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**Groundbreaking research demonstrates control over quantitative variables, the most open computing possible and the ultra-precise in space.**

A group of researchers from the Massachusetts Institute of Technology (TOGETHER) reached the level of quantum mechanics, demonstrating for the first time the control of quantum randomness.

A group of researchers focused on a special phenomenon of quantum physics known as “vacuum fluctuations.” You can think of a vacuum as a completely empty space without matter or light. However, in the quantum world, this “empty” space experiences fluctuations or changes. Imagine a calm sea suddenly experiencing waves – that’s the same as what happens in a vacuum at the quantum level. In the past, this flexibility has allowed scientists to create extraordinary numbers. They are also responsible for many interesting discoveries made by quantum scientists over the past hundred years.

The results were recently published in the journal *Science*, a paper led by MIT postdoctoral associates Charles Roques-Carmes and Yannick Salamin; MIT professors Marin Soljačić and John Joannopoulos; and colleagues.

#### Computing in a New Light

Normally, computers work in a deterministic way, executing step-by-step instructions that follow a set of predefined rules and algorithms. In this paradigm, if you run the same task many times, you will always get the same results. This modeling approach has enabled our digital age, but it has its limitations, especially when it comes to simulating the virtual world or optimizing complex systems, tasks that often involve large amounts of uncertainty and randomness.

This is where the concept of probabilistic computing comes in. Probabilistic computing systems propose the inherent randomness of certain systems to perform computations. They don’t just offer one “right” answer, but a range of possible outcomes each associated with probability. This makes them well-suited to modeling and dealing with optimization problems where multiple solutions may exist and where the exploration of multiple possibilities may lead to a better solution.

#### Overcoming Quantum Challenges

However, the practical implementation of probabilistic computing has historically been hindered by an important obstacle: the lack of control over the probability distribution associated with randomness. However, research by the MIT team has shed light on a possible solution.

In particular, researchers have shown that injecting a weak laser “bias” into a parametric oscillator, an optical system that naturally produces random numbers, can act as a controlled source of “biased” quantum randomness.

“Despite the extensive study of these quantum systems, the influence of the weakest part has not been explored,” said Charles Roques-Carmes, a researcher in the study. “Our discovery of controlled quantum randomness not only allows us to revisit decades-old concepts in quantum optics but also unlocks the potential for quantum computing and ultra-precise space detection.”

The team successfully demonstrated the ability to use probabilities associated with the output of a parametric optical oscillator, thus creating the first controllable photonic probabilistic bit (p-bit). In addition, the system showed sensitivity to transient oscillations of bias field pulses, even less than a single one. photon level.

#### Future Impacts and Resources

Yannick Salamin, another member of the team, says, “Our photonic p-bit production system now allows the production of 10,000 bits per second, each of which can follow a binomial distribution. We expect this technology to emerge in the next few years, leading to a standard of photonic p-bits and a wide range of applications.”

Professor Marin Soljačić from MIT emphasizes the broad implications of the work: “By making vacuum dynamics controllable, we are pushing the limits of what is possible in quantum-enhanced computing.” The prospect of measuring dynamic dynamics in areas such as coupling and lattice quantum chromodynamics is very exciting. “

Reference: “Tendency to quantum vacuum control of large probability distributions” by Charles Roques-Carmes, Yannick Salamin, Jamison Sloan, Seou Choi, Gustavo Velez, Ethan Koskas, Nicholas Rivera, Steven E. Kooi, John D. Joannopoulos and Marin, Soljači Nge- 13 July 2023, *Science*.

DOI: 10.1126/science.adh4920

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