Dwave Systems Interviews about their Quantum Computer, Continuing Controversy and a competitor with a 20 qubit system and a million qubit design based on trapped ions

The scientific community accepts two models for building qubits and keeping them in quantum states—gate and adiabatic. D-Wave’s is adiabatic, and so is the 20-qubit model Christopher Monroe, the Bice Zorn Professor of Physics at the University of Maryland and fellow of the Joint Quantum Institute, has in his lab. Adiabatic quantum computers apply quantum annealing—put simply, a strategy for finding the lowest-energy solution to a numerical problem. Munroe has published a design for scaling trapped ion quantum computers to 1 million qubits or more. Chris Munroe is highly critical of Dwave System and their 512 qubit approach. Dwave has not convinced all doubters of the quantumness of their system. Of course, Munroe has a conflict of interest. However, it would be good if Munroe can get funding for his system as well. There are many approaches to highly scalable quantum computers and it would be good to thoroughly explore all of those approaches. It is too early in the development to settle on one approach for quantum computers.

Christopher Munroe has co-written a paper on how to Scaling the Ion Trap Quantum Processor. Monroe and Kim discussed the challenges of scaling trapped ion architectures to hundreds and thousands of qubits and beyond.

In order to scale beyond 10 to 100 qubits in ion trap quantum computing, Munroe and Kim turned to a multiplexed architecture called the quantum charge coupled device. This involves the sequential entanglement of small numbers of ions through there selective motion in a single chain and the classical shuttling of individual ions between different trapping zones to propogate the entanglement.

(A) Optical dipole forces (red) displace two ions depending on their qubit states, and the resulting modulation of the Coulomb interaction allows the implementation of the controlled-NOT gate between these two ions. (B) Concept of a quantum CCD trap, in which ions can be shuttled between various zones. Ions can be entangled within a small crystal using laser forces as in (A) and then moved to different zones to propagate the entanglement to other ion crystals. Additional zones can be used for the loading of ions or qubit state detection. In principle, any pair of ions can be brought together through a web of ion trap channels, and a separate ion species can be used for sympathetic cooling to quench any residual motion from the shuttling procedure. [Image credit: National Institute of Standards and Technology] (C) Ion trap structure for the shuttling of ions through a junction. [Main image adapted with permission from (18); copyright 2011 by the American Physical Society] (D) Surface ion trap structure for shuttling ions through a three channel junction.

Nextbigfuture had summary coverage the work and the special Science journal issue on Quantum computing back in March 2013.

A design to scaling trapped ion to 1 million qubits or more

A single ion chain (or several chains on a chip connected through the QCCD architecture) with an optical interface (Fig. 2F) can serve as a processor node (ELU) of a distributed quantum multicomputer, in which two-qubit gates between ions that belong to different ELUs are realized by using the photonic gate. When a large number (∼10^3 ) of such ELUs are connected through a reconfigurable photonic network supported by an optical crossconnect switch, a scalable quantum computer with up to 1 million qubits can be constructed.


Trapped atomic ions are standards for quantum information processing, serving as quantum memories, hosts of quantum gates in quantum computers and simulators, and nodes of quantum communication networks. Quantum bits based on trapped ions enjoy a rare combination of attributes: They have exquisite coherence properties, they can be prepared and measured with nearly 100% efficiency, and they are readily entangled with each other through the Coulomb interaction or remote photonic interconnects. The outstanding challenge is the scaling of trapped ions to hundreds or thousands of qubits and beyond, at which scale quantum processors can outperform their classical counterparts in certain applications. We review the latest progress and prospects in that effort, with the promise of advanced architectures and new technologies, such as microfabricated ion traps and integrated photonics.

Techcrunch interviewed Dwave CEO Brownell

D-Wave CEO Vern Brownell gives us an overview of D-Wave and talk about the company’s past, present, and future.

This bit starts at around 16:10:

“My one kind of disappointment I might have… there are very few investors today who are willing to invest in world-changing technologies, and it’s really going to have a large impact on the world. Not that game companies and other great companies like Twitter and Facebook don’t change the way we all operate. But there’s a real lack of ground-breaking kind of research and things that take more than a few years to develop, and hardware companies, and things like that. It’s kind of disappointing to see.

I hope the pendulum swings back the other way at some point, where it becomes more in vogue to do more of this science-based research, because it’s really important for us to transform science into technology.”

Venturebeat interview of Brownell and Dwave Chief Scientist Ladizinsky

Eric Ladizinsky: This is not another supercomputer. It’s akin to discovering fire or electricity. The quantum computers use physical phenomenon that seems like magic. The same physical object can live out many possibilities. Here’s an example I use: Imagine you’re at the Library of Congress and surrounded by 50 million books. I put an X in one of those books. Go and find the X. It would be near impossible if you were to look at one book sequentially. You would be acting classically. That is the way that processors work today.

The trick is to talk to all of those shelves at once, and have one of those shelves raise its hand when it recognizes the X.

Ladizinsky: We can solve very hard problems — those problems that scale exponentially with a number of variables. Genomic matching is a good example — you have to take a genome and match it against a certain pattern. It can be done by a quantum computer much faster than a classical computer. For financial services, we could use the computer to value risk and look at what could happen to the firm’s portfolio or balance sheet.

We are really in this for the types of problems we can solve; we are trying to move science forward. There are so many problems that are beyond the capability of classical computers today.