The scientific method, in a nutshell, is to make an assumption then test it against evidence to increase knowledge. To evaluate a scientific theory, I first assume it is true then follow the logic to see if it fits the facts. In computing, implementing an information system involves first design, to create a logical model, then building and testing to validate it.
Design science then follows science in first designing an information system in theory and then testing it against requirements in an iterative way (Hevner, March, & Park, 2004). Reverse engineering is a subset of design science, where given an output, one deduces the processing behind it. The method in this case is to first best-guess the processing involved, then test its output against the observed output, and repeat until it consistently simulates. Scientists that run quantum simulations to predict physical results use this method.
Quantum realism puts the research question “Does quantum processing generate physical reality?” and uses the method of reverse engineering to answer it as follows:
1. Specify: Specify the output of the physical world (physics).
2. Design: Design quantum processing to satisfy those requirements (computer science).
3. Validate: Validate the expected output against the actual output (science).
4. Repeat: Given no invalidation or inconsistency, repeat until a feasible design is achieved (quantum realism).
The consistency constraint is critical, as while one can easily “fit” a system to one requirement, satisfying many is much harder. In addition, the design should:
1. Follow computing best practice. Based on established computer science principles.
2. Satisfy Occam’s razor. Given a design choice, take the simpler option.
The aim of quantum realism is to reverse engineer the physical world, to derive the laws of physics from information first principles in a “Physics from scratch” approach (Tegmark, 2007 p6). Cherry-picking cases, so that selected programs mimic some world properties is not a new kind of science but an old kind of bias (Wolfram, 2002). Science can’t choose what a model explains, so quantum realism must explain all of physics including space, time, energy, matter, gravity, magnetism, spin and charge.
Reverse engineering physical reality could reveal quantum realism to be:
1. Spurious. A spurious model adds no value because it needs new assumptions or parameters to explain every new fact. Spurious models always have a back-door excuse.
2. Coincidence. Coincidental models work for a while by luck but fail over time as supporters have to cherry-pick cases to support the model and ignore those that contradict it.
3. Useful. A useful model isn’t necessarily entirely true but it opens up productive new research that leads to a better model or theory. It is a useful stepping-stone.
4. True. A true model is based on essentially valid assumptions about reality. Given a few assumptions, it matches observed reality in many ways and predicts something new that contradicts established models but is later found to be true.
Given the circumstantial case presented earlier, that quantum realism warrants further investigation, even if it is not entirely true it may provide useful insights. The aim is to attribute the results of current physics to quantum processing based on quantum theory and general computer science. If physics describes physical events and computer science describes processing events, whether quantum processing could produce physical events is a question design science can evaluate. Whatever the outcome, quantum realism poses a question that science can answer, but only by exploration can that answer be discovered. To reject the question out of hand is not science.