NEWS AND VIEWS

Large quantum systems tamed

Quantum-computing devices can be more powerful than their classical counterparts, but controlling large quantum systems is difficult. Two studies report work that overcomes this challenge.
Christine Muschik is at the Institute of Quantum Computing and the University of Waterloo, Ontario N2L 3G1, Canada.
Contact

Search for this author in:

Predicting the behaviour of more than a few quantum particles is tricky. The problem is so difficult that, in general, it cannot be tackled using classical (non-quantum) computers, and this has motivated the quest to build quantum simulators — controlled quantum devices that provide us with answers to questions about the nature of quantum matter. Quantum simulators can address fundamental problems in physics, ranging from exotic quantum phases to open questions in high-energy physics. On the more applied side, they might even help chemists to create low-cost fertilizers and organic batteries (see go.nature.com/2jvwchw). In the long run, they could revolutionize our ability to design materials and drugs1. Today, however, quantum simulators are still at an early stage of development. On pages 579 and 601, respectively, Bernien et al.2 and Zhang et al.3 report advances in this exciting endeavour.

We are only beginning to understand how to build quantum simulators. One method is to use digital simulations4, in which a sequence of logic operations is performed on a quantum computer. Another approach is to use analog simulations, in which a specific model is emulated. For example, a classical analog simulation was used to design the roof of Germany’s Olympic Stadium in Munich, which consists of a tantalizing structure of membranes. To find such lightweight yet stable configurations, architect Frei Otto experimented with soap bubbles. The experiments of Bernien et al. and Zhang et al. are quantum versions of this scenario — the researchers used trapped particles instead of soap solution and studied quantum phase transitions rather than roof designs.

Bernien and colleagues trapped atoms using optical tweezers — laser fields that hold atoms in place. This technique has the advantage that large arrays of atoms with arbitrary patterns can be prepared quickly and deterministically. The authors used additional lasers to excite atoms from the ground state to a Rydberg state, in which one of the atom’s electrons is far away from the nucleus. Rydberg atoms have a large electric dipole moment and are coupled by long-range dipole–dipole interactions. The use of such interactions for quantum computing5 has developed into an active field of research.

Although Bernien et al. trapped only ground-state atoms, they were able to observe the scientifically interesting effects associated with Rydberg states because these effects happen so quickly. When exciting the atoms, the authors temporarily switched off the optical tweezers. For the short time required for the dynamics of interest to occur, the atoms remained in place. The authors then switched the tweezers back on and detected the system’s quantum state. This approach provides a promising route to realizing controllable quantum many-body systems that have strong long-range interactions. The authors worked with a chain of atoms, but, as demonstrated by a Paris-based research group6, this technique can be extended to two and even three dimensions.

Following similar studies6,7, Bernien and colleagues created a programmable version of the Ising model of magnetism8 to explore quantum phase transitions, which are analogous to classical phase transitions such as water turning into ice. The authors observed transitions of atom chains into ordered structures known as Rydberg crystals. By changing the initial separation between the atoms, the authors were able to produce different crystals (Fig. 1a).

Figure 1 | Quantum simulators. Bernien et al.2 and Zhang et al.3 present devices called quantum simulators that allow quantum matter to be investigated. a, Bernien and colleagues trapped chains of atoms, which were initially in the ground state (grey). They then observed quantum phase transitions (QPTs), in which some of the atoms were promoted to an excited (Rydberg) state (purple). The atoms formed ordered structures called Rydberg crystals. By varying the initial separation between the atoms, the authors produced different crystals. b, Zhang and colleagues trapped a string of ions whose magnetic moments (spins) initially pointed in the same direction (black). They then applied laser fields to the ions that induced interactions between the ions and mimicked a magnetic field, causing some of the spins to flip (red). Next, the authors measured the length of the largest domain in which spins were aligned (black box). By varying the strength of the simulated magnetic field, they identified a quantum phase transition.

In another experiment, Bernien et al. applied a rapid parameter change (a quench) to their system and measured the system’s response — a quantum analogue to striking a bell and observing the ringing. The authors performed these measurements using up to 51 atoms. After the quench, they detected oscillating quantum many-body dynamics (quantum ‘ringing’), which is an indicator of the quantum nature of the resulting correlations between the atoms.

By contrast, Zhang and colleagues trapped a string of ions using electric fields. Each ion encodes a qubit (the quantum version of a classical bit) in its atomic state. Trapped ion qubits offer great versatility in their ability to perform high-quality quantum logic operations, and building useful quantum computers based on such qubits is an active area of research. The authors induced strong, long-range interactions using a method proposed9 by physicists Ignacio Cirac and Peter Zoller. Using a clever modification of these interactions10, realizations of quantum phase transitions have progressed from early proof-of-concept experiments using two ions11 to experiments involving up to 16 ions1215. Zhang and colleagues now extend the number of ions to an impressive 53.

The authors performed quench experiments by switching on the coupling between the qubits and observing the system’s response in different parameter regimes. They not only directly observed correlations between pairs of qubits, but also evaluated higher-order quantum correlations. They used these measurements to characterize a dynamical phase transition, in which their system transitioned between differently ordered quantum states (Fig. 1b).

In strings involving fewer than ten ions, the ability to perform quantum simulations has already been demonstrated16. A complementary strategy is the maximization and automatization of control in few-qubit systems, such that long sequences of well-controlled, high-quality logic operations can be performed and different simulation concepts such as digital simulations can be realized17,18. This approach has also been successfully pursued in solid-state systems19.

The experiments of Bernien et al. and Zhang et al. that used 51 and 53 particles, respectively, simulate models that are programmable but native — they use directly available couplings between pairs of particles. A much larger class of model could be realized (involving, for example, three- and four-body couplings) using digital rather than analog approaches. In the future, it will be useful to combine analog and digital elements in the demonstrated systems to expand the range of accessible problems.

Analog quantum simulations can also be realized using thousands of ultracold atoms in optical lattices20. The short-range interactions between these atoms can be used to emulate theoretical models central to condensed-matter physics called Hubbard models, and involve particles called bosons and fermions, as opposed to qubits. As quantum simulators grow, it will become increasingly important to develop tools for validation and error correction — in the case of the described analog demonstrations, it is not yet clear how this could be achieved. Methods to scale up quantum simulators include the intriguing possibility of combining simulation units in a quantum network.

Developing scalable, practical and useful quantum simulators is an ambitious task. We might find that different systems are suited for different problems and that different simulation concepts will be used for different physics questions. We are witnessing the first steps in this direction. Extending the size of controlled quantum systems is an exciting frontier, but progress in this area is not simply a matter of the size of a quantum register — it matters what we can do with it.

Nature 551, 569-571 (2017)

doi: 10.1038/d41586-017-07438-8

References

  1. 1.

    Mueck, L. Nature Chem. 7, 361–363 (2015).

  2. 2.

    Bernien, H. et al. Nature 551, 579–584 (2017).

  3. 3.

    Zhang, J. et al. Nature 551, 601–604 (2017).

  4. 4.

    Lloyd, S. Science 273, 1073–1078 (1996

  5. 5.

    Jaksch, D. et al. Phys. Rev. Lett. 85, 2208–2211 (2000).

  6. 6.

    Labuhn, H. et al. Nature 534, 667–670 (2016).

  7. 7.

    Schauß, P. et al. Nature 491, 87–91 (2012).

  8. 8.

    Lewenstein, M., Sanpera, A. & Ahufinger, V. Ultracold Atoms in Optical Lattices: Simulating Quantum Many-Body Systems (Oxford Univ. Press, 2012).

  9. 9.

    Cirac, J. I. & Zoller, P. Phys. Rev. Lett. 74, 4091–4094 (1995).

  10. 10.

    Porras, D. & Cirac, J. I. Phys. Rev. Lett. 92, 207901 (2004).

  11. 11.

    Friedenauer, A., Schmitz, H., Glueckert, J. T., Porras, D. & Schaetz, T. Nature Phys. 4, 757–761 (2008).

  12. 12.

    Islam, R. et al. Science 340, 583–587 (2013).

  13. 13.

    Jurcevic, P. et al. Nature 511, 202–205 (2014).

  14. 14.

    Richerme, P. et al. Nature 511, 198–201 (2014).

  15. 15.

    Jurcevic, P. et al. Phys. Rev. Lett. 119, 080501 (2017).

  16. 16.

    Lanyon, B. P. et al. Science 334, 57–61 (2011).

  17. 17.

    Barreiro, J. T. et al. Nature 470, 486–491 (2011).

  18. 18.

    Martinez, E. A. et al. Nature 534, 516–519 (2016).

  19. 19.

    Barends, R. et al. Nature Commun. 6, 7654 (2015).

  20. 20.

    Bloch, I., Dalibard, J. & Nascimbène, S. Nature Phys. 8, 267–276 (2012).

Download references

Nature Briefing

An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.