Skip to content

Updating age-old viewpoints through a quantum lens: Classic principle revamped for contemporary perspectives

Research reveals construction method for authentic quantum Bayes' rule, providing a fresh basis for the frequently employed Petz map.

Redefining the Approach to Updating Beliefs: A Quantum Twist on an Ancient Rule from 250 Years Ago...
Redefining the Approach to Updating Beliefs: A Quantum Twist on an Ancient Rule from 250 Years Ago is Given a Contemporary Revamp

Updating age-old viewpoints through a quantum lens: Classic principle revamped for contemporary perspectives

In a groundbreaking study published in the prestigious journal Physical Review Letters, a team of researchers has proven the existence of a quantum version of Bayes' rule, a 250-year-old formula that has been instrumental in making smarter guesses in classical probability.

The study, authored by Rupendra Brahambhatt, presents a quantum counterpart to Bayes' rule that could guide how algorithms update their models when fed quantum data in quantum machine learning.

The quantum Bayes' rule provides a recipe for updating quantum beliefs, offering a new foundation for the Petz map, a formula often seen as the quantum analogue of Bayes' rule. The Petz map is a systematic way of trying to reverse the effect of a quantum process and recover information in line with the rules of quantum probability.

Interestingly, the researchers' derived rule for updating quantum states turned out to be the same as the Petz map, a formula introduced in the 1980s. This discovery gives the Petz map a clear and principled role as the quantum counterpart to Bayes' rule.

In quantum physics, instead of talking about simple probabilities, scientists talk about quantum states. When you measure a quantum system, you see only one outcome, but you still want to update your whole picture of the system. The quantum Bayes' rule could help design better ways to correct mistakes in quantum computers without disturbing the system more than necessary.

The group of researchers who developed the quantum counterpart to Bayes' rule recognized a fundamentally new framework for updating probabilities in quantum systems, extending classical Bayesian inference into the quantum domain. This finding marks a significant step forward in our understanding of quantum systems and their potential applications in machine learning and computing.

The study authors asked what 'minimum change' means for quantum systems, and their research has provided a promising answer. The quantum Bayes' rule represents a significant milestone in the quest to harness the power of quantum mechanics for practical applications.

Read also: