* the Dwave system is broadly 15 times faster than commercial multi-core solvers
* the Dwave system can be 600 times faster
* Dwave is doubling the number of qubits every year or two
* there are over 2000 physical qubits on the latest chip but they guarantee 1000 qubits
* There system is speeding up hundreds to ten thousands of times as they double
* they redesign their chips every month or so for tuning and improving input and output and other bottlenecks and not just qubits
* applications are primarily optimization and machine learning
* they are getting to be the best solution for particular classes of large and complex optimization problems
* Google, Lockheed, military and others have been seeing if they can learn to use this system to their advantage for a few years
* Google and others are also exploring options around quantum system that are different from the Dwave design with more error correction
* Dwave’s design is adiabatic (a physical analog system) and not a gate like design
* there are dozens of vastly different approaches to quantum computing but the others have not reached significant scale versus the superconducting annealing approach
* in the future some of the different approaches will be developed and scaled. They could be better at different kinds of problems. Just as there are many kinds of semiconductor computing (RISC chips, GPUs, FPGAs etc…)
The latest Dwave 2X 1000-2048 qubit quantum annealing system TTT benchmark is as follows:
* The D-Wave 2X finds near-optimal solutions up to 600x faster (depending on inputs) than comparable times for the best known and highly tuned, classical solvers. This comparison uses the quantum anneal time of the D-Wave processor.
* The D-Wave 2X finds near-optimal solutions up to 15x faster than the solvers using total time measurements.
* The greatest performance advantage for the D-Wave 2X compared to the software solvers is seen on inputs with more challenging structures than simple random cases that have been the predominant focus in previous benchmarks. This means the hardware performance is showing its best performance against software solvers on hard problem instances.
* In cases where it could be calculated, the difference between “near-optimal” and “optimal” is quite small, less than one percent of the latter. The D-Wave 2X is up to 100x faster at finding good near-optimal solutions than optimal solutions.
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.