According to Google, The quantum computer in its lab is 100 million times faster than any other traditional computer in the facility. In less esoteric terms, the Google quantum computer generates 2.5 exabytes of data per day, which is comparable to 5 million laptops. Full disclosure… commercial use of quantum computing is 5 to 10 years away. Lab environments, however, are promising. Read on as we imagine the future of computing.
Quantum computing will touch many industries and, according to Honeywell, could become a one trillion dollar industry in the coming decades. Without getting into the deep math details of quantum mechanics versus classical physics, quantum computers are not just faster binary computers. They approach data in an entirely different method than traditional computers.
The classic computational process in use today is based on making a series of individual decisions. Computers increase performance through faster processing and/or parallel processing of binary decisions. Traditional computers are very good at solving math problems but tend to get bogged down when there are many different combinations of data that need to be solved in order to find the best solution.
Data in the quantum realm is represented not in binary bits and bytes of 1s and 0s, but in non-binary quantum bits or qubits. While a classical binary computer bit must be in a 1 state or a 0 state, quantum mechanics allows data to be in a coherent superposition of both states simultaneously. Quantum computers can solve problems involving numerous combinations and permutations significantly faster than regular computers because of this superpositioning.
On the way to work, commuters drive down the road in cars. While driving, they scan the road, listen to the radio, make constant adjustments to the speed, braking, and steering. They are also managing the space between their car and the cars around them. The situation is constantly changing. As they drive their car, they must also instantly adjust to the actions of other cars on the road as those cars change lanes, speed up, slow down, stop for a red light or perhaps even swerve into another lane. These adjustments are made by the driver observing and calculating multiple data inputs simultaneously.
Other data points, such as pedestrians eating at a sidewalk cafe or a stop sign on the next block are not important to our immediate driving decisions. They are still data but not important until we observe them and determine that they need to be evaluated. This is one of the key tenets of quantum mechanics. Data is not real until it is observed. One of the critical shortfalls of autonomous driving cars is the binary computing method of making decisions including the need to solve for all multiple possibilities one combination at a time.
In a highly simplified example, a self-driving vehicle senses a pedestrian standing on a street corner. The automobile sensors detect that the light is green and it is legal to make a right turn. The self-driving vehicle, however, can also sense the presence of a pedestrian but can not predict the intentions of the pedestrian. What are the probabilities that the pedestrian will attempt to cross the street in front of the vehicle as it starts to turn? Traditional computing might ask, “Is it legal to turn now?” if Yes, turn. If No, don’t turn. That is a binary decision.
However, in this instance, there are many more variables that can be observed and played out in this scenario. The human mind can instantly observe the site and calculate the probabilities of a successful turn with no pedestrian injuries in an instant. An onboard quantum computer may be able to sense, record and process all the necessary information including the signal lights and potential leanings of the pedestrian. Using important observed data and ignoring other data points such as a light post or a mailbox, the quantum computer will instantly determine the probabilities of a successful turn and execute the turn with no injuries or other issues.
Impact on Artificial Intelligence
Artificial Intelligence (AI) technology continues to advance using massive computing power to analyze data and predict outcomes. However, the binary method of traditional computing requires lengthy calculations to review all possibilities before delivering an outcome. AI computers are very good at solving complex mathematical problems. However, when there are many variables and combinations of those variables, the computation becomes exponentially more time-consuming and difficult. AI is not yet proficient at solving problems with human-like intelligence such as reasoning, generalization, or discovering meaning. The use of quantum computing may greatly advance the use and scope of AI by achieving faster and more accurate predictions.
Combinatorics is solving problems of a discreet set of variables with many different combinations and permutations to arrive at an optimal solution. Biological sciences, working to develop new medicines, vaccines, and treatments, will be able to advance their research using quantum computing. Computations of variables such as the movement of molecules and the interaction of sub-atomic particles require a massive number of variables. Quantum computing will help advance the development of new materials and drug development.
Using a binary computer to brut force a password of seven characters consisting of both upper case and lower case letters takes two seconds. That same computer will take 64,000 years to crack a 14 character password consisting of both upper and lower case letters. The reason, of course, is that the binary computer will have to try each possible combination of those letters in upper and lower case one combination at a time. A quantum computer may be able to crack the 14 character password in 8 hours.
As quantum computing becomes mainstream, certain types of asymmetric encryption techniques such as RSA and ECDSA may become more vulnerable to cyber-attacks. Of course, as quantum computing becomes commercialized, new encryption algorithms will likely be developed to help keep infrastructure and information secure. Just as quantum computing may be used to crack some of the older encryption algorithms, these same computers will be used to develop new, more secure algorithms.
Regardless of whether your network is using binary or quantum computing, it is still necessary to maintain constant visibility of the traffic on network links. The scientists at Network Critical have recently introduced network monitoring and cyber security products that can inform network managers and protect network information. A recent introduction is INVIKTUS™, a network security tool that is invisible to the network and physically un-hackable by any type of computing technique, quantum or otherwise. Simply put, hackers can not hack what they can not see.
Although we may be 5 or more years away from commercial viability of quantum computing, it is prudent to be aware of computing trends and their potential impact on our business, our network, and our world. Network Critical is committed to research and development of cyber security and network monitoring tools. For more information about INVIKTUS™ and other advanced cybersecurity and monitoring products go to www.networkcritical.com/contact-us. Follow us to stay visible, protected, and updated with current trends.