Bayes’s rule is one of the most fundamental principles in probability and statistics. It allows us to update our beliefs in the face of new evidence. In its simplest form, the rule tells us how to revise the probability of a hypothesis once new data becomes available.
A standard way to teach it involves drawing coloured balls from a pouch: you start with some expectation (e.g. “there’s a 20% chance I’ll draw a blue ball”), then you update your belief depending on what you observe (“I’ve drawn a red ball, so the actual chance of drawing a blue ball is 10%”). While this example seems simple, the rule carries considerable weight: physicists and mathematicians have described it as the most consistent way to handle uncertainty in science, and it’s a central part of logic, decision theory, and indeed nearly every field of applied science.
There are two well-known ways of arriving at Bayes’s rule. One is the axiomatic route, which treats probability as a set of logical rules and shows that Bayesian updating is the only way to preserve consistency. The other is variational, which demands that updates should stay as close as possible to prior beliefs while remaining consistent with new data. This latter view is known as the principle of minimum change. It captures the intuition that learning should be conservative: we shouldn’t alter our beliefs more than is necessary. This principle explains why Bayesian methods have become so effective in practical statistical inference: because they balance a respect for new data with loyalty to old information.
A natural question arises here: can Bayes’s rule be extended into the quantum world?
Quantum theory can be thought of as a noncommutative extension of probability theory. While there are good reasons to expect there should be a quantum analogue of Bayes’s rule, the field has for a long time struggled to identify a unique and universally accepted version. Instead, there are several competing proposals. One of them stands out: the Petz transpose map. This is a mathematical transformation that appears in many areas of quantum information theory, particularly in quantum error correction and statistical sufficiency. Some scholars have even argued that it’s the “correct” quantum Bayes’s rule. Still, the situation remains unsettled.
In probability, the joint distribution is like a big table that lists the chances of every possible pair of events happening together. If you roll a die and flip a coin, the joint distribution specifies the probability of getting “heads and a 3”, “tails and a 5”, and so on. In this big table, you can also zoom out and just look at one part. For example, if you only care about the die, you can add up over all coin results to get the probability of each die face. Or if you only care about the coin, you can add up over all die results to get the probability of heads or tails. These zoomed-out views are called marginals.
The classical Bayes’s rule doesn’t just update the zoomed-out views but the whole table — i.e. the entire joint distribution — so the connection between the two events also remains consistent with the new evidence.
In the quantum version, the joint distribution isn’t a table of numbers but a mathematical object that records how the input and output of a quantum process are related. The point of the new study is that if you want a true quantum Bayes’s rule, you need to update that whole object, not just one part of it.
A new study by Ge Bai, Francesco Buscemi, and Valerio Scarani in Physical Review Letters has taken just this step. In particular, they’ve presented a quantum version of the principle of minimum change by showing that when the measure of change is chosen to be quantum fidelity — a widely used measure of similarity between states — this optimisation leads to a unique solution. Equally remarkably, this solution coincided with the Petz transpose map in many important cases. As a result, the researchers have built a strong bridge between classical Bayesian updating, the minimum change principle, and a central tool of quantum information.
The motivation for this new work isn’t only philosophical. If we’re to generalise Bayes’s rule to include quantum mechanics as well, we need to do so in a way that respects the structural constraints of quantum theory without breaking away from its classical roots.
The researchers began by recalling how the minimum change principle works in classical probability. Instead of updating only a single marginal distribution, the principle works at the level of the joint input-output distribution. Updating then becomes an optimisation problem, i.e. finding the subsequent distribution that’s consistent with the new evidence but minimally different from the evidence from before.
In ordinary probability, we talk about stochastic processes. These are rules that tell us how an input is turned into an output, with certain probabilities. For example if you put a coin into a vending machine, there might be a 90% chance you get a chips packet and a 10% chance you get nothing. This rule describes a stochastic process. This process can also be described with a joint distribution.
In quantum physics, however, it’s tricky. The inputs and outputs aren’t just numbers or events but quantum states, which are described by wavefunctions or density matrices. This makes the maths much more complex. The resulting stochastic processes also become sequences of events called completely positive trace-preserving (CPTP) maps.
A CPTP map is the most general kind of physical evolution allowed: it takes a quantum state and transforms it into another quantum state. And in the course of doing so, it needs to follow two rules: it shouldn’t yield any negative probabilities and it should ensure the total probability adds up to 1. That is, your chance of getting a chips packet shouldn’t be –90% nor should it be 90% plus a 20% chance of getting nothing.
These complications mean that, while the joint distribution in classical Bayesian updating is a simple table, the one in quantum theory is more sophisticated. It uses two mathematical tools in particular. One is purification, a way to embed a mixed quantum state into a larger ‘pure’ state so that mathematicians can keep track of correlations. The other is Choi operators, a standard way of representing a CPTP map as a big matrix that encodes all possible input-output behaviour at once.
Together, these tools play the role of the joint distribution in the quantum setting: they record the whole picture of how inputs and outputs are related.
Now, how do you compare two processes, i.e. the actual forward process (input → output) and the guessed reverse process (output → input)?
In quantum mechanics, one of the best measures of similarity is fidelity. It’s a number between 0 and 1. 0 means two processes are completely different and 1 means they’re exactly the same.
In this context, the researchers’ problem statement was this: given a forward process, what reverse process is closest to it?
To solve this, they looked over all possible reverse processes that obeyed the two rules, then they picked the one that maximised the fidelity, i.e. the CPTP map most similar to the forward process. This is the quantum version of applying the principle of minimum change.
In the course of this process, the researchers found that in natural conditions, the Petz transpose map emerges as the quantum Bayes’s rule.
In quantum mechanics, two objects (like matrices) commute if the order in which you apply them doesn’t matter. That is, A then B produces the same outcome as B then A. In physical terms, if two quantum states commute, they behave more like classical probabilities.
The researchers found that when the CPTP map that takes an input and produces an output, called the forward channel, commutes with the new state, the updating process is nothing but the Petz transpose map.
This is an important result for many reasons. Perhaps foremost is that it explains why the Petz map has shown up consistently across different parts of quantum information theory. It appears it isn’t just a useful tool but the natural consequence of the principle of minimum change applied in the quantum setting.
The study also highlighted instances where the Petz transpose map isn’t optimal, specifically when the commutativity condition fails. In these situations, the optimal updating process depends more intricately on the new evidence. This subtlety departs clearly from classical Bayesian logic because in the quantum case, the structure of non-commutativity forces updates to depend non-linearly on the evidence (i.e. the scope of updating can be disproportionate to changes in evidence).
Finally, the researchers have shown how their framework can recover special cases of practical importance. If some new evidence perfectly agrees with prior expectations, the forward and reverse processes become identical, mirroring the classical situation where Bayes’s rule simply reaffirms existing beliefs. Similarly, in contexts like quantum error correction, the Petz transpose map’s appearance is explained by its status as the optimal minimal-change reverse process.
But the broader significance of this work lies in the way it unifies different strands of quantum information theory under a single conceptual roof. By proving that the Petz transpose map can be derived from the principle of minimum change, the study has provided a principled justification for its widespread use rather than being restricted to particular contexts. This fact has immediate consequences for quantum computing, where physicists are looking for ways to reverse the effects of noise on fragile quantum states. The Petz transpose map has long been known to do a good job of recovering information from these states after they’ve been affected by noise. Now that physicists know the map embodies the smallest update required to stay consistent with the observed outcomes, they may be able to design new recovery schemes that exploit the structure of minimal change more directly.
The study may also open doors to extending Bayesian networks into the quantum regime. In classical probability, a Bayesian network provides a structured way to represent cause-effect relationships. By adapting the minimum change framework, scientists may be able to develop ‘quantum Bayesian networks’ where the way one updates their expectations of a particular outcome respects the peculiar constraints of CPTP maps. This could have applications in quantum machine learning and in the study of quantum causal models.
There are also some open questions as well. For instance, the researchers have noted that if different measures of divergence other than fidelity are used, e.g. the Hilbert-Schmidt distance or quantum relative entropy, the resulting quantum Bayes’s rules may be different. This in turn indicates that there could be multiple valid updating rules, each suited to different contexts. Future research will need to map out these possibilities and determine which ones are most useful for particular applications.
In all, the study provides both a conceptual advance and a technical tool. Conceptually, it shows how the spirit of Bayesian updating can carry over into the quantum world; technically, it provides a rigorous derivation of when and why the Petz transpose map is the optimal quantum Bayes’s rule. Taken together, the study’s finding strengthens the bridge between classical and quantum reasoning and offers a deeper understanding of how information is updated in a world where uncertainty is baked into reality rather than being due to an observer’s ignorance.