You would hardly think that a quantum channel could have any sort of thermodynamic behavior. We were surprised, too.

How do the laws of thermodynamics apply in the quantum regime? Thanks to novel ideas introduced in the context of quantum information, scientists have been able to develop new ways to characterize the thermodynamic behavior of quantum states. If you’re a Quantum Frontiers regular, you have certainly read about these advances in Nicole’s captivating posts on the subject.

Asking the same question for quantum channels, however, turned out to be more challenging than expected. A quantum channel is a way of representing how an input state can change into an output state according to the laws of quantum mechanics. Let’s picture it as a box with an input state and an output state, like so:

A computing gate, the building block of quantum computers, is described by a quantum channel. Or, if Alice sends a photon to Bob over an optical fiber, then the whole process is represented by a quantum channel. Thus, by studying quantum channels directly we can derive statements that are valid regardless of the physical platform used to store and process the quantum information—ion traps, superconducting qubits, photonic qubits, NV centers, etc.

We asked the following question: If I’m given a quantum channel, can I transform it into another, different channel by using something like a miniature heat engine? If so, how much work do I need to spend in order to accomplish this task? The answer is tricky because of a few aspects in which quantum channels are more complicated than quantum states.

In this post, I’ll try to give some intuition behind our results, which were developed with the help of Mario Berta and Fernando Brandão, and which were recently published in Physical Review Letters.

First things first, let’s worry about how to study the thermodynamic behavior of miniature systems.

Thermodynamics of small stuff

One of the important ideas that quantum information brought to thermodynamics is the idea of a resource theory. In a resource theory, we declare that there are certain kinds of states that are available for free, and that there are a set of operations that can be carried out for free. In a resource theory of thermodynamics, when we say “for free,” we mean “without expending any thermodynamic work.”

Here, the free states are those in thermal equilibrium at a fixed given temperature, and the free operations are those quantum operations that preserve energy and that introduce no noise into the system (we call those unitary operations). Faced with a task such as transforming one quantum state into another, we may ask whether or not it is possible to do so using the freely available operations. If that is not possible, we may then ask how much thermodynamic work we need to invest, in the form of additional energy at the input, in order to make the transformation possible.

Interestingly, the amount of work needed to go from one state ρ to another state σ might be unrelated to the work required to go back from σ to ρ. Indeed, the freely allowed operations can’t always be reversed; the reverse process usually requires a different sequence of operations, incurring an overhead. There is a mathematical framework to understand these transformations and this reversibility gap, in which generalized entropy measures play a central role. To avoid going down that road, let’s instead consider the macroscopic case in which we have a large number n of independent particles that are all in the same state ρ, a state which we denote by . Then something magical happens: This macroscopic state can be reversibly converted to and from another macroscopic state , where all particles are in some other state σ. That is, the work invested in the transformation from to can be entirely recovered by performing the reverse transformation:

If this rings a bell, that is because this is precisely the kind of thermodynamics that you will find in your favorite textbook. There is an optimal, reversible way of transforming any two thermodynamic states into each other, and the optimal work cost of the transformation is the difference of a corresponding quantity known as the thermodynamic potential. Here, the thermodynamic potential is a quantity known as the free energy . Therefore, the optimal work cost per copy w of transforming into is given by the difference in free energy .

From quantum states to quantum channels

Can we repeat the same story for quantum channels? Suppose that we’re given a channel , which we picture as above as a box that transforms an input state into an output state. Using the freely available thermodynamic operations, can we “transform” into another channel ? That is, can we wrap this box with some kind of procedure that uses free thermodynamic operations to pre-process the input and post-process the output, such that the overall new process corresponds (approximately) to the quantum channel ? We might picture the situation like this:

Let us first simplify the question by supposing we don’t have a channel to start off with. How can we implement the channel from scratch, using only free thermodynamic operations and some invested work? That simple question led to pages and pages of calculations, lots of coffee, a few sleepless nights, and then more coffee. After finally overcoming several technical obstacles, we found that in the macroscopic limit of many copies of the channel, the corresponding amount of work per copy is given by the maximum difference of free energy F between the input and output of the channel. We decided to call this quantity the thermodynamic capacity of the channel:

Intuitively, an implementation of must be prepared to expend an amount of work corresponding to the worst possible transformation of an input state to its corresponding output state. It’s kind of obvious in retrospect. However, what is nontrivial is that one can find a single implementation that works for all input states.

It turned out that this quantity had already been studied before. An earlier paper by Navascués and García-Pintos had shown that it was exactly this quantity that characterized the amount of work per copy that could be extracted by “consuming” many copies of a process provided as black boxes.

To our surprise, we realized that Navascués and García-Pintos’s result implied that the transformation of into is reversible. There is a simple procedure to convert into at a cost per copy that equals . The procedure consists in first extracting work per copy of the first set of channels, and then preparing from scratch at a work cost of per copy:

Clearly, the reverse transformation yields back all the work invested in the forward transformation, making the transformation reversible. That’s because we could have started with ’s and finished with ’s instead of the opposite, and the associated work cost per copy would be . Thus the transformation is, indeed, reversible:

In turn, this implies that in the many-copy regime, quantum channels have a macroscopic thermodynamic behavior. That is, there is a thermodynamic potential—the thermodynamic capacity—that quantifies the minimal work required to transform one macroscopic set of channels into another.

Prospects for the thermodynamic capacity

Resource theories that are reversible are pretty rare. Reversibility is a coveted property because a reversible resource theory is one in which we can easily understand exactly which transformations are possible. Other than the thermodynamic resource theory of states mentioned above, most instances of a resource theory—especially resource theories of channels—typically produce the kind of overheads in the conversion cost that spoil reversibility. So it’s rather exciting when you do find a new reversible resource theory of channels.

Quantum information theorists, especially those working on the theory of quantum communication, care a lot about characterizing the capacity of a channel. This is the maximal amount of information that can be transmitted through a channel. Even though in our case we’re talking about a different kind of capacity—one where we transmit thermodynamic energy and entropy, rather than quantum bits of messages—there are some close parallels between the two settings from which both fields of quantum communication and quantum thermodynamics can profit. Our result draws deep inspiration from the so-called quantum reverse Shannon theorem, an important result in quantum communication that tells us how two parties can communicate using one kind of a channel if they have access to another kind of a channel. On the other hand, the thermodynamic capacity at zero energy is a quantity that was already studied in quantum communication, but it was not clear what that quantity represented concretely. This quantity gained even more importance as it was identified as the entropy of a channel. Now, we see that this quantity has a thermodynamic interpretation. Also, the thermodynamic capacity has a simple definition, it is relatively easy to compute and it is additive—all desirable properties that other measures of capacity of a quantum channel do not necessarily share.

We still have a few rough edges that I hope we can resolve sooner or later. In fact, there is an important caveat that I have avoided mentioning so far—our argument only holds for special kinds of channels, those that do the same thing regardless of when they are applied in time. (Those channels are called time-covariant.) A lot of channels that we’re used to studying have this property, but we think it should be possible to prove a version of our result for any general quantum channel. In fact, we do have another argument that works for all quantum channels, but it uses a slightly different thermodynamic framework which might not be physically well-grounded.

That’s all very nice, I can hear you think, but is this useful for any quantum computing applications? The truth is, we’re still pretty far from founding a new quantum start-up. The levels of heat dissipation in quantum logic elements are still orders of magnitude away from the fundamental limits that we study in the thermodynamic resource theory.

Rather, our result teaches us about the interplay of quantum channels and thermodynamic concepts. We not only have gained useful insight on the structure of quantum channels, but also developed new tools for how to analyze them. These will be useful to study more involved resource theories of channels. And still, in the future when quantum technologies will perhaps approach the thermodynamically reversible limit, it might be good to know how to implement a given quantum channel in such a way that good accuracy is guaranteed for any possible quantum input state, and without any inherent overhead due to the fact that we don’t know what the input state is.

Thermodynamics, a theory developed to study gases and steam engines, has turned out to be relevant from the most obvious to the most unexpected of situations—chemical reactions, electromagnetism, solid state physics, black holes, you name it. Trust the laws of thermodynamics to surprise you again by applying to a setting you’d never imagined them to, like quantum channels.

It’s always exciting when you can bridge two different physical concepts that seem to have nothing in common—and it’s even more thrilling when the results have as broad a range of possible fields of application as from fault-tolerant quantum computation to quantum gravity.

Physicists love to draw connections between distinct ideas, interconnecting concepts and theories to uncover new structure in the landscape of scientific knowledge. Put together information theory with quantum mechanics and you’ve opened a whole new field of quantum information theory. More recently, machine learning tools have been combined with many-body physics to find new ways to identify phases of matter, and ideas from quantum computing were applied to Pozner molecules to obtain new plausible models of how the brain might work.

In a recent contribution, my collaborators and I took a shot at combining the two physical concepts of quantum error correction and physical symmetries. What can we say about a quantum error-correcting code that conforms to a physical symmetry? Surprisingly, a continuous symmetry prevents the code from doing its job: A code can conform well to the symmetry, or it can correct against errors accurately, but it cannot do both simultaneously.

By a continuous symmetry, we mean a transformation that is characterized by a set of continuous parameters, such as angles. For instance, if I am holding an atom in my hand (more realistically, it’ll be confined in some fancy trap with lots of lasers), then I can rotate it around and about in space:

A rotation like this is fully specified by an axis and an angle, which are continuous parameters. Other transformations that we could think of are, for instance, time evolution, or a continuous family of unitary gates that we might want to apply to the system.

On the other hand, a code is a way of embedding some logical information into physical systems:

By cleverly distributing the information that we care about over several physical systems, an error-correcting code is able to successfully recover the original logical information even if the physical systems are exposed to some noise. Quantum error-correcting codes are particularly promising for quantum computing, since qubits tend to lose their information really fast (current typical ones can hold their information for a few seconds). In this way, instead of storing the actual information we care about on a single qubit, we use extra qubits which we prepare in a complicated state that is designed to protect this information from the noise.

Covariant codes for quantum computation

A code that is compatible with respect to a physical symmetry is called covariant. This property ensures that if I apply a symmetry transformation on the logical information, this is equivalent to applying corresponding symmetry transformations on each of the physical systems.

Suppose I would like to flip my qubit from “0” to “1” and from “1” to “0”. If my information is stored in an encoded form, then in principle I first need to decode the information to uncover the original logical information, apply the flip operation, and then re-encode the new logical information back onto the physical qubits. A covariant code allows to perform the transformation directly on the physical qubits, without having to decode the information first:

The advantage of this scheme is that the logical information is never exposed and remains protected all along the computation.

But here’s the catch: Eastin and Knill famously proved that error-correcting codes can be at most covariant with respect to a finite set of transformations, ruling out universal computation with transversal gates. In other words, the computations we can perform using this scheme are very limited because we can’t perform any continuous symmetry transformation.

Interestingly, however, there’s a loophole: If we consider macroscopic systems, such as a particle with a very large value of spin, then it becomes possible again to construct codes that are covariant with respect to continuous transformations.

How is that possible, you ask? How do we transition from the microscopic regime, where covariant codes are ruled out for continuous symmetries, to the macroscopic regime, where they are allowed? We provide an answer by resorting to approximate quantum error correction. Namely, we consider the situation where the code does not have to correct each error exactly, but only has to reconstruct a good approximation of the logical information. As it turns out, there is a quantitative limit to how accurately a code can correct against errors if it is covariant with respect to a continuous symmetry, represented by the following equation:

where specifies how inaccurately the code error-corrects ( means the code can correct against errors perfectly), n is the number of physical subsystems, and the and are measures of “how strongly” the symmetry transformation can act on the logical and physical subsystems.

Let’s try to understand the right-hand side of this equation. In physics, continuous symmetries are generated by what we call physical charges. These are physical quantities that are associated with the symmetry, and that characterize how the symmetry acts on each state of the system. For instance, the charge that corresponds to time evolution is simply energy: States that label high energies have a rapidly varying phase whereas the phase of low-energy states changes slowly in time. Above, we indicate by the range of possible charge values on the logical system and by the corresponding range of charge values on each physical subsystem. In typical settings, this range of charge values is related to the dimension of the system—the more states the system has, intuitively, the greater range of charges it can accommodate.

The above equation states that the inaccuracy of the code must be larger than some value given on the right-hand side of the equation, which depends on the number of subsystems n and the ranges of charge values on the logical system and physical subsystems. The right-hand side becomes small in two regimes: if each subsystem can accommodate a large range of charge values, or if there is a large number of physical systems. In these regimes, our limitation vanishes, and we can circumvent the Eastin-Knill theorem and construct good covariant error-correcting codes. This allows us to connect the two regimes that seemed incompatible earlier, the microscopic regime where there cannot be any covariant codes, and the macroscopic regime where they are allowed.

From quantum computation to many-body physics and quantum gravity

Quantum error-correcting codes not only serve to protect information in a quantum computation against noise, but they also provide a conceptual toolbox to understand complex physical systems where a quantum state is delocalized over many physical subsystems. The tight connections between quantum error correction and many-body physics have been put to light following a long history of pioneering research at Caltech in these fields. And as if that weren’t enough, quantum error correcting codes were also shown to play a crucial role in understanding quantum gravity.

There is an abundance of natural physical symmetries to consider both in many-body physics and in quantum gravity, and that gives us a good reason to be excited about characterizing covariant codes. For instance, there are natural approximate quantum error correcting codes that appear in some statistical mechanical models by cleverly picking global energy eigenstates. These codes are covariant with respect to time evolution by construction, since the codewords are energy eigenstates. Now, we understand more precisely under which conditions such codes can be constructed.

Perhaps an even more illustrative example is that of time evolution in holographic quantum gravity, that is, in the AdS/CFT correspondence. This model of quantum gravity has the property that it is equivalent to a usual quantum field theory that lives on the boundary of the universe. What’s more, the correspondence which tells us how the bulk quantum gravity theory is mapped to the boundary is, in fact, a quantum error-correcting code. If we add a time axis, then the picture becomes a cylinder where the interior is the theory of quantum gravity, and where the cylinder itself represents a traditional quantum field theory:

Since the bulk theory and the boundary theory are equivalent, the action of time evolution must be faithfully represented in both pictures. But this is in apparent contradiction with the Eastin-Knill theorem, from which it follows that a quantum error-correcting code cannot be covariant with respect to a continuous symmetry. We now understand how this is, in fact, not a contradiction: As we’ve seen, codes may be covariant with respect to continuous symmetries in the presence of systems with a large number of degrees of freedom, such as a quantum field theory.

What’s next?

There are some further results in our paper that I have not touched upon in this post, including a precise approximate statement of the Eastin-Knill theorem in terms of system dimensions, and a fun machinery to construct covariant codes for more general systems such as oscillators and rotors.

We have only scratched the surface of the different applications I’ve mentioned, by studying the properties of covariant codes in general. I’m now excited to dive into more detail with our wonderful team to study deeper applications to correlations in many-body systems, global symmetries in quantum gravity, accuracy limits of quantum clocks and precision limits to quantum metrology in the presence of noise.

This has been an incredibly fun project to work on. Such a collaboration illustrates again the benefit of interacting with great scientists with a wide range of areas of expertise including representation theory, continuous variable systems, and quantum gravity. Thanks Sepehr, Victor, Grant, Fernando, Patrick, and John, for this fantastic experience.