Quantum and classical

Quantum and Classical Computing: Key Differences to Understand

Rebel Brown

Rebel Brown

Quantum and classical computing are as different as night and day.

We all know that quantum computing offers the potential of powerful solutions to complex computational problems. Quantum is expected to solve today’s problems faster and better than we ever imagined. More importantly, to efficiently solve currently unsolvable problems, and larger and larger future problems as our data and insights expand.

One reason for this promise is the key difference between classical computing’s binary data analysis vs quantum’s multi-dimensional analysis.

Quantum and Classical Computing

Classical Processing is Binary

With classical computing, today’s data volumes limit the performance and results that a classical application can achieve. As data grows, the volumes overload classical resources.

  • Serial processing in a binary space can’t handle the large data volumes of many problems. That limits the size and validity of analytics. This forces SMEs and programmers to compress/reduce and limit the data that is processed, resulting in a lower quality solution.
  • Classical computing results can be reproduced, since the results are always computed from the same exact “state.”
  • Classical computers generally return one result, limiting the power of decisions.

binary space

Quantum-Computing is Multi-Dimensional

Quantum approaches accelerate complex analysis since they can handle larger data volumes [1], while accelerating time-to-results and the quality of results.

  • Multi-dimensional, simultaneous analysis means that data can be structured to accelerate performance, better mirroring the natural multi-dimensional state of most problems. The world we live and act in is multi-dimensional. That means solving our real-world problem in the same state better reflects what actually happens in our environment.
  • Limitless size leads to increased validity of analytics and better-quality results
  • You cannot reproduce results with quantum since the physics of the “state” is constantly changing. That’s why quantum processing requires efficient complex mathematical iteration of results to assure that the best quality results are delivered back to the SME, application or workflow.
  • Quantum computers return a diversity of results, offering more and better opportunities to find the best possible solution in different situations, or states.

A New Paradigm Means New Everything

Quantum and classical are different because quantum computing is a completely new paradigm, for both hardware and software. It requires a new and highly technical set of skills to create the software that will drive quantum problems and results.

Quantum requires different software approaches.

  • With classical computing, a programmer writes software, using binary elements of one and zeros (abstracted by app dev software), that are serially processed.
  • Quantum problems are not programmed. Instead, a matrix of multiple elements is presented to a quantum computer in a format that is already pre-optimized for a qubit to resolve. For example, a Quadratic Unconstrained Binary Operation (QUBO) is used to create the quantum lattice for annealing machines, while that QUBO is converted to a quantum circuit using the Quadratic Approximation Optimization Algorithm (QAOA) for a gate model machine.
  • To create these packages, math, physics and quantum experts need to program complex circuits, algorithms and more to create the problem submission to the quantum system. They also have to program low level hardware configurations for each QPU type, and again for all upgrades or expansions.
  • The difference between linear binary programming and sequential, multi-dimensional presentation and optimization means that quantum requires highly trained quantum experts to define the problem and its processing to extract full benefit from the quantum computer.

multi-dimensional space

Thanks to superposition, multi-dimensional space enables a quantum computer to solve a large set of constraints in an optimization problem simultaneously, which dramatically accelerates the results.  For example:

  • Let’s take a simple problem (a 4×4) that has 16 potential combinations that could meet a constrained objective.
  • Classic computers have to check the validity of each option to see if it is true or false that it meets the criteria.
  • Thanks to superposition, a quantum computer can check all 16 options simultaneously.
  • Now apply that to problems with hundreds of thousands of potential combinations and you see the power of quantum

The Bottom Line

The power of quantum is obvious, even as we search for ways to process large problems on the limited number of qubits available today. The only question is, “How do we get there?”

Since quantum experts are difficult to find and expensive, organizations will see high costs and long lead-times for quantum results when using component-based tool kits to create the entire quantum workflow.

[1] as qubits become available.