the progenitors of mouse CXP lymphomas suggests that peripheral editing occurs in vivo and may contribute to the development of such

cancers. Paradoxically, in the authors’ study, the signals activating V(D)J recombination in B cells are typically associated with activation of class-switch recombination rather than autore-activity — the trigger for receptor editing in the bone marrow. So, if not the revision of an auto-reactive receptor, what is achieved by replac-ing the light chain in this subset of peripheral B cells? To fully understand the significance of this phenomenon, it will be important to determine the frequency of receptor edit-ing in peripheral B cells and its physiological function. Finally, this novel B-cell population can be exploited to elucidate the mechanisms that promote translocations between antigen-receptor loci and proto-oncogenes. ■

Marilyn Diaz and Janssen Daly are at the

Laboratory of Molecular Genetics, National

Institute of Environmental Health Sciences of the

National Institutes of Health, Research Triangle

Park, North Carolina 27709, USA.

e-mails: diaz@niehs.nih.gov;

dalyj2@niehs.nih.gov

1. Wang, J. H. et al. Nature 460, 231–236 (2009).

2. Gay, D., Saunders, T., Camper, S. & Weigert, M. J. Exp. Med.

177, 999–1008 (1993).

3. Tiegs, S. L., Russell, D. M. & Nemazee, D. J. Exp. Med. 177, 1009–1020 (1993).

4. Hertz, M. & Nemazee, D. Curr. Opin. Immunol. 10, 208–213

(1998).

5. Wang, J. H. et al. J. Exp. Med. 205, 3079–3090 (2008).

6. Robbiani, D. F. et al. Cell 135, 1028–1038 (2008).

DNA

breaks

Igh

Igl

AID

dependent

RAG

dependent

A-EJ ?

Chromosome

Figure 2 | Chromosome translocations in B cells. Wang et al.1 show that mature B cells that lack an essential DNA-repair enzyme undergo both AID-induced class-switch recombination at the immunoglobulin heavy chain locus (Igh) and RAG-induced receptor editing at the immunoglobulin-λ light-chain locus (Igl). These breaks can lead to chromosome translocations involving Igh and Igl. Use of the alternative end-joining (A-EJ) pathway may contribute to the formation of such translocations.

MATERIALS SCIENCE

Nanotubes sorted using DNAMark C. Hersam

A vast number of DNA sequences are possible, and so finding the few that bind to a particular non-DNA entity is a daunting task. A systematic search algorithm has found sequences that target specific carbon nanotubes.

For nearly two decades, the carbon nanotube has been the poster child of nanotechnology. Researchers have used its exemplary physical and chemical properties in a diverse range of prototype devices, spanning such technolo-gies as alternative energy, biotechnology and computing. Underlying this success is the exquisite sensitivity of the nanotubes’ proper-ties to their physical size and atomic structure. However, this sensitivity also creates a funda-mental problem: because current syntheses of carbon nanotubes lack atomic-level control, samples produced are mixtures of nanotubes of different sizes and atomic geometries, and thus possess non-uniform properties. This non-uniformity has confounded their use in large-scale commercial applications, which invariably require materials that have consist-ent, reproducible performance.

Many researchers have therefore devised schemes for sorting carbon nanotubes accord-ing to their physical and electronic structures1.

Inspiration has often come from bioseparation methods, leading to the use of electrophore-sis2, ultracentrifugation3 and chromatography4 techniques. DNA has had a recurring support-ing role in these studies because of its ability to disperse carbon nanotubes in biologically compatible aqueous solutions5–7. But despite its ability to bind to specific molecules depending on its base sequence, DNA has not been system-atically explored as a means of isolating different types of carbon nanotube — until now. On page 250 of this issue, Tu et al.8 describe the heroic efforts that resulted in their identifying more than 20 DNA sequences that each selectively bind a specific carbon-nanotube structure. Their careful study uncovers distinct patterns of DNA sequences that will inform future efforts in nanotube separation, and provides fundamental insight into the chemical interactions between arguably the most important biomolecule and one of the most-studied nanomaterials.

To appreciate the magnitude of the authors’

task, consider that custom-made DNA sequences containing 100 nucleotides are read-ily available commercially. Because there are four DNA bases — adenine (A), thymine (T), guanine (G) and cytosine (C) — this amounts to 4100 (1060) sequences that could be screened for their nanotube-binding properties. This number is almost unfathomably large, and so the authors had to devise a systematic method to focus their search before they could attack this problem experimentally.

Initially, Tu et al. limited their search to DNA molecules containing 28 or 30 bases, thus restricting the number of possibilities to 430 (1018). Although this is a huge improvement over 1060, further refinement was clearly neces-sary. The authors therefore used a sequence-pattern-expansion scheme to come up with a manageable set of DNA sequences, starting with simple patterns and then adding complex-ity in a confined, progressive way. The scheme started with molecules that contained only one kind of base, thus yielding four sequences. Complexity was added in the next phase of the scheme — the second-order expansion — when all 16 variants of two-base repeats were added (for example, (AT)15). By following this proce-dure to a third and fourth order of complexity, Tu et al. constructed a search set containing approximately 350 different DNA sequences.

The authors used each of these sequences to disperse a randomly produced mixture of carbon nanotubes in water. They then used chromatography to separate the resulting 350 solutions into fractions based on the ionic charge of the solutes, and characterized each fraction spectroscopically to see if any of the DNA sequences had formed complexes specifi-cally with a single kind of nanotube. Although most of the sequences had not, a series of DNA molecules that contained alternating patterns of one or more purines (A or G) and pyrimidines (T or C) — such as (GT)15, (TCG)10 and (ATTT)7 — showed a differential affinity for nanotubes as a function of nanotube structure.

Recognizing the successful purine–pyrimi-dine motifs, Tu et al. performed more experi-ments in which they varied the length of their DNA sequences, and found that shorter DNA molecules (as short as eight bases) bind to nano-tubes with exceptional selectivity. In all, more than 20 distinct DNA sequences selected one kind of carbon nanotube from an as-prepared mixture. The purity of semiconducting nano-tubes isolated in this way approached 99%, equalling or exceeding those obtained by all pre-vious carbon-nanotube sorting techniques1.

Although the molecular-recognition mech-anism involved in this DNA–nanotube bind-ing8 is not fully understood, highly suggestive trends can be identified from the successful DNA sequences. For example, DNA molecules that contain alternating purine–pyrimidine patterns form stable, well-ordered, two-dimen-sional sheets through hydrogen bonding (see Fig. 2a on page 252) — structures that resemble the ubiquitous β-sheet motif in proteins. Fur-

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thermore, these DNA sheets are expected to form stable cylindrical structures reminiscent of the barrel-shaped structures formed from β-sheets in proteins. Such DNA barrels could thus encapsulate cylindrical carbon nanotubes, presumably with high affinity for nanotubes that have diameters that match the inner diam-eter of the barrel (see Fig. 2c on page 252). This structural mechanism is different from those of previously described methods for separat-ing carbon nanotubes (which are based on dif-ferential chemical binding affinity1) and thus might explain the exceptional purities achieved by Tu and colleagues.

The current study8 marks a considerable advance in the carbon-nanotube field, but major issues remain unresolved. For example, carbon nanotubes are chiral9,10 — each type of nanotube exists as one of two mirror-image forms depending on the direction in which its

carbon atoms coil up to form the tube. So far, DNA has not been shown to be able to distin-guish between the mirror-image forms of nan-otubes, which means that the DNA-separated nanotubes might be sub-optimal for some optical-device applications.

In addition, the DNA sequences identified by Tu et al.8 show higher selectivity for semi-conducting carbon nanotubes than for those that have metal-like conductivities, and so further work is required to isolate both types equally using DNA. The authors’ approach is also relatively expensive (because of the high cost of DNA), which might limit its use in large-scale applications. The ultimate solu-tion to sorting carbon nanotubes will therefore probably be a hybrid method that combines the best attributes of several different techniques1. In the meantime, Tu and colleagues’ approach possesses clear advantages for single-step, low-

quantity separations that will be of great inter-est to research groups around the world. ■

Mark C. Hersam is in the Departments of

Materials Science and Engineering, and of

Chemistry, Northwestern University, Evanston,

Illinois 60208-3108, USA.

e-mail: m-hersam@northwestern.edu

1. Hersam, M. C. Nature Nanotechnol. 3, 387–394 (2008).

2. Tanaka, T., Jin, H., Miyata, Y. & Kataura, H. Appl. Phys.

Express 1, 114001 (2008).

3. Arnold, M. S., Green, A. A., Hulvat, J. F., Stupp, S. I. &

Hersam, M. C. Nature Nanotechnol. 1, 60–65 (2006).

4. Zheng, M. & Semke, E. D. J. Am. Chem. Soc. 129, 6084–6085 (2007).

5. Zheng, M. et al. Nature Mater. 2, 338–342 (2003).

6. Zheng, M. et al. Science 302, 1545–1548 (2003).

7. Arnold, M. S., Stupp, S. I. & Hersam, M. C. Nano Lett.

5, 713–718 (2005).

8. Tu, X., Manohar, S., Jagota, A. & Zheng, M. Nature

460, 250–253 (2009).

9. Peng, X. et al. Nature Nanotechnol. 2, 361–365 (2007).

10. Green, A. A., Duch, M. C. & Hersam, M. C. Nano Res.

2, 69–77 (2009).

QUANTUM INFORMATION

Circuits that process with magicRaymond W. Simmonds and Frederick W. Strauch

Practical quantum computation will require a scalable, robust system to generate and process information with precise control. This is now possible using a superconducting circuit and a little quantum magic.

Have you ever wondered what quantum computers will really look like? Will they be warehouses full of vacuum chambers, lasers and optics tables? What will be doing the com-puting? Thus far, simple quantum algorithms have been performed with small numbers of molecules1, cold ions2 or photons3 — systems that were designed by nature but that can be cleverly controlled. Scaling these systems up to a workable quantum computer will require a little error correction and a lot of hard work.

This challenge may have become a little easier. Superconducting circuits4 offer a unique platform for constructing custom-designed, fully engineered, scalable quantum systems. These systems involve electrical circuits pat-terned on a microchip, similar to the classi-cal processors of today. The circuit used by DiCarlo et al.5, described on page 240 of this issue, which incorporates two quantum bits (qubits) on either side of an extended, resonant microwave cavity, can be controlled by tabletop electronics and has allowed these authors to demonstrate the first superconducting two-qubit quantum-information processor.

The processing of quantum information involves ‘magic tricks’ that are only possible through careful control of a quantum system6. Finding a system that maintains quantum coherence, the magic spell, over relatively long periods of time — long enough to use the magi-cian’s tricks — is a challenge. DiCarlo and col-leagues5 have developed ‘transmon’ qubits, tiny

superconducting circuits that routinely hold on to quantum coherence for a microsecond7. Although this is shorter than the millisecond (or longer) timescales obtained in atomic and photonic systems, superconducting cir-cuits benefit from nanosecond timescales for single-qubit control and pairwise qubit interac-tions provided by fast, commercial electronics. These control times are orders of magnitude faster than those for most atomic systems.

Single qubits have two ‘basis states’, a ground state ∣0 and an excited state ∣1 . A two-qubit system is described with four basis states ∣0,0 , ∣0,1 , ∣1,0 and ∣1,1 , as denoted by ∣left qubit, right qubit . In quantum mechanics, an arbi-trary two-qubit state is formed with combi-nations of these basis states: a∣0,0 + b∣0,1+ c∣1,0 + d∣1,1 , where the four coefficients a, b, c and d are complex numbers with a partic-ular amplitude and phase. The computational elements (gates) of quantum algorithms are divided into single-qubit rotations (altering sin-gle-qubit states, such as flipping a bit ∣0 ↔ ∣1 ) and two-qubit operations (altering two-qubit states).

Previously, the authors have shown high-fidelity benchmarking of single-qubit rotations8. In their new study5, DiCarlo et al. implement a two-qubit operation, a trick known as a control-led phase (C-Phase) gate. By tuning a magnetic flux on each qubit, the energy of the two-qubit states can be controlled directly. Here, the presence of the cavity provides a two-qubit

interaction, helping to uniquely determine these energy values. Because quantum states evolve in time (t) according to e−iEt/ħ, where E is the energy of the state, i is the imaginary unit and ħ is Planck’s constant, this leads to a phase change in the coefficient of each two-qubit basis state. Careful control and timing of these flux excur-sions allows a specific two-qubit basis state to be targeted for an overall π phase change or com-plete sign reversal (± to ±). Measurement of the two-qubit states is achieved by passing micro-wave photons through the resonant cavity con-taining the two qubits9,10.

Consider the blackjack-style card game ana-logue shown in Fig. 1. Imagine a deck of many ‘quantum cards’, where each card has a colour (red or blue), a rank (0 or 1) and a suit (+ or −). A two-qubit quantum state can be represented by a set of up to four possible two-card plays, in which the two colours represent the left and right qubit respectively, the rank represents the individual qubit states, and the suit indicates a phase factor. A ‘ground-state’ hand — a single two-card play {(+0,+0)} (corresponding to ∣0,0 ) — occupies one square of the card table (Fig. 1a). In this game, the player can ask the dealer for a ‘hit’ (a single-qubit rotation) on a chosen colour (qubit), in which case additional cards are added to the table. Each square with a ±0-card (±1-card) adds a ±1-card (±0-card) paired with a copy of the other colour card from that square. These additional two-card plays are then distributed to the appropriate squares on the card table. The hand can then be further refined, removing cards if necessary (see Fig. 1 for rules).

Hits on both colours, starting from the ground-state hand, lead to four single-qubit cards {([+0,+1],[+0,+1])} (Fig. 1b). Once paired with each other and distributed, four two-card plays {(+0,+0),(+0,+1),(+1,+0),(+1,+1)} occupy each square of the card table (Fig. 1c). Note that this hand corresponds to the two-qubit ‘prod-uct state’ ∣0,0 + ∣0,1 + ∣1,0 + ∣1,1 and has an

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