Quantum software programming is critical to make quantum computers process actual results. Understanding how to create quantum software, and the implications for your business is vital to getting the most out of quantum computers.
In many ways, quantum software programming is the bigger challenge. That’s because programming quantum software requires a significant and new level of expertise. Expertise that classical programmers, Subject Matter Experts, and others don’t have. Only a rare few know right now to create quantum software. It’s a different programming paradigm, including extremely low-level coding that’s proprietary to each QPU (Quantum Processing Unit) manufacturer.
The differences between classical and quantum software programming are vast. So how do you get started?
Here are three fundamental shifts that will help you think about how to build quantum software to address your complex business problems.
1. Quantum Software Programming Isn’t Classical
First, don’t assume programming quantum computers is anything like programming the CPUs running your business today. You can’t simply make existing programs run faster using Quantum SDKs (Software Development Kit). Quantum programming is fundamentally a new paradigm.
Secondly, realize that if you choose to use an SDK approach and build new quantum software programs, it’s going to take a lot of time and resources before you can successfully build, test, tune and then successfully run a quantum program.
Since quantum software programming calls for such dramatic new skills and knowledge, the time-to-expertise will be significant, unless you can locate and hire these scarce resources. Even then, you’ll be hiring expensive quantum experts, as well as PhDs in math and physics—all significant investments. They’ll have to build quantum gates, circuits, pulses, algorithms, and more to create your program. If that isn’t complicated enough, they’ll also have to do extremely low-level coding for each QPU type to manage the QPUs for the program execution. And every time the QPUs are updated, or you add more, they’ll have to modify that low-level code as well.
That’s complex, time-consuming, and expensive.
2. Qubits Are Inherently Probabilistic
With quantum, you have to think about your problem, or request, in a completely different manner. Quantum processing reflects the multi-dimensional, multi-state nature of our world. Classical computers use binary bits and process in serial, as a thread. That’s quite a difference to manage.
Quantum algorithms work in terms of probabilistic factors, seeking out all probable outcomes that meet whatever constraints and objectives your constrained optimization problem requires. Creating a program from scratch to solve those problems is a long and complex process. It’s an even larger programming paradigm shift than the move from vector processing to parallel vectors, and to massively parallel and beyond.
The implications are threefold: 1) you can’t use classical programming paradigms and skills, 2) you’ll have to write code at a lower, more technical level than before, and c) you’ll have to visualize the problems and create programs to solve them, differently.
This can get complicated fast. Here’s why:
- In classical programs, bits of 1s and 0s represent data in states that are repeatable. Bits are binary, —either on or off—and can be measured or read without changing the state.
- In quantum programming, qubits are inherently probabilistic, meaning that two identical qubits can have different measurements based on their states. Qubits are vectors of 1s and 0s and can exist in the same state, different states, or in multiple states at the same time (that’s called superposition.) These outcomes are based on the moment in time when they are measured, and the probabilities present at that exact moment. You can only measure a qubit once. As it is measured, it begins to go through a state called decoherence, and it collapses into one of two states – just like a bit.
Running a problem in multi-dimensional space with multiple, probabilistic, answers being “true” at the same point in time is a lot to get your head around. But that’s the dynamic state of reality in our constantly changing world. Everything changes, every single nanosecond. Even your business.
The beauty is that with qubits, you can simulate the real world and all of its diverse probabilities, versus only a binary scenario. You just have to be able to transform your business problem and its story into the mathematical terms that represent them as qubits.
3. Quantum Software is Discrete
Now let’s add another twist. Quantum does not like floating-point calculations. It’s all about discrete problems, which as any mathematician knows, are more difficult to solve than floating points.
Let’s use the example of constrained optimization problems.
Classical “solvers” (software for constrained optimization with a single result) tend to not perform well when you give them a discrete problem. That’s why so many organizations have been forced to change their discrete problems ( e.g., one truck, one plane, one package) into continuous problems, allowing floating-point results. These organizations then have to address the decimals in the result to determine what discrete numbers best represent the floating-point result. This introduces the potential for error within the results.
This is another reason why quantum is enticing. Classical programs use a variety of advanced heuristics to find the best solutions to constrained optimization on binary variables. The software solutions are usually aligned with floating-point results – meaning you can have a result of 1.278 crates of toilet paper per load to meet your constraints for optimizing your stock in different warehouses or stores. That’s not exactly helpful when you need to optimize trucking deliveries to stores. How are you supposed to load .278 of a crate? And by the way, you can’t just round up or round down. That further reduces the accuracy of the result, and the decisions you make about your supply chain optimization.
Quantum computers work in discrete measurements – meaning an answer must be a discrete number, not a floating-point decimal. That means every single problem variable, constraint and goal have to be formed as a discrete number. When you visualize your problem and begin to create variables and more to transform into qubits, you have to be thinking about the upstream requirements for discrete processing. It also means that when you run your logistics problem on a quantum computer – you will get results that map to solving your business problem. No more partials for people, crates, packages, or planes.
The Bottom Line
Listen to the news about quantum. It’s truly game-changing technology, perhaps like none we will ever see again in our lifetimes.
But also get ready for it. To properly prepare your organization to take advantage of it requires an understanding of the fundamental quantum software programming shifts it will bring—and the complexity of programming these new systems.
That’s the reason we created Qatalyst, to make it easier for your SMEs and programmers to take advantage of quantum architectures, even today, without the complexity and enormous expense to do so.