Matthew Griffin, award winning Futurist and Founder of the 311 Institute is described as "The Adviser behind the Advisers." Recognised for the past five years as one of the world's foremost futurists, innovation and strategy experts Matthew is an author, entrepreneur international speaker who helps investors, multi-nationals, regulators and sovereign governments around the world envision, build and lead the future. Today, asides from being a member of Centrica's prestigious Technology and Innovation Committee and mentoring XPrize teams, Matthew's accomplishments, among others, include playing the lead role in helping the world's largest smartphone manufacturers ideate the next five generations of mobile devices, and what comes beyond, and helping the world's largest high tech semiconductor manufacturers envision the next twenty years of intelligent machines. Matthew's clients include Accenture, Bain & Co, Bank of America, Blackrock, Bloomberg, Booz Allen Hamilton, Boston Consulting Group, Dell EMC, Dentons, Deloitte, Deutsche Bank, Du Pont, E&Y, Fidelity, Goldman Sachs, HPE, Huawei, JP Morgan Chase, KPMG, Lloyds Banking Group, McKinsey & Co, Monsanto, PWC, Qualcomm, Rolls Royce, SAP, Samsung, Schroeder's, Sequoia Capital, Sopra Steria, UBS, the UK's HM Treasury, the USAF and many others.
WHY THIS MATTERS IN BRIEF
Tomorrow’s ultra powerful Quantum Computers could revolutionise the direction and development of future AI’s, and companies are making some interesting breakthroughs.
While many of Quantum computing’s practical applications remain largely unknown, with, arguably, only our imagination being the limit of what we can do with these ultra powerful machines, Rigetti Computing, which describes itself as a “full stack Quantum Computing company,” based out of California in the US, has proven that they could potentially be used to help make giant advances in how we create and develop new machine learning models.
Recently the company used Forest, their quantum development environment, and one of their prototype quantum computer chips to successfully run an Artificial Intelligence (AI) clustering algorithm, a technique that’s commonly used in machine learning to group data with similar features, and the accomplishment could one day mean that tomorrow’s quantum computers, which are slated to arrive in earnest around 2020, could have a significant impact on the future direction and development of tomorrow’s AI’s, a field that some experts are already starting to call Quantum Artificial Intelligence.
“This is a new path toward practical applications for quantum computers,” said Will Zeng, the company’s head of software and applications, “clustering is a really fundamental and foundational mathematical problem. No one has ever shown you can do this.”
To date, physicists have demonstrated how quantum computers could be used to perform tasks in cybersecurity and molecule simulation, but, until now, no one has been successful in using quantum technologies to run this type of machine learning algorithm.
The crossover between quantum machines and AI has been a hot topic since the early 90s when Elizabeth Behrman, a physics professor at Wichita State University, began experimenting with the core ideas of neural networks. Initially, it was a tough sell, but as the field of quantum computing continues to bloom, more and more scientists are trying their hand at this mesh of technologies. For the team at Rigetti, the relationship between quantum computers and machine learning seems almost organic, as both handle massive amounts of data with the ability to detect the formation of patterns.
“There is a natural combination between the intrinsic statistical nature of quantum computing…and machine learning,” said team physicist, Johannes Otterbach, in an interview.
Further research could position quantum computing as a means for speeding up machine learning processes and, ergo, advancing the realm of AI, but for the moment it’s too early to say whether or not the latest experiment in this field will deliver the lofty results the team hope.
While it may be a step in some direction, quantum computers are exceptionally puzzling machines and it’s still unclear just how useful, if at all, this clustering algorithm could be.
“We don’t really understand how and why classical machine learning works, so it seems that applying it to quantum might just further obfuscate an already obfuscated field,” said Christopher Monroe, a physicist at the University of Maryland and chief scientist of quantum startup, IonQ, another startup who are developing something known as general purpose quantum processors designed from the ground up to tackle a wide range of calculations.
Startups like Rigetti are running alongside some of the biggest tech companies in the business, such as IBM, Google and Microsoft, to chip away at the ever complex task of developing a useful quantum computer. Not only are researchers attempting to build a practical quantum machine, but they’re also working to find ways in which these machines could be most effectively used.
Unlike conventional computers that transmit data through a series of “bits,” labelled as 1s and 0s, quantum computers transmit data with “qubits,” using superposition and entanglement, which allows them to sort through massive amounts of data at far faster speeds. Already, quantum computers have outperformed earth’s most powerful supercomputers and, in theory, have the potential to scale to a point that was previously inconceivable. But, their complex inner workings continue to slow any major bounds of progress, and researchers believe it will take years before a fully functional machine can be applied usefully.
Rigetti is currently using a quantum machine that works in hybrid with a conventional computer to ease the pains of programming. They recently made their new 19-qubits quantum computer available through Forest, the company’s cloud computing platform. Following in the footsteps of IBM and Google, Rigetti’s move at making this technology available to the public will allow for more collaboration, experimentation, and hopefully bring us closer to unlocking quantum computing’s hidden range of potential.