Harnessing and Controlling Quantum Randomness in Vacuum

Sunday - July 16 2023, 02:18 UTC - 1 year ago

Scientists at MIT have claimed that they can control the probability distributions linked to quantum randomness in a vacuum. This could have applications in various domains such as machine learning, artificial intelligence, data analysis, optimization and simulation. The team injected a low-intensity laser beam into an optical parametric oscillator to manipulate the probabilities linked to the output states of the OPO.

It's hard to think of a vacuum, as it's a place devoid of matter or light. But in the quantum world, this isn't true. Tiny changes or fluctuations exist in a vacuum, like ripples in water. These fluctuations are in electromagnetic fields, which can produce perfect randomness.

Scientists have previously used these fluctuations to generate random numbers. These vacuum fluctuations are also responsible for several phenomena, such as quantum tunneling, in which a quantum particle can pass through a potential energy barrier that would be classically impassable.

Now, scientists from the Massachusetts Institute of Technology (MIT) have claimed they can harness and control quantum randomness in optical systems.

The objective of MIT postdoctoral associates Charles Roques-Carmes and Yannick Salamin, who were part of the research team, was to develop a technique for controlling the probability distributions linked to quantum randomness. This is crucial for applications like weak field sensing and probabilistic computing.

Probabilistic computing .

In the quantum world, nothing is 100 certain, so we often deal with probabilities. Randomness plays a fundamental role due to the inherent probabilistic nature of quantum particles.

In conventional or classical computers, every task is deterministic, meaning it is performed step-by-step, and the outcome remains the same each time. Although this type of computing has led to the dawn of the digital age, it has some limitations.

Physical systems are complex and follow quantum mechanics, which involves randomness and uncertainty, which classical computers can't simulate. Probabilistic computing relies on statistical inference, stochastic processes, and probabilistic models to simulate and study phenomena involving randomness and in scenarios where multiple solutions exist, and exploring different possibilities can lead to better results.

It has applications in various domains, including machine learning, artificial intelligence, data analysis, optimization, and simulation.

Practically implementing probabilistic computing has been challenging due to the inability to control the probability distributions related to quantum randomness. It means manipulating or adjusting the probabilities associated with the outcomes of quantum random processes is difficult.

Injecting bias .

The MIT researchers achieved control by injecting a weak laser bias into an optical parametric oscillator or OPO, a system capable of generating random numbers. A weak laser bias refers to a low-intensity laser beam that they intentionally introduced.

By introducing this weak laser bias, the researchers could influence the quantum system and manipulate the probabilities linked to the output states of the OPO.

Even at very weak intensities similar to the amplitude of vacuum fluctuations, the bias field enabled the researchers to move between completely random outcomes and deterministic state selection.

In simple words, the researchers demonstrated that by adjusting the attenuation level of the bias field, they could contrally the statistical properties of the output state of the OPO.