Author: Hope

Hello! I'm Hope Alemayehu, a passionate quantum software developer and an A2SV Trainee with hands-on experience in developing software solutions and writing articles related to Quantum Computing. 

Quantum Random Number Generators: Ushering in a New Era of Mobile Security

Posted on by Hope

The latest innovation in Samsung’s mobile lineup, the Samsung Galaxy Quantum 5, introduces Quantum Random Number Generators (QRNGs) as a key feature for enhanced security. QRNGs leverage the inherent unpredictability of quantum mechanics to generate truly random numbers, which are crucial for encryption, authentication, and secure communications.

But what does this mean for everyday users? How does QRNG technology differ from the methods traditionally used in smartphones, and what role does it play in securing your personal data on the Galaxy Quantum 5? Let’s explore the mechanics of this advancement and compare QRNG with more conventional approaches.

Traditional RNG vs. Quantum RNG: What’s the Difference?

Most digital systems today, including smartphones, rely on Pseudo-Random Number Generators (PRNGs) for generating random numbers. PRNGs use algorithms and an initial seed value to produce sequences that appear random. However, because they are deterministic-algorithm-based, PRNGs are deterministic—meaning that if you know the seed or the algorithm, you can predict the sequence of numbers.  Due to the nature of the algorithm, it’s very hard to actually predict what a deterministic random number is going to do, but in principle it’s possible if you set a large enough computer at the task for a long enough time.

Here’s a basic example of a PRNG in Python:

python

import random

# Seed the PRNG

random.seed(42)

# Generate a pseudo-random number

prng_number = random.randint(0, 100)

print(f”Pseudo-Random Number: {prng_number}”)

In contrast, Quantum Random Number Generators (QRNGs) use quantum phenomena to produce truly random numbers. QRNGs generate numbers based on the random behavior of quantum particles like photons, making them inherently unpredictable and non-reproducible. This provides a higher level of security for cryptographic purposes.

How Does QRNG Work in the Galaxy Quantum 5?

At the heart of Samsung’s Galaxy Quantum 5 is a QRNG chipset developed by ID Quantique. This chipset, measuring just 2.5mm x 2.5mm, is currently the world’s smallest QRNG. It works by detecting random quantum states of photons to generate truly random numbers, which are then used to create encryption keys and protect sensitive data.

Here’s a simple look at how a QRNG works in practice:

python

from qiskit import QuantumCircuit, transpile, Aer, execute

# Create a Quantum Circuit with one qubit

circuit = QuantumCircuit(1, 1)

# Apply a Hadamard gate to the qubit to put it in a superposition

circuit.h(0)

# Measure the qubit’s state

circuit.measure(0, 0)

# Use AerSimulator to execute the circuit

simulator = Aer.get_backend(‘aer_simulator’)

compiled_circuit = transpile(circuit, simulator)

result = execute(compiled_circuit, simulator, shots=1).result()

# Get the random bit (either 0 or 1)

random_bit = result.get_counts()

print(random_bit)

This code uses a quantum circuit to generate a truly random bit. In this example, the Hadamard gate is applied to a qubit to create a superposition—meaning the qubit exists in both the 0 and 1 states at the same time. When measured, the qubit collapses into either 0 or 1, generating a truly unpredictable bit.

In the quantum approach – unless the laws of physics as we currently understand them are wrong in some significant and relevant way – there is in principle no way for anyone to predict what numbers will be randomly generated.   The laws of physics say there is no pattern.

In the Galaxy Quantum 5, this principle is applied on a larger scale, with the QRNG generating random numbers that are used for encryption, authentication, and securing data in applications like mobile banking, social media, and external storage.

Credit: Tesfu Assefa

Samsung Galaxy Quantum Series Comparison

The Samsung Galaxy Quantum series has progressively integrated QRNG technology to enhance mobile security. The Galaxy Quantum 5, the latest in the series, features a smaller, more advanced QRNG chipset compared to its predecessors. While the earlier models like the Galaxy Quantum, Quantum 2, and Quantum 4 introduced QRNG technology in various capacities, the Galaxy Quantum 5 expands its application significantly.

Compared to the previous models, the Galaxy Quantum 5 boasts a QRNG chipset that is not only smaller (2.5mm x 2.5mm) but also includes enhanced encryption capabilities and a Quantum Indicator feature. This indicator notifies users when an application is using quantum-secured services, providing additional transparency. The Galaxy Quantum 5 supports a wider range of QRNG-based apps, including those for games and service applications, and offers improved encryption for external memory.

The earlier models, such as the Galaxy Quantum and Quantum 2, provided basic QRNG-based encryption, with limited app support. The Quantum 4 introduced QRNG-based encryption for external memory, but it was the Galaxy Quantum 5 that expanded the scope of QRNG applications, including enhanced authentication and encryption of information.

Applications in the Samsung Galaxy Quantum 5

The Galaxy Quantum 5 integrates quantum technology deeply into its security features. Partnered with Samsung Knox, the device uses QRNG to enhance protection in various ways:

  • Authentication: Whenever you use biometric data like fingerprints or facial recognition to log in, the encryption keys used to protect this data are generated by QRNG, ensuring they can’t be predicted or replicated.
  • Secure Payments and Banking: QRNG provides extra layers of security for financial apps and mobile banking, protecting sensitive information like payment details and banking credentials.
  • Social Media and Gaming: With QRNG technology, even apps unrelated to finance—like social media and games—can take advantage of quantum-enhanced security, ensuring your data is safe during login and in-app transactions.

Additionally, a “quantum indicator” feature alerts users when QRNG is actively securing an application, adding transparency and peace of mind.

What’s Next for Quantum Technology in Smartphones?

The Samsung Galaxy Quantum 5 represents a significant advancement in mobile security through its integration of QRNG technology. As quantum technology continues to evolve, we may see more devices adopting QRNG and other quantum-based solutions, offering stronger protection for personal data.

For now, the Galaxy Quantum 5 is primarily available in South Korea, but it sets a new standard for mobile security. The introduction of QRNG technology could pave the way for broader adoption of quantum-enhanced security in mobile devices worldwide.

Conclusion

The Samsung Galaxy Quantum 5 is a notable step forward in mobile security with its use of Quantum Random Number Generator (QRNG) technology. This device provides an additional layer of protection against potential security threats, especially in high-risk applications like mobile banking and secure communications.

For those interested in exploring the technology further, including a web-based implementation of QRNG, you can find the complete project on my GitHub: https://github.com/Hope-Alemayehu/trulyRandom.

Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter. 

Quantum Bitcoin Mining: The Future of Cryptocurrency?

Posted on by Hope

Spoiler Introduction

Ah, the apocalyptic headlines: “Quantum computers will break the blockchain and destroy Bitcoin!” But fear not, dear readers, for we’re not quite there yet. In fact, we’re still a ways off from having the necessary 4 million qubits to pose a real threat to the blockchain. So, let’s take a deep breath and explore a more exciting application of quantum computers: Quantum Bitcoin mining.

What’s Bitcoin Mining, Anyway?

Before we dive into the quantum stuff, let’s cover the basics. Bitcoin is a digital currency that uses classical cryptographic technologies to secure transactions. The blockchain is a public ledger that stores all these transactions, divided into blocks. Miners compete to solve complex mathematical problems to validate these blocks and add them to the chain. It’s a bit like solving a giant puzzle, but with more computers and less actual puzzle pieces.

The Blockchain: A Chain of Blocks

Think of a blockchain as a chain of blocks, each one connected to and affecting the others, sharing the weight. Each block contains a list of transactions. When a new transaction occurs, it’s broadcast to the entire network. Miners collect these transactions into a block and add this block to the blockchain. Each block references the previous one, creating a secure and chronological order of transactions. This setup ensures that once a block is added, it’s incredibly difficult to alter the information without changing all subsequent blocks, providing the security and immutability that blockchain technology is known for.

Nonces and Hash Functions: The Key to Mining

The key aspects of Bitcoin mining are nonces and hash functions.

  • Nonces: These are arbitrary numbers that miners tweak to find a hash value that meets the target difficulty. Imagine nonces as the secret ingredient in your grandma’s cookie recipe that you keep adjusting until the cookies come out perfect.
  • Hash Function (SHA-256): This function takes an input and generates a 256-bit output. No matter how many times you input the same data, the output will always be the same. However, even a tiny change in the input will produce a vastly different output. It’s a bit like a magical blender where putting in different fruits always gives you a unique smoothie, but you can’t reverse-engineer the smoothie to get back the original fruits.

The Goal: Finding the Right Nonce

Miners aim to find a nonce that, when combined with the data in the block and passed through the hash function, produces a hash that meets a certain target – usually a hash with a specific number of leading zeros (PoW). This process is like playing a massive game of slot machines, where you pull the lever (change the nonce) over and over, hoping to hit the jackpot (the correct hash value).

Classical Mining: A Computational Nightmare

In classical mining, miners iterate through a massive search space to find the right combination of nonce values that satisfy the proof-of-work conditions. This is a computationally costly problem, which is why miners use high-powered machines specifically designed for this task. The total complexity of this operation is a whopping O(2^256/t), where t is the time it takes to perform the calculation. Imagine trying to find a needle in a haystack, where each strand of hay represents a possible nonce. Now imagine that haystack is the size of the sun – that’s the scale miners are working with!

Credit: Tesfu Assefa

Enter Quantum Algorithms

This is where quantum computers come in. Quantum algorithms like Grover’s algorithm can search this vast space much faster, thanks to the power of superposition and parallel processing.

Quantum Superposition and Parallelism

Quantum computers leverage superposition, where qubits can exist in multiple states simultaneously. This is unlike classical bits, which are strictly either 0 or 1. It’s as if you could be in multiple places at once, doing multiple tasks. This allows quantum computers to process a vast number of possibilities simultaneously, rather than sequentially as classical computers do.

Grover’s Algorithm

Grover’s algorithm is a quantum algorithm that provides a quadratic speedup for unstructured search problems. In the context of Bitcoin mining, it can theoretically reduce the time needed to find the right nonce significantly. Instead of searching through all possible nonces one by one, Grover’s algorithm allows us to find the correct one in roughly the square root of the number of possibilities.

The Reward: New Bitcoin

Assuming we find the right nonce, what happens next? When a miner successfully solves the puzzle, they broadcast the block to the network, and other miners verify the solution. The winning miner is rewarded with new Bitcoin (currently 3.125BTC per block as of April, 2024), plus transaction fees from the transactions included in the block. This reward system incentivizes miners to keep participating and securing the network.

The Current State of Quantum Bitcoin Mining

So, where are we now? The short-term impact of quantum computers on Bitcoin is likely to be minimal. For quantum mining, we need extremely fast quantum hash rates, which are still a ways off.

Quantum Hardware Limitations

Quantum computers are still in their infancy. Current quantum computers, like those developed by Google and IBM, have achieved around 100 qubits. However, to outperform classical miners and pose any significant impact on Bitcoin mining, we would need millions of qubits, operating with low error rates. This level of quantum hardware is still many years, if not decades, away.

Potential Vulnerabilities

There is a vulnerability in pending transactions due to elliptical curve cryptography, which is used in Bitcoin’s public key infrastructure. Quantum computers could theoretically break this cryptography, allowing them to alter transactions before they are confirmed. However, the Bitcoin community is already aware of this and is researching quantum-resistant cryptographic algorithms to mitigate this risk.

Stability and Adoption

The mere possibility of quantum computers existing could potentially destabilize Bitcoin. Investors might be wary of the security implications, and this uncertainty could affect Bitcoin’s value. However, until quantum computers are practically feasible and scalable, this remains a theoretical concern rather than an immediate threat.

Conclusion

In conclusion, Quantum Bitcoin mining is an exciting development that could revolutionize the way we mine cryptocurrency. While we’re not quite there yet, the potential benefits are undeniable. With the right quantum algorithms and hardware, we could see a significant increase in mining efficiency. So, let’s keep an eye on this space and see where it takes us. Who knows? Maybe one day we’ll be mining Bitcoin with the power of quantum computers.

Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter. 

Wavefunction Matching: A Breakthrough in Quantum Calculation Accuracy

Posted on by Hope

Introduction

Picture a world where we can peer into the tiniest particles that make up our universe, understanding their behaviors and interactions with stunning precision. This is the realm of quantum physics, a field that delves into the mysteries of protons, neutrons, and other minute particles known as “quantum many-body systems.” To unlock these secrets, scientists prefer to use an approach called “ab initio,” which means starting from the most basic principles and using them to derive the behavior of the system. Within this approach, the Quantum Monte Carlo Simulation plays a crucial role, using randomness to help make sense of complex calculations, via simulating a large number of possible ways a system may evolve and probabilistically combining the results.  However, in many applications this technique has faced a major technical challenge: the “sign problem,” 

This issue arises when positive and negative results offset each other, leading to erroneous conclusions. To tackle this challenge, researchers devised a solution known as “Wavefunction matching.” Imagine a complex puzzle simplified into a more manageable form—this is akin to what Wavefunction matching accomplishes. It involves mapping the intricate problem onto a simpler model devoid of oscillations, and any discrepancies are resolved using perturbation theory. By streamlining the problem and addressing differences, accurate calculations become feasible.

Applying this technique, researchers successfully computed the masses and radii of all nuclei up to a mass number of 50, aligning closely with real-world measurements. Furthermore, the utility of Wavefunction matching extends beyond rectifying Monte Carlo Simulations—it holds promise in the realm of quantum computing. By enhancing calculation accuracy and reliability, it serves as a valuable tool for understanding how nature actually works and applying those insights to problems in chemistry, physics, and quantum computing.

As Dr. Meißner suggests, this method’s versatility spans both classical and quantum computing domains, ushering in a new era of precision and reliability in calculations. This advancement isn’t just confined to rectifying existing methodologies; it holds the potential to revolutionize quantum algorithms, paving the way for swifter computations and superior outcomes across various quantum applications.

Credit: Tesfu Assefa

Conclusion

Richard Feynman said, ‘Any field for which there is a prize that is defined is a field that already has its best days behind it. It’s a field that barely has a name and is going to have the most fertile moment.’  Wavefunction matching is a good example of such a fertile moment in a research area still in its most exciting, early, innovative phase.

Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter.