In part 1, we looked at some preliminary concepts of what quantum computing is and how it compares with analog computing. Let's continue the analysis.
I contacted Dr. Ned Allen, chief scientist at Lockheed Martin, to discuss the relationship between quantum computing and analog computers. Lockheed Martin purchased the first commercial D-Wave One computer in 2012. In a Lockheed Martin video, Allen says quantum computing “might be considered the rebirth maybe of analog computing.” In an email, I asked him to elaborate. In his reply, he described the difference between quantum computing and digital computing with a reference to “analogue reckoning.”
Rather than holding discrete values of 0 or 1 the way transistors do, qubits in superposition (a quantum mechanical term meaning multiple states being simultaneously possible) can simultaneously have a value of 0, 1, and all values in between. Thus, qubits are more analog in nature than transistors.
I should note that the École Polytechnique Fédérale De Lausanne (EPFL) says it is working to develop transistors using quantum tunneling, along with IBM Zurich and Cea-Leti. The EPFL said in a press release that these devices will be commercialized by 2017 and will lower transistor power consumption by 100 times from present levels. Unfortunately, such chips still won't be any better at solving the problems for which quantum computers show talent.
Very few head-to-head performance comparisons exist between quantum and digital computers. This is partly because there are very few quantum computers of significant size, but it is also a challenge to devise problems where solutions can be properly compared. In a paper titled “Experimental Evaluation of an Adiabatic Quantum System for Combinatorial Optimization“, Catherine McGeoch of Amherst College and Cong Wang of Simon Fraser University reported a number of tests between a D-Wave Two quantum computer and a digital multi-processor computer with seven Intel quad-core processors. They reported that the quantum computer performed about 3,600 times better, using 439 working qubits. Colin Williams, director of business development and strategic partnerships at D-Wave, told the New York Times about the results: “For most problems, [the quantum computer] was 11,000 times faster, but in the more difficult 50 percent, it was 33,000 times faster.”
This kind of performance encouraged Google to initiate what it is calling the Quantum Artificial Intelligence Lab. The company will foot the bill for a D-Wave Two computer to be housed at the NASA Ames Research facility and managed by the Universities Space Research Association. Google said in a blog post on this topic that the goal is to “study how quantum computing might advance machine learning”. The company also said it has a general interest in the “highly difficult” field of machine learning.
It is interesting to consider what might be done with future quantum computers if they become more widespread. Synaptic Laboratories has been considering the impact of quantum computing on data security for several years. The Maltese company says on its website that many cryptographic techniques employed today, particularly the public key infrastructure (PKI), will be quite easy for powerful quantum computers to break.
Benjamin Gittins, CTO of Synaptic, told me in an email, “One thing we do know is that fundamental flaws (see this paper) already exist in the global PKI model.” Nevertheless, “it is still unclear how many qubits might be needed in a D-Wave computer to break public key crypto in practice.”
Perhaps of more relevance to this forum are the possibilities to automate validation processes, such as validating an analog IC design or simulating a mixed-signal IC and validating firmware for it. There are very good design tools available now, but a quantum computer might be better at testing a large range of input conditions, and it might handle the analog parts more naturally. This is speculation, but I can imagine a validation system using a quantum computer that would avoid number-of-bits precision limitations during a simulation of an analog IC. Perhaps the ultimate in analog design will be enabled by advances in quantum computing.