Nextbigfuture interviewed Bo Ewald, who is D-Wave System’s President of International Business. Bo previously worked at Cray, Silicon Graphics and Los Alamos National Laboratory.
How does Quantum Annealing work ?
Bo provided the following analogy of the Quantum Annealing process.
Imagine the solution to complex math and science problems is like a mountain landscape. The best answer would be found at the lowest valley of the solution space.
D-Wave’s 2000 qubit annealers start with a fog covering the mountains. The annealing removes the fog and the qubits move to the lowest valley based on the input parameter qubits.
D-Wave introduced Reverse Annealing recently. What is reverse annealing and how does it work ?
Reverse annealing lets programmers start the search for the best solution at a localized spot in solution space. The system will then focus on the area which may be more promising for the best answer.
D-Wave enables users to have more control of qubits during the annealing process.
Reverse annealing allows users to start systematic searches for the best answer based upon their understanding of the solution space. It circumvents limitations on the number of qubits or the entanglement time by starting new searches in different locations.
IBM, Google and Rigetti have been making news with their gate model quantum computers. What is Bo’s view of their work ?
There are two major approaches to quantum computing systems – annealing and the gate model.
Gate model systems actually require longer coherence times, more qubit control, etc. than D-Wave does. D-Wave forces the quantum states into a digital state with each annealing cycle, some 5000 – 10,000 times per second.
Currently IBM, Google and Rigetti have created or are creating about 50 qubit gate systems with no error correction. They are not universal quantum computing systems but approximate gate model systems. These system may be good at one or two applications, that’s unknown so far.
Over the next year or so they will perform tests to see how fast the new systems are. They will see if their quantum systems have an advantage over classical (regular) computing systems. It is possible that quantum advantage might be found in problems dealing with materials and quantum chemistry.
Error correction for quantum systems is a thousand times harder than non-error correcting
D-Wave does not have error correction and neither will the current generation of quantum gate systems. D-Wave repeatedly solves problems to get around the lack of error correction. 1000 problem runs generates a distribution of solutions. Those solutions are easily checked to see which is the best solution.
Classical digital systems get buy with one error correct bit for every 8 bits.
Research on quantum error correction suggests that for 2000 qubits one would need 1 million error correcting qubits and another paper said for 1000 qubits one would need 6.3 million error correcting qubits.
The vast number of qubits needed for an error corrected system is why no error correcting system has been implemented for any quantum gate.
There has been a fair amount of discussion in both the media and the scientific community about the impact of environmental noise (heat, vibration, magnetism etc) and the need for quantum error correction in quantum computing, and what that means for the D-Wave technology. One of the most attractive characteristics of quantum annealing systems such as the D-Wave system, is that they are more robust against decoherence from certain types of environmental noise than other quantum systems, such as those built on a gate model, would be.
D-Wave future is a 5000 qubit chip within 2 years and doubling the qubits in chips every 2 years
D-Wave recently installed a 2000 qubit system with a customer. They are working on a 5000 qubit system now and will likely install it with a customer in less than 2 years. They are also working to broaden connectivity on their chips.
D-Wave has a redesign for their architecture to enable it to become a general purpose quantum system
D-Wave has modifications to their architecture which COULD convert their annealing system into a general purpose quantum computing system. It will require more control over the qubits. It would still be annealing. It would be programmable and allow for multiple quantum programs at the same time and allow for repeatable answer runs.
The general purpose annealing machine project would require several tens of millions in funding.
This month there was another Paper where Dwave 2000 qubit system was shown to be faster than classical computers
A paper by Salvatore Mandr`a (NASA) and Helmut G. Katzgraber (Texas A and M) has found another problem where D-Wave systems provides a faster solution than classical computers.
Abstract – There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines. To date, classical heuristics have consistently outperformed quantum-annealing based approaches. Here we introduce a class of problems based on frustrated cluster loops — deceptive cluster loops — for which all currently known state-of-the-art classical heuristics are outperformed by the DW2000Q quantum annealing machine. While there is a sizable constant speedup over all known classical heuristics, a noticeable improvement in the scaling remains elusive. These results represent the first steps towards a detection of potential quantum speedup, albeit without a scaling improvement and for synthetic benchmark
The speedup is a few hundred to a few thousand times over classical systems.
Quantum computer working with regular computers
For most applications that Bo can imagine for the next few years, he does not think quantum computing will replace classical computing, but will rather be used alongside classical computers. It will be like where GPUs were used to accelerate certain types of problems. GPUs became more general purpose over two decades.
Perhaps with one or two exceptions, quantum computing will follow a similar path, [Bo] believes. Today’s quantum “accelerators” will become more general purpose over time.
The next engineering challenge is to figure out how to dramatically improve the connectivity between qubits (while simultaneously improving precision, reducing noise, etc.). To increase the generality of the gate model systems, in addition to increasing the number of qubits and improving their characteristics, determining how many error-correcting qubits will be needed and adding them will be the next chasm to cross.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.