Quantum Monte Carlo

The Quantum Montecarlo Service provides you with a batch of random numbers within the range you specify.


Easily generate Monte Carlo simulations using Quantum Random Numbers.

The Quantum Monte Carlo Service provides you with a batch of random numbers within the range you specify.

Benefits of Quantum Monte Carlo vs. Classic

  • Most of Classic RNGs used for Monte Carlo method are Pseudo Random Number Generators (PRNG)
  • The quantum source of randomness is unpredictable and controlled by quantum process.
  •  The entropy source tends to produce true random output.
  • Live/ real-time monitoring of entropy source is possible and highly effective as well.
  • All attacks on the entropy source are detectable.

Use Cases

Physical sciences

Monte Carlo methods are very important in computational physics, physical chemistry, and related applied fields, and have diverse applications from complicated quantum chromodynamics calculations to designing heat shields and aerodynamic forms as well as in modeling radiation transport for radiation dosimetry calculations.


Monte Carlo methods are widely used in engineering for sensitivity analysis and quantitative probabilistic analysis in process design. The need arises from the interactive, co-linear and non-linear behavior of typical process simulations. For example:

  • Microelectronics engineering
  • Geostatistics and geometallurgy
  • Wind energy yield analysis
  • Fluid dynamics
  • Autonomous robotics
  • Telecommunications, when planning a wireless network
  • Reliability engineering
  • Signal processing and Bayesian inference
  • Groundwater modeling

Climate change

The Intergovernmental Panel on Climate Change relies on Monte Carlo methods in probability density function analysis of radiative forcing.

Computational biology

Monte Carlo methods are used in various fields of computational biology, for example for Bayesian inference in phylogeny, or for studying biological systems such as genomes, proteins, or membranes.

Computer graphics

Path tracing, occasionally referred to as Monte Carlo ray tracing, renders a 3D scene by randomly tracing samples of possible light paths.

Applied statistics

In applied statistics, Monte Carlo methods may be used for at least four purposes:

  • To compare competing statistics for small samples under realistic data conditions.
  • To provide implementations of hypothesis tests that are more efficient than exact tests such as permutation tests (which are often impossible to compute) while being more accurate than critical values for asymptotic distributions.
  • To provide a random sample from the posterior distribution in Bayesian inference. This sample then approximates and summarizes all the essential features of the posterior.
  • To provide efficient random estimates of the Hessian matrix of the negative log-likelihood function that may be averaged to form an estimate of the Fisher information matrix.

Artificial intelligence for games

Monte Carlo methods have been developed into a technique called Monte-Carlo tree search that is useful for searching for the best move in a game. Possible moves are organized in a search tree and many random simulations are used to estimate the long-term potential of each move. A black box simulator represents the opponent’s moves.

Finance and business

Monte Carlo simulation is commonly used to evaluate the risk and uncertainty that would affect the outcome of different decision options.

Monte Carlo simulation allows the business risk analyst to incorporate the total effects of uncertainty in variables like sales volume, commodity and labour prices, interest and exchange rates, as well as the effect of distinct risk events like the cancellation of a contract or the change of a tax law.

Monte Carlo methods in finance are often used to evaluate investments in projects at a business unit or corporate level, or other financial valuations. They can be used to model project schedules, where simulations aggregate estimates for worst-case, best-case, and most likely durations for each task to determine outcomes for the overall project.

Monte Carlo methods are also used in option pricing, default risk analysis.

Additionally, they can be used to estimate the financial impact of medical interventions.


  • Easy-to-use SaaS endpoint
  • Request from 32 bits to 1MB of random data per request
  • Online service based on True Random Numuber Generators that relay on hardware components using Photonic Integrated Chips (PICs).

Additional information

Sustainability focus


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