We’ve come a long way from the humble 5MHz.
In the last decade, computers have quadrupled their performance in terms of raw power. Thanks to advancements in their architecture and manufacturing processes, CPUs have seen impressive efficiency built-in.
From the 5MHz to the Intel 8086, computers have made quantum leaps forward.
Feature | Intel 8086 (1978) | Intel Core i9-13900K (2024) |
Release Date | 1978 | 2024 |
Clock Speed | 5 MHz | Up to 5.8 GHz |
Architecture | 16-bit | 64-bit |
Transistor Count | 29 | Approx. 14.4 billion |
Process Technology | 3 µm | 10nm |
Core Count | 1 core | 24 cores (8 Performance + 16 Efficiency) |
Threads | 1 thread | 32 threads |
These improvements have been made possible thanks to a concept known as Moore’s Law. It is an observation made by Intel’s co-founder, Gordon Moore, who in 1975 predicted that every two years, the number of transistors in integrated circuits would double.
However, as transistors keep shrinking to atomic scales, limitations begin to appear. As researchers and manufacturers push the boundaries of physics, we are reaching a point where downscaling transistors is no longer an option.
To overcome the limitations of traditional transistor-based processors, engineers are exploring alternative technologies, such as Quantum Computing, where binary 0s and 1s can take a near-magical turn.
Quantum Computing Basics
Traditional computers use bits as their smallest unit of data, which could be either a 0 or a 1.
Quantum computing, on the other hand, uses quantum bits, also known as qubits. This new unit has a fundamentally different way of working, compared to a bit. Qubits can be a 0 and a 1 at the same time, due to a quantum property called superposition.
This ability enables Quantum Computers to solve complex problems exponentially faster than traditional ones, as they can perform calculations in parallel.
Another key concept in quantum computing is entanglement, where qubits are correlated in a particular way, so the state of one of them is dependent on the other. Notably, quantum computing doesn’t take into account distance, and can allow dependency even if qubits are separated by immense distances.
Jargon aside, essentially thanks to all these new properties, quantum computers are simply astronomically fast computers. They can make computations at the metaphorical speed of light, rendering your new MacBook Pro with 32GB RAM, and an M3 chip, looking like a horse and a carriage of the digital age.
The first publicly known demonstration of a quantum algorithm took place in 1998, at Oxford University. A 2-qubit working Quantum Computer was used to solve Deutsch's problem , and later that year a 3-qubit computer was born (today there are 1,000+ qubits Quantum Computers).
But what really made quantum computing take a quantum leap was Shor’s Algorithm, published by Peter Shor in 1994. Shor’s algorithm allows current cryptographic algorithms -like those widely used in cryptocurrencies- to potentially break.
Quantum Computing has several challenges
One of the main concerns with Quantum Computing is its potential to render useless current public-key cryptographic algorithms, such as the RSA algorithm.
The RSA algorithm relies on a mathematical property that states that it’s easy to calculate the product of two large prime numbers, but extremely difficult to factor the product back into its original prime factors.
This property is known as the difficulty of factoring and forms the basis for RSA encryption. Notably, RSA encryption was first published in 1977, and continues to be the cryptographic algorithm most used by mainstream banks today.
ECC (Elliptic Curve Cryptography) is another public-key cryptographic algorithm, which is used by Bitcoin. It offers users the ability to generate a public key. Unlike RSA, which relies on the difficulty of factoring large numbers, ECC relies on the difficulty of finding discrete logarithms on elliptic curves.
A Bitcoin private key is a randomly generated 256-bit number (between 1 and 2²⁵⁶).
Finding the private key while knowing the public key is nearly impossible due to how difficult it would be to guess a random 256-bit number. The possible private key combinations are comparable to the number of atoms in the observable universe, multiplied by 77.
In other words, and for our still nascent human minds, a number that we can hardly begin to comprehend.
For example, it would take a modern computer 260,000,000,000,000,000,000,000,000,000 (260 billion billion billion billion billion billion billion billion) years to guess the number. Odds exist, but they are virtually zero.
However, Shor’s Algorithm is a quantum algorithm tailor-made to efficiently find the prime factors of an integer (kind of “bending” traditional math with this new quantum computing technology).
With a Quantum Computer sufficiently powerful, applying the algorithm would not only threaten crypto security, but also online banking, communications, and every transaction we secure under public-key cryptographic algorithms, such as RSA and ECC.
How experts are overcoming these challenges
Experts are actively developing post-quantum cryptography (PQC) and transitioning systems, establishing new computational standards. These algorithms are designed to be secure against both classical and quantum computing attacks.
There are several promising quantum-resistant algorithms:
Lattice-based cryptography: Cryptographic algorithms that are based on mathematical problems related to lattices. The main lattice-based cryptographic algorithm is NTRU, which is based on the hardness of the shortest vector problem (SVP) in lattices. NTRU is a public-key cryptosystem that can be used for encryption and digital signatures.
Code-based cryptography: Cryptographic algorithms that are based on error-correcting codes. The main code-based cryptographic algorithm being developed is the McEliece cryptosystem, which uses the difficulty of decoding linear codes to achieve security.
Isogeny-based cryptography: A class of cryptographic algorithms that are based on the study of isogenies in elliptic curves. The main isogeny-based cryptographic algorithm being developed is the Supersingular Isogeny Diffie-Hellman (SIDH).
Essentially, these 3 categories are part of advanced mathematics being applied in very ingenious ways to achieve goals in terms of engineering security. There are also hybrid approaches that combine classical and quantum-resistant algorithms to ensure a smoother, and more secure transition.
The Garbling Circuits solution
One approach that is overcoming the challenges presented by Quantum Computing in a more secure and efficient way: Garbled Circuit.
It allows for encryption to take place between two mistrusting parties that can validate information without knowing each other's input or any middle calculations.
COTI V2's approach uses Garbling Circuits to enhance the security of on-chain data management.
The garbling scheme at the heart of the protocol relies on applying a pseudo-random function (PRF) like AES to circuit wires' labels. Additionally, they use a symmetric encryption scheme for on-chain ciphertexts, ensuring data remains encrypted and secure.
Basically, achieving the same or even better results but in a way more private manner for both parties.
To protect against quantum attacks (e.g., Shor’s Algorithm), the key length of PRFs is doubled from 128 to 256 bits. Enhanced security measures are being developed to make on-chain ciphertexts quantum-safe.
Advantages of Garbling Circuits:
Confidential Data Management: Sensitive data is stored and processed securely on-chain.
Privacy Preservation: Authorized parties can query and analyze encrypted data without compromising confidentiality.
Multi-Party Computation (MPC): Allows multiple parties to jointly compute a function over their inputs while keeping those inputs private.
Their approach enhances security by mitigating risks associated with centralized data storage, defining precise rules for data access, and ensuring that only authorized personnel can view sensitive information.
On-chain data cannot be altered or deleted once it’s stored, a crucial advantage for legal, medical, defense, and regulatory applications.
COTI V2 Garbled Circuits solution might be the only solution cryptocurrency systems have to face the challenges posed by quantum computing.