Chad Orzel is a physics professor and blogger. He believes that the most exciting developments in the field are in the area of quantum simulation- Using quantum computers to understand the states of complex and strongly-interacting systems.
Quantum computing is an exceptionally hot topic right now, with the juggernauts of the computing industry like Google and Microsoft and IBM and Intel giving presentations at big meetings about their new quantum devices. And in a lot of ways they’re making interesting devices mostly in the area of quantum simulation.
What does this mean? Popular treatments of quantum computing mostly focus on the area of quantum algorithms, where you investigate ways that replacing classical bits that are either “0” or “1” with qubits that can be some arbitrary superposition of “0” and “1” at the same time lets you do certain computations faster.
The big-money application of this is Shor’s algorithm for factoring numbers, which Scott Aaronson explained a while back, gets spies and bankers interested because a fast method for factoring large numbers could compromise some encryption techniques. There are also quantum algorithms for fast search and other sots of problems mostly relating to collective properties of systems of numbers.
That branch of quantum computing is very exciting, but also super difficult to make dramatic progress in. The problem is that to do anything really interesting, you need huge numbers of qubits, and many of the current architectures don’t scale well or quickly – [qubits are exceptionally fragile and become more unstable the more you add]. We’ll probably get to the point of doing interesting computations with these kinds of systems, but I don’t think it will be revolutionizing the world all that soon.
There’s another angle on quantum computing though, what I referred to as “quantum simulation” above, and that’s more promising in the short term. It’s what’s really going on in a lot of the currently-hyped quantum devices, and there was a nice paper in Physical Review X (open-access) a couple of weeks ago giving a simple demonstration of the kind of thing you can do in this area.
The idea behind quantum simulation is that a quantum-mechanical world is somewhat costly to simulate on a classical computer. Not only do you have to worry about particles being in multiple states at the same time, you have to worry about correlations between particles– the exact superposition of states for one particle can be tied to the state of each of the other particles in the system, regardless of where they’re located.
This adds a lot of overhead: not only do you need to keep track of slightly more complicated individual states, you also need to include elements in your system that describe the “coherences” between each individual particle and all of the other particles. The resources needed to keep track of all that grow extremely rapidly as you increase the size of the simulation.
But, that’s mostly a problem if you’re thinking about doing the system with a classical computer, whose bits don’t naturally behave in a quantum manner. The idea of quantum simulation, on the other hand, is to model these kinds of systems with bits that are already quantum, and so have those kinds of superpositions and correlations already built in, with no need to add extra bits to track them.
If you can map your quantum system of interest onto a system of qubits that you control very well, the system will naturally simulate exactly the kinds of quantum behavior that are most difficult to model on a classical computer.
It turns out that the scaling here makes it a lot easier to make progress, compared to the more algorithmic side of things. There’s some argument about the exact limit, but if you can get something like 50 qubits together, you’re in the realm where the best classical computers would have an extremely hard time simulating the results. This is the kind of thing that’s going on with most of the “quantum computing” systems being discussed at big companies right now: they’re doing things like finding the lowest-energy configuration for a system of interacting magnetic particles by mapping those interactions onto a network of qubits and letting it find its own lowest-energy configuration.
Of course, most systems that you’d be interested in for real-world applications also interact with the environment, which is a tricky problem because ensuring the integrity of a quantum computation involves working very hard to remove environmental effects that might complicate matters. That’s what the recent PRX paper is about: Putting the environment back into the simulation.
The specific problem they’re looking at is an energy transfer reaction where one part of a quantum system has some extra energy, and passes it to another part of the system. This is something that plays an important role in a lot of biological processes- photosynthesis, for example, where energy absorbed by a pigment molecule in one part of a plant cell is transferred to another part of the cell to be used in driving the chemical reactions of life.
This is a simple matter if the energy levels of the destination molecule exactly match those at the start of the process, but that rarely happens. Different molecules tend to have different energy levels, and even if they start out close together, environmental factors like the temperature and chemical environment can shift those energy states around. For these reactions to proceed in a hot, wet and messy environment like a real cell, there needs to be a way to gain a little extra energy from the environment, or dump a little excess energy into the environment. But that’s a tricky thing to simulate in a system like a quantum computer where you’ve worked very hard to limit disruption from the environment.
The recent paper is demonstrating a way of doing just that, in an extremely simple “simulator” consisting of two ions held in an electrostatic trap. These ions are held in the trap by high-voltage electric fields, but they also repel each other, and can thus “talk” to each other via their collective vibrational motion.
If they put the two ions in there and excite one, it’s not hard to set up a situation where that energy will move back and forth between the two via that vibrational channel.
What they do in this paper is to add a laser that shifts the energy states of one of the two ions to stop that easy direct transfer. Then they simulate an environment by adding some extra lasers to modify the vibrational motion so that it can either provide an extra little boost to enable the energy transfer (when the target ion needs more energy than the source has), or soak up a little energy (when the source ion has more energy than the target ion). They can monitor how the reaction proceeds by tracking the state of the target ion, and compare their results to what they would expect from classical simulations of an energy transfer reaction in a noisy environment, and it agrees pretty nicely.
Now, of course, this is a really simple simulation, one that you can model perfectly well with a good classical computer. In order to do something genuinely of interest for modeling biology, they’d need a lot more trapped ions and more complicated interactions. This is, however, a nice proof-of-principle experiment showing how you can use a relatively simple quantum system to simulate fairly rich physics, and get total control over not only the way the process of interest is mapped onto the simulator, but also total control over the “environment” of the simulated system.
That’s a pretty cool addition to the quantum-simulation toolkit, and another demonstration of how this is a fascinating research area in the near term. Quantum simulation is pushing forward rapidly, and it’s fun to watch.
Chad Orzel is a physics professor, pop-science author, and blogger. His next book, Breakfast with Einstein: The Exotic Physics of Everyday Objects, will be released in December 2018.
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