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The quantum buzzwords have arrived. Just as with every other new technology, buzzwords are everywhere.

So just which quantum buzzwords do you really need to know, specifically when it comes to software. They’ll help you gain a basic understanding of quantum software programming.****

First, let’s talk about the buzzwords that define the claims around quantum today.

**Quantum advantage**refers to the demonstrated and measured success to process a real-world problem faster on a quantum computer than on a classic computer. Since QPUs (quantum processing units) are not yet scalable enough to run real-world problems, it will likely be years before we reach true quantum advantage. Anyone who claims quantum advantage right now is, well, a bit over the hype line.

**Quantum superiority**, on the other hand, refers to the demonstrated and measured success to process*any*problem faster on a quantum computer than on a classic computer. Since QPUs can process smaller problems right now, we can and have reached quantum superiority for some types of computations and simulations, where the data sets have been sampled and significantly compressed to fit onto a quantum computer. For example, a gate-model quantum system with 150 qubits can hold 150 variables. So the problem needs to be 150 variables or less.

Now, let’s do a quick review of some popular quantum hardware terms you’ll discover..

**QPUs,**or quantum processing units, (sometimes referred to as a quantum chip,) are physical chips that contain a number of interconnected qubits. It is the fundamental processing component of a quantum computer, which also includes the housing for the processor, the control electronics, and other necessary computing components.

**Qubits**are quantum bits.

Some of the software programming terms you need to know are:

**Quantum SDKs**, or software development kits, offer the deep level coding tools you’ll need to create new quantum gates and circuits, as well as the low level hardware coding needed to begin to form a quantum program.

**Low-level coding.**Unlike classical computers, quantum computers are not standardized in architecture, design or programming. Aside from the differences between annealing and gate model architectures, each vendor has proprietary hardware variations for their Quantum Processing Units (QPUs). Individual quantum computers and their qubits (quantum bits) must be configured using proprietary low-level coding techniques that map to each vendor and system. You also have to reprogram all coding every time you update your QPUs to new versions, or expand your number of QPUs. The best analogy is from early classical computing, when we had to use assembly code to write device drivers. Quantum is much more complicated.

**Quantum gates**are the building blocks of quantum circuits, like classical logic gates are the building blocks of classical circuits. Since quantum computers are three-dimensional, quantum gates are represented as arrays of vectors. Quantum gates are combined into quantum circuits to create a function or program flow, just as in classical computing.

**Quantum**circuits are a conceptual model of the steps in a quantum algorithm, composed of elementary quantum gates, much like classical circuits are composed of classical gates. Quantum algorithms function by applying instructions, or quantum gates, to qubits that contain subsets of data. Quantum circuits are represented as groups of quantum gates that connect qubits represented by wires. The quantum wires do not perform any transformations in a computational sense, they simply represent the change of state of the qubits based on the circuit flows.

If you’re optimizing an aspect of your business, such as your supply chain or job scheduling, you’re likely employing some type of constrained optimization. It turns out quantum computers can be very effective with this type of problem.

For constrained optimization computation, there are specific programs or algorithms you need to develop for different quantum vendors’ QPUs. These include:

**QUBO**Quadratic unconstrained binary optimization (QUBO), also known as unconstrained binary quadratic programming (UBQP), is a combinatorial optimization problem with a wide range of applications from finance and economics to machine learning. It is used to solve optimization problems on D-Wave quantum annealing computers. QUBOs are extremely difficult and time-consuming to create if you are manually creating quantum programs.

Optimization problems are commonly expressed in two part: an objective function, a value to be maximized (number of packages in a truck) or minimized (number of miles the truck drives) and a set of constraints, rules the solution has to meet, total weight of the packages on the truck must be less than 2000 pounds, packages going to the same address have to be next to each other. The constraints and objective function are usually represented in two separate arrays. To solve these sorts of problems with a QUBO, the constraints and objective function arrays must be mathematically combined into a single array and, frequently, the addition of penalty values to force the solution to meet the constraints. Combining the two arrays into a single QUBO array can be difficult and identifying the appropriate penalty values requires deep understanding of the problem and may also involve some trial and error.

**QAOA**Quantum optimization algorithms are used to solve optimization problems. The quadratic approximate optimization algorithm (QAOA) is a quantum algorithm used for combinatorial optimizations on gate model quantum computers

The above terms are merely a sampling of the components your teams need to learn before they can write a quantum program. Assuming they understand the fundamentals of quantum mechanics and the mathematics required to design the programs before they begin. If not, that’s another set of deep expertise you’ll need to acquire.****

Quantum software programming is complex, expensive and time consuming. Even with quantum experts, you should expect months to even years before you have a functional quantum program. After that, you’ll have to also address the lifecycle maintenance of your program code every time you change your QPUs or expand your system capabilities. If you change quantum QPU vendors, you’ll need to rewrite your program to include their low-level proprietary code to access their specific hardware.

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