QR1.5.2 Reverse Engineering Physical Reality

The scientific method in a nutshell is to make an assumption then test it to see what happens. Scientists evaluate a theory by assuming it is true then analyzing it to see if it predicts facts or not. In computer science, this involves designing a logical model, implementing it and then testing it against expectations.

Design science follows this method, as an information system is first designed in theory then built and tested against requirements in an iterative way (Hevner et al., 2004). Reverse engineering is a subset of design science, where given an output, one deduces the processing behind it. The method is to first best-guess the processing involved, then test the output this processing predicts against the observed output, and repeat until the results are consistent. Scientists that run quantum simulations to predict physical results use this method.

The following chapters will use this method of reverse engineering to answer the research question:

Does quantum processing generate physical reality?

The steps are as follows:

1. Specify: Specify the physical world output (physics).

2. Design: Design quantum processing to satisfy those requirements (computer science).

3. Validate: Compare the expected output with the actual output (science).

4. Repeat: Repeat to achieve a consistent design (quantum realism).

The consistency constraint is critical, as while one can easily “fit” a system to one requirement, satisfying many at once is much harder. In addition, the design should:

1. Follow computing best practice. Use established computer science principles.

2. Satisfy Occam’s razor. Given a design choice, take the simpler option.

The aim is to reverse engineer the physical world, to derive the laws of physics from information first principles. A scientific theory can’t choose what it explains so quantum realism must explain all of physics including space, time, energy, matter, gravity, magnetism, spin and charge. Cherry-picking cases to find selected programs that mimic some world properties isn’t a new kind of science but an old kind of bias (Wolfram, 2002). 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 true but opens up productive new research that increases knowledge. It is a useful as a stepping-stone.

4. Valid. Based on a few assumptions, a valid model 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, quantum realism may provide useful insights even if it isn’t entirely true. If physics describes physical events and computer science describes processing events, whether quantum processing produces physical events is a question design science can evaluate.