Nextbigfuture spoke with an experienced quantum computing machine learning solutions lead from 1qbit. 1qbit solves problems with quantum computer solutions with D-Wave Systems quantum and with Fujitsu’s digital (classical computer hardware) annealing system. They also have worked with many other gate-based quantum systems. 1QBit solves industry’s most demanding computational challenges by recasting problems to harness the power of quantum computing.
From Regular Computers Through Simulated Quantum Computers and the Types of Quantum Computers
Smart people can solve extremely difficult problems with minimal computing capabilities. Alan Turing and the Bletchley Park team broke the German Enigma code in WW2. The German airforce and army Enigmas could be set up 1.5×10^19 ways. In 1941 the German navy introduced a version of Enigma with rotatable reflector (the M4 or Four-rotor Enigma) for communicating with its U-boats. This could be set up in 1.8×10^20 different ways.
Classical computers have no quantumness but they can solve a lot of problems.
There are now digital annealing systems from Fujitsu. They could have up to 1 million simulated annealing qubits. Annealing systems are less powerful than superconducting noisy qubits. However, those simulated annealing qubits are inferior to D-Wave systems actual quantum annealing systems which have entanglement of qubits and some other actual quantum properties.
However, the digital annealing systems help to solve problems beyond the capability of smart people using classical computers. The large optimization problems can be broken down into a series of smaller problems but some of the smaller problems can be bottlenecks. Systems with more quantumness are able to solve more of the bottleneck issues.
Digital annealers can solve many bottleneck problem areas but D-Wave Systems tend to solve more. However, digital annealers with more qubits could represent larger problems.
Further research could make the simulators of annealer and gate qubits and quantum systems better simulations.
There is some quantum specialness advantage that the D-Wave Systems quantum annealers have over the digital annealing hardware simulators.
The D-Wave Systems need more qubits to solve problems that can be solved with fewer qubits with gate NISQ systems.
There is still a lot of experimentation during this time where we do not know which systems might be better for particular instances of different problems.
1Qbit and QC Ware are making software interfaces to the different quantum systems and sending quantum problem to as many of the different systems at the same time to see which can produce an answer or better answers.
For important high-value optimization and financial problems that are mathematically hard problems (ie. we do not have any kind of computer to fully solve or get the complete right answers, we will have large teams of mathematicians and scientists working out the best approximate solutions. The teams try to get closer approximate answers computers where it was possible.
1QBit’s hardware-agnostic platform leverages the best available techniques to solve industry problems without the operational overhead of integrating with specialized classical and quantum hardware.
1QBit Partners’ applications are built on 1QBit’s API in their preferred programming language. The API is built on our internal software development kit (SDK) which can solve many NP-complete problems using both classical and quantum hardware. The SDK also contains methods for decomposing problems to push the limits of what’s possible with available hardware. New classical, quantum, and quantum-inspired hardware architectures are continually added to the SDK to enable rapid testing and adjustment of the combination of solvers and interfacing techniques that will generate the strongest solution.
The simulated quantum systems from Fujitsu and from the quantum annealing system from D-Wave are able to help solve extremely valuable and tough problems for machine learning, optimization, finance and new drug discovery. Most of the parts of the problems can be solved without quantum systems. Real value and solutions are being made.
The belief of the quantum experts is that the 1 million qubits of the digital annealer will not be able to factor numbers to break commercial encryption.
NP-hardness (non-deterministic polynomial-time hardness), in computational complexity theory, is the defining property of a class of problems that are, informally, “at least as hard as the hardest problems in NP”. A simple example of an NP-hard problem is the subset sum problem.
The traveling salesman problem (TSP) asks the following question: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?” It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.
The traveling purchaser problem and the vehicle routing problem are both generalizations of TSP. Better approximation answers for these problems could provide significant gains for the overall economic value of the entire world economy. UPS, Fedex, airlines and the military could have their large routing made more efficient.
Even without quantum computers of any kind we have been able to get okay but not great approximate answers.
Summary Range of Qubits for Different Computational Systems
1. Smart people have no qubits but are constantly researching and improving algorithms and understanding the phenomena and problems.
2. Smart people with classical computers and classical algorithms have been working on optimization and other problems. Regular supercomputers have tens of petaflops and will soon reach exaflop levels.
3. The regular people and systems also have simulated quantum annealing from Fujitsu. This does leverage actual quantum properties. There is no entanglement. This was at 8192 qubits nine months ago in a commercial chip. There are FPGAs which Fujitsu has dominant market share. Clearly, Fujitsu can make these 8192 simulated qubit chips in the thousands of units. There are many that have been provided to research groups in Toronto, Vancouver and Japan. Many of these chips are being used together. They have a research goal of solving problems using 128 chips which would have 1 million simulated qubits.
4. There are different simulations of quantum annealing using FPGAs which are more accurate than the algorithm used in the Fujitsu chip. There are research papers and some small scale implementations.
5. D-Wave Systems has been making physical quantum annealing superconducting chips for about 14 years. They have a commercial unit which they sell for about $10 million which has 2048 qubits. They have an improved architecture system with about 5640 qubits in the lab. This should be released commercially next year.
6. There are simulated quantum gate systems. Supercomputers have simulated a different gate model qubits. Gate qubits are programmed in more step-wise fashion which is more similar to traditional computing. Annealing systems are more like analog chips. Alibaba used a cloud computing setup to simulate over 80 gate qubits.
7. There are numerous companies working on many different kinds of gates systems and technologies. The dominant ones currently use superconducting systems with better quality qubits than D-Wave Systems or they use trapped ion.
These systems are in the well tested and available on the cloud 16-30 qubit range and then lab systems with 50-100 qubits. New systems with 128 qubits or more could arrive shortly.
Leading systems are by IBM, Intel, Google and more. There are startups Rigetti and IonQ.
There are also other technologies in Australia and photonic systems.
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.