They can control quantum randomness

A team of scientists at the Massachusetts Institute of Technology (MIT, in Boston) has reached a milestone in quantum technologies by demonstrating for the first time the quantum randomness control. The study is presented this week in the journal Science.

The authors focused on a unique feature of quantum physics known as vacuum fluctuations. You might think of a vacuum as space without matter or light. However, in the quantum world, even this “empty” space experiences fluctuations or changes. Imagine a calm sea that is suddenly filled with waves: this is similar to what happens in a vacuum on a quantum level.

Vacuum fluctuations once made the generation of random numbers possible and underlie many of the fascinating quantum phenomena discovered over the last hundred years.

These kinds of fluctuations once allowed scientists to generate random numbers. They are also responsible for many fascinating quantum phenomena discovered over the past hundred years.

Working with these vacuum fluctuations, MIT postdocs Charles Roques-Carmes It is Yannick Salamintogether with their teachers Marin Soljacic, john joannopoulos and other colleagues managed to control quantum randomness, an important advance for the so-called probabilistic computing.

Conventional x probabilistic computing

Conventionally, the Computers work deterministically, executing step-by-step instructions that follow a set of predefined rules and algorithms. In that scope, if you perform the same operation multiple times, you will always get exactly the same result. This approach has driven our digital age forward, but it has its limitations, especially when it comes to simulating the physical world or optimizing complex systems, tasks that often involve large amounts of uncertainty and randomness.

This is where the concept of probabilistic computing. These systems take advantage of the intrinsic randomness of certain processes to perform calculations. They don’t provide a single “correct” answer, but a series of possible outcomes, each with its associated probability. This makes them ideal for simulating physical phenomena and dealing with optimization problems where there may be multiple solutions and exploring different possibilities may lead to a better solution.

However, the practical application of probabilistic computing has historically been hampered by a major obstacle: the lack of control over the probability distributions associated with quantum randomness. It is here that the research carried out by the MIT team presents a possible solution.

Specifically, the researchers showed that injecting a weak laser “bias” on a optical parametric oscillatoran optical system that generates random numbers naturally, can serve as a controllable source of “biased” quantum randomness.

“Despite the extensive study of these quantum systems, the influence of a very weak polarized field has not been explored”, says Roques-Carmes, “our discovery of the controllable quantum randomness not only allows us to revisit decades-old quantum optics concepts, but also opens up possibilities in probabilistic computing and ultra-precise field detection.”

The team managed to manipulate the probabilities associated with the output states of an optical parametric oscillator, thus creating the first controllable photonic probabilistic bit (p-bit).

The team managed to manipulate the probabilities associated with the output states of an optical parametric oscillator, thus creating the first controllable photonic probabilistic bit (p-bit). Furthermore, the system showed sensitivity to the temporal oscillations of the ‘distorted’ field pulses, even well below the level of a single photon.

Roques-Carmes explains to SINC how they used the concept of ‘bias’ here: “As a metaphor, you can think of flipping a coin and measuring “heads” or “tails”. If the coin is perfectly balanced, the probability of getting heads or tails is 50%. Now, if there’s some kind of imbalance, you might get a different ratio p:(100-p) where p isn’t exactly 50. This is what we call a bias, or deviation, in the distribution; and in our experiment we fed it into a random number generator that uses quantum fluctuations as the source of randomness.”

Artist’s illustration of tunable random number generation from the quantum vacuum. /Lei Chen

“In our study – he highlights-, we advanced in the field of quantum physics by obtaining control of quantum randomness. Thanks to our work with vacuum fluctuations, we found a way to adjust for this randomness using weak laser “bias”. This step can have useful implications for probabilistic computing. We also believe this is the first time that a tunable or tunable source of quantum randomness has been generated.”

We believe this is the first time that a tunable or tunable source of quantum randomness has been generated.

Charles Roques-Carmes (MIT)

Probabilistic computing, which takes advantage of randomness to perform calculations, can be invaluable for simulating physical phenomena and complex optimization problems. However, the lack of control over quantum randomness has been a stumbling block. Our research could offer a way around this challenge, albeit modestly, by creating a photonic controlled probabilistic bit (p-bit).

On the other hand, the co-author Yannick Salamincomments: “Our photonic p-bit generation system currently allows us to produce 10,000 bits per second, each of which can follow an arbitrary binomial distribution. We expect this technology to evolve in the coming years, giving rise to photonic p-bits.” higher speed and a wider range of applications.

Professor Marin Soljačić concludes by highlighting the broader implications of the work: “By making vacuum fluctuations a controllable element, we are pushing the boundaries of what is possible in quantum enhanced probabilistic computing. The prospect of simulating complex dynamics in areas such as combinatorial optimization and so-called network quantum chromodynamics simulations are very exciting.”


Charles Roques-Carmes et al. “Influencing the quantum vacuum to control macroscopic probability distributions”. Science2023.

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