Quantum Random Numbers
PreviousLow-cost transaction feesNextThe Post Quantum Network is Building a Quantum-Resistant Future with XMSS
Last updated
Last updated
Random number generation is a process by which a sequence of numbers or symbols that cannot be reasonably predicted is generated.
Before the advent of quantum security, and even today in most scenarios, random numbers are pseudorandom. A pseudorandom number generator (PRNG) is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed. PRNGs are central in applications such as simulations, electronic games, and cryptography.
Quantum random number generators use principles of quantum mechanics as a source of entropy. By doing so, they are able to provide true random numbers. There are many different methods of using quantum physics principles as an entropy source.
QRNG represents the system that satisfies the single random quantum effect of resetting to the initial settings after system value measurements. Due to the laws of quantum physics, each measurement with identical initial conditions and the same measurement mode provides different values. Therefore, such a system has a broad application of random number generators where the randomness of measured values is highly desirable.
Such systems include the smallest units such as electrons (smallest quantity of charge) or qubits (smallest quantity of information). A single quantum of light (photon) can be used as qubit carrier which is favorable due to laws of quantum mechanics that prevent making a faithful qubit's copy. In the early development of QRNGs, schemes based on measuring qubit states were widely adopted due to theoretical simplicity. A qubit cannot be split, copied or amplified without introducing detectable disturbances and it can be represented as a linear combination of two basic states (horizontal and vertical).
Parameters α and β are probability amplitudes: the probability that the outcome of the measurement will be a vertical or a horizontal base, respectively. Unlike the classical bit, which can only have two possible values, 0 or 1.
RNGs that rely on quantum processes (QRNGs) offer guaranteed indeterminism and entropy, since quantum processes are intrinsically random.
True-randomness is based on non-numeric techniques. One intriguing aspect of quantum mechanics is that properties of a particle are not determined with arbitrary precision until one measures them; consequently, the individual result of a measurement contains an inevitable intrinsic random component. This characteristic of quantum theory provides fundamental randomness that can be used for generating true random numbers.
Quantum mechanical random numbers are derived from the fundamental principles of random processes from quantum mechanics. Due to the laws of quantum physics, each measurement with identical initial conditions and the same measurement mode provides different values.
In terms of unpredictability, a stream of Quantum random numbers exhibits two forms:
Forward unpredictability: If the seed is unknown, the next output bit in the sequence should be infeasible to predict, regardless of any knowledge of previous bits in the sequence.
Backward unpredictability: It should also not be feasible to determine the seed from knowledge of any generated values. No correlation between a seed and any value generated from that seed should be evident; each element of the sequence should appear to be the outcome of an independent random event whose probability is 50%.
In contrast to deterministic random number generators that generate random values with entropy limited by the entropy of the initial seed, PostQuantum.Network uses non-deterministic random number generators that rely on the quantum state of matter for generation of truly random numbers. Quantum physics is fundamentally random in nature, as confirmed by theory and experimental research.
PostQuantum.Network uses a laser-based quantum source to generate randomness for its cryptography, hashing, and digital signatures. It is a highly sophisticated engineering innovation involving complex deep-tech technologies such as semiconductors, optoelectronics, high-precision electronics, and quantum physics working together to create the highest level of randomness possible.
Photon Generation
A laser produces a stream of elementary particles called photons. These photons are used to generate random numbers.
Photons, unlike classical objects, are unpredictable under certain situations. When incident on a semi-transparent mirror, a photon has a 50/50 chance of being reflected or transmitted. The photon is then in a superposition of both states (reflected and transmitted), i.e., the photon exists in both states simultaneously. Upon measurement, it collapses to one of these states, which is intrinsically random and there is no way to predict which state the photon will collapse to. This gives the inherent randomness from the photons, which cannot be influenced by any external parameters.
Photons to high entropy random numbers
The process starts with the generation of light from a laser source, which is converted into a single-photon level using attenuators. The photons are then sent onto a semi-transparent mirror for the superposition phenomenon and are detected using SPD (Single Photon Detector). They are then converted into bits of 1's and 0's, depending on the clicks generated on the SPD. Then there is post-processing in FPGAs to do the conditioning, and statistical checks, and then deliver the random numbers to the outside world.
The test suites check the randomness of the bits. Only if the conditions are met are they forwarded to the PostQuantum.Network nodes, PostQuantum.Network wallet and D-Apps deployed in PostQuantum.Network.
PostQuantum.Network's unparalleled Quantum Random Number Generators (QRNGs) leverage the random properties of quantum physics to generate a true source of entropy, improving the quality of seed content for key generation.
The source of randomness is unpredictable and controlled by quantum processes.
The entropy source tends to produce true random output.
Live/real-time monitoring of the entropy source is possible and highly effective.
All attacks on the entropy source are detectable.
These factors indicate that our QRNG is provably secure.
PostQuantum.Network's QRNGs embed elementary components that can be easily monitored to detect any failure or attacks.
Environmental perturbations can be ruled out by simple health checks, guaranteeing QRNG always produces high-quality entropy.
PQCN-Chain's implementation of QRNG technology ensures robust security measures, making it a leader in quantum-resistant blockchain solutions.
| Property | Traditional/Classical | Quantum |
|---|---|---|
Entropy Source
Randomness based on complexity of process and partial ignorance.
Fundamental randomness.
Ease of certification
Limited ability to certify the underlying physical process, which is inherently complex. Certification of the output quality based on standard tests.
Can validate the underlying physical processes. Certification of the output quality based on standard tests.
Resistance to tampering
Some ability to run health check on entropy source.
Built-in check based on simplicity of process and more sensitive to tampering. Device-independent versions offer highest resistance against tampering of entropy source itself, even by the providers.
Quality of entropy
Various degrees. The underlying process used as entropy source may work in a physical regime where there are large bias and relatively high correlations (that is, small entropy)
High entropy from the start based on the simple design of the source; a QRNG entropy source can be argued to be very close to i.i.d. from the start.
Speed
Can be very high, and several sources may be combined to obtain higher rates.
High, also because of the quality of the initial entropy, but device-independent implementations may be slow, for example.
Size
Can be very small and embedded on chip, e.g.: exploiting a randomness source like thermal noise.
Varies substantially, going from embeddable in smartphones to room-size dimensions for implementing device-independent randomness generation based on non-locality.
