Editorial Feature

Learning How to Control Quantum Randomness

A paper recently published in the journal Science demonstrated a new approach to realize controllable biased quantum randomness/fluctuations for the first time, unlocking the potential for ultra-precise field sensing and probabilistic computing.

quantum randomness

Image Credit: fujiwara_ss/Shutterstock.com

Importance of Vacuum Fluctuations for Controllable Randomness

Natural fluctuations of electromagnetic fields are one of the important insights of quantum field theory. Both magnetic and electric fields possess a nonzero variance even in the vacuum state, which leads to ubiquitous effects such as the Casimir effect, the Lamb shift, and spontaneous emission.

These vacuum fluctuations can be harnessed as a source of perfect randomness for several applications, such as to generate perfectly random photonic bits. However, several potential applications of quantum randomness in different fields, such as probabilistic computing, currently depend on controllable probability distributions and have not been realized on photonic platforms.

Probabilistic computing has received significant attention for accelerating the inference and optimization tasks while avoiding the implementation challenges of quantum computers.

Probabilistic computing systems leverage the inherent randomness of specific processes to perform computations and provide a number of possible outcomes, with every outcome having its associated probability.

Thus, these computing systems are suitable for simulating physical phenomena and tackling optimization problems where several solutions exist, and the exploration of different possibilities can result in a better solution.

A probabilistic bit (p-bit) is the central building block of probabilistic computing. The p-bit is a stochastic logical unit described using a controllable probability distribution. Vacuum fluctuations can be leveraged as a source of controllable randomness for photonic probabilistic computing as photonic analog platforms possess enhanced computational efficiency and greater speed.

However, most existing optical random bit generators depend on perfect symmetry to ensure unbiased outcomes from vacuum fluctuations. Additionally, the steady state is completely determined in externally seeded devices for nonlinear optical systems, which eliminates the perception of randomness.

Macroscopic injection-seeding mechanisms are used to enable single-mode operation and narrow bandwidth in lasers, optical parametric oscillators (OPOs), and microwave oscillators. The injection of extremely weak bias fields influences the turn-on time of lasers.

Thus, the simultaneous existence of complete determinism and perfect unbiased randomness regimes can provide a solution to harness electromagnetic fluctuations in a vacuum as a source of controllable biased quantum randomness.

A New Approach to Achieve Controllable Quantum Randomness

In this study, researchers proposed to inject extremely weak/vacuum level bias fields into a multistable optical system to realize a controllable biased quantum randomness source and demonstrated their concept in a biased OPO, where the random variable was the signal field phase that can take on values π and 0.

Bias fields with amplitudes similar to that of vacuum fluctuations were injected into the system to constantly interpolate between the perfect determinism and perfect unbiased randomness regimes. Controllable biased quantum randomness was realized for the first time in a nonlinear photonic system.

The two possible OPO output state probabilities were controlled by injecting less than one photon on average. The first photonic p-bit was generated with a parameter p that continuously tuned from zero to one through control over the bias phase and amplitude. This biased symmetry breaking was attributed to the interference between the vacuum fluctuations and the bias field, which led to biased randomness.

Probability measurements were performed in the system to translate the vacuum-level fluctuations into macroscopic observables that are easily manipulable to develop a new method to investigate the temporal profile of very weak electromagnetic pulses and vacuum field fluctuations. Moreover, the potential of the proposed approach was also displayed for sub-photon-level field sensing by reconstructing the temporal shape of fields below the single-photon level. 

Conclusion and Future Outlook

The proposed framework offers a simple approach to realize p-bits in nonlinear driven-dissipative quantum systems by harnessing zero-point fluctuations as a noise source. The photonic p-bit generation system generated 10,000 bits per second, with each of them following an arbitrary binomial distribution.

Thus, the framework can be implemented in multistable systems, such as integrated nanolaser arrays, to realize p-bits with extreme bandwidth by leveraging spatial multiplexing, non-trivial p-bit topologies using Hamiltonians H0 with higher-order symmetries, and emulate complex many-body Hamiltonians by engineering couplings between p-bits.

To summarize, the findings of this study demonstrated the effectiveness of the proposed framework to realize controllable biased quantum randomness and its potential for applications such as weak field sensing and probabilistic computing.

Specifically, the findings can assist in realizing macroscopic configurable probability distributions based on biased quantum vacuum fluctuations. Moreover, quantum-enhanced probabilistic computing also creates the possibility of simulating complex dynamics in areas such as lattice quantum chromodynamics and combinatorial optimization.

More from AZoQuantum: What to Know About Quantum Teleportation

References and Further Reading

Roques-Carmes, C., Salamin, Y., Sloan, J., Choi, S., Velez, G., Koskas, E., Rivera, N., Kooi, S. E., Joannopoulos, J. D., Soljačić, M. (2023). Biasing the quantum vacuum to control macroscopic probability distributions. Science. https://doi.org/10.1126/science.adh4920

Study demonstrates control over quantum fluctuations, unlocking potential for ultra-precise field sensing [Online] Available at https://phys.org/news/2023-07-quantum-fluctuations-potential-ultra-precise-field.html (Accessed on 21 July 2023)

Gabriel, C., Wittmann, C., Sych, D., Dong, R., Mauerer, W., Andersen, U. L., Marquardt, C., Leuchs, G. (2010). A generator for unique quantum random numbers based on vacuum states. Nature Photonics, 4(10), 711-715. https://doi.org/10.1038/nphoton.2010.197

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Samudrapom Dam

Written by

Samudrapom Dam

Samudrapom Dam is a freelance scientific and business writer based in Kolkata, India. He has been writing articles related to business and scientific topics for more than one and a half years. He has extensive experience in writing about advanced technologies, information technology, machinery, metals and metal products, clean technologies, finance and banking, automotive, household products, and the aerospace industry. He is passionate about the latest developments in advanced technologies, the ways these developments can be implemented in a real-world situation, and how these developments can positively impact common people.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Dam, Samudrapom. (2023, August 18). Learning How to Control Quantum Randomness. AZoQuantum. Retrieved on March 01, 2024 from https://www.azoquantum.com/Article.aspx?ArticleID=442.

  • MLA

    Dam, Samudrapom. "Learning How to Control Quantum Randomness". AZoQuantum. 01 March 2024. <https://www.azoquantum.com/Article.aspx?ArticleID=442>.

  • Chicago

    Dam, Samudrapom. "Learning How to Control Quantum Randomness". AZoQuantum. https://www.azoquantum.com/Article.aspx?ArticleID=442. (accessed March 01, 2024).

  • Harvard

    Dam, Samudrapom. 2023. Learning How to Control Quantum Randomness. AZoQuantum, viewed 01 March 2024, https://www.azoquantum.com/Article.aspx?ArticleID=442.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type
Submit
Azthena logo

AZoM.com powered by Azthena AI

Your AI Assistant finding answers from trusted AZoM content

Your AI Powered Scientific Assistant

Hi, I'm Azthena, you can trust me to find commercial scientific answers from AZoNetwork.com.

A few things you need to know before we start. Please read and accept to continue.

  • Use of “Azthena” is subject to the terms and conditions of use as set out by OpenAI.
  • Content provided on any AZoNetwork sites are subject to the site Terms & Conditions and Privacy Policy.
  • Large Language Models can make mistakes. Consider checking important information.

Great. Ask your question.

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.