The mechanically gated transistor operates by using mechanical forces to control the flow of electrical current through a single molecule. Here's a simplified breakdown of how it works:

1. Mechanically Controllable Break Junction (MCBJ): This technique involves creating a tiny gap in a metal wire, just large enough to fit a single molecule. This gap is controlled mechanically, often by bending the wire.

2. Single Molecule Placement: A molecule, such as ferrocene, is placed in this gap. The molecule acts as the active component of the transistor.

3. Mechanical Force Application: By applying mechanical forces (e.g., stretching or compressing the wire), the orientation and position of the molecule can be altered.

4. Conductivity Modulation: The electrical conductivity between the electrodes is influenced by the molecule's orientation. When the molecule is aligned in a certain way, it can either enhance or reduce the flow of electrons, effectively acting as a switch.

5. Signal Control: Unlike traditional transistors that use electrical signals to control current flow, this transistor uses mechanical forces, which can lead to lower power consumption and potentially faster switching speeds.

This innovative approach could pave the way for more energy-efficient and compact electronic devices.

Molecular structure of Ferrocene

Indian Scientists Use Machine Learning to Help Predict New Materials for Nano Alloys, Semiconductors and Rare Earths

Scientists at the S N Bose Centre for Basic Sciences (SNBNCBS), an autonomous institute of the Department of Science and Technology (DST), Govt. of India, have used Machine learning (ML) to help predict new materials for nano alloys, semiconductors & rare earths. 

Technically speaking, SNBNCBS scientists used ML to develop a design map of alloys at the nano-scale which can help predict the match of pairs of metals that can form bimetallic nanoalloys (composed of two different metal elements).

Fir uninitiated, Rare Earths are a group of 15 elements in the periodic table and a substantial part of these elements are less common and more expensive. Rare earth compounds with permanent magnetic properties are used in loudspeakers and computer hard drives. Of these, 17 elements of the periodic table like Neodymium, Lanthanum and so on are found sparsely on the earth’s crust, and their supply is monopolized by the countries where their mines happen to be located.

S.N Bose Centre has used machine learning to search for cheaper substitutes of naturally occurring rare earth material. By painstakingly creating a database of rare earth compounds and their attributes and then constructing a machine-learning model, SNBNCBS researchers have predicted a list of potential candidates for permanent magnets whose cost will be less than $100 per Kg.

Machine learning model for core-shell bimetallic nanoclusters

SNBNCBS team successfully connected Machine Learning with nanoscience in tracing the mixing patterns of metal atoms in nanoclusters and formed a basis for the design map, which can help select the pairs of metals for nanocluster alloys. This design map developed by the scientists will be tested out in the nano laboratories at the Moscow State University and also at the S.N Bose Centre.

Besides, in an another study by SNBNCBS team, which is on the heterogeneous structure formed at the junction of two dissimilar semiconductors, they have established that using machine learning, hetero-structure types used in hetero-junctions of two semiconductors which are at the heart of devices like LEDs, solar cells and photovoltaic devices, can be predicted fairly accurately.

The ML model designed by the S.N Bose team predicted 872 unknown semiconductor hetero-structures of type 2 where the electrons and holes align themselves in A semiconductor and B semiconductor, respectively, giving rise to a desirable hetero-structure for semiconductor gadgets.

In their paper published in the Journal of Physical Chemistry, they investigated the key attributes driving the core−shell morphology using the statistical tool of machine learning applied on this large data set. Core-shell structures with lighter metals having lower atomic numbers in the core were classified as Type 1, and those having the heavier metals in the core were classified as Type 2. A number of attributes were built to characterise each data point in the set. The performance of the ML model was tallied with existing experimental data, and the ML model was proved to be reliable.