When scientists and engineers encounter difficult problems, they turn to supercomputers. These are very large classical computers, often with thousands of classical CPU and GPU cores capable of running very large calculations and advanced artificial intelligence. However, even supercomputers are binary code-based machines reliant on 20th-century transistor technology. They struggle to solve certain kinds of problems.

If a supercomputer gets stumped, that's probably because the big classical machine was asked to solve a problem with a high degree of complexity. When classical computers fail, it's often due to complexity.

Complex problems are problems with lots of variables interacting in complicated ways. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. Identifying subtle patterns of fraud in financial transactions or new physics in a supercollider are also complex problems. There are some complex problems that we do not know how to solve with classical computers at any scale.

The real world runs on quantum physics. Computers that make calculations by using the quantum states of quantum bits should in many situations be our best tools for understanding it.

Let's look at an example that shows how quantum computers can succeed where classical computers fail:

A classical computer might be great at difficult tasks like sorting through a big database of molecules. But it struggles to solve more complex problems, like simulating how those molecules behave.

Today, if scientists want to know how a molecule will behave they must synthesize it and experiment with it in the real world. If they want to know how a slight tweak would impact its behavior, they usually need to synthesize the new version and run their experiment all over again. This is an expensive, time-consuming process that impedes progress in fields as diverse as medicine and semiconductor design.

A classical supercomputer might try to simulate molecular behavior with brute force, by using its many processors to explore every possible way every part of the molecule might behave. But as it moves past the simplest, most straightforward molecules available, the supercomputer stalls. No computer has the working memory to handle all the possible permutations of molecular behavior by using any known methods.

Quantum algorithms take a new approach to these sorts of complex problems—creating multidimensional computational spaces. This turns out to be a much more efficient way of solving complex problems like chemical simulations.

We do not have a good way to create these computational spaces with classical computers, which limits their usefulness without quantum computation. Industrial chemists are already exploring ways to integrate quantum methods into their work. This is just one example. Engineering firms, financial institutions, global shipping companies—among others—are exploring use cases where quantum computers could solve important problems in their fields. An explosion of benefits from quantum research and development is taking shape on the horizon. As quantum hardware scales and quantum algorithms advance, many big, important problems like molecular simulation should find solutions.

An IBM Quantum processor is a wafer not much bigger than the one found in a laptop. And a quantum hardware system is about the size of a car, made up mostly of cooling systems to keep the superconducting processor at its ultra-cold operational temperature.

A classical processor uses classical bits to perform its operations. A quantum computer uses qubits (CUE-bits) to run multidimensional quantum algorithms.

Superconductors

Your desktop computer likely uses a fan to get cold enough to work. Our quantum processors need to be very cold–about a hundredth of a degree above absolute zero—to avoid “decoherence,” or retain their quantum states. To achieve this, we use super-cooled superfluids. At these ultra-low temperatures certain materials exhibit an important quantum mechanical effect: electrons move through them without resistance. This makes them "superconductors."  

When electrons pass through superconductors they match up, forming "Cooper pairs." These pairs can carry a charge across barriers, or insulators, through a process known as quantum tunneling. Two superconductors placed on either side of an insulator form a Josephson junction.

Control

Our quantum computers use Josephson junctions as superconducting qubits. By firing microwave photons at these qubits, we can control their behavior and get them to hold, change and read out individual units of quantum information.

Superposition

A qubit itself isn't very useful. But it can perform an important trick: placing the quantum information it holds into a state of superposition, which represents a combination of all possible configurations of the qubit. Groups of qubits in superposition can create complex, multidimensional computational spaces. Complex problems can be represented in new ways in these spaces.

Entanglement

Quantum entanglement is an effect that correlates the behavior of two separate things. Physicists have found that when two qubits are entangled, changes to one qubit directly impact the other.

Interference

In an environment of entanged qubits placed into a state of superposition, there are waves of probabilities. These are the probabilities of the outcomes of a measurement of the system. These waves can build on each other when many of them peak at a particular outcome, or cancel each other out when peaks and troughs interact. These are both forms of interference.

A computation on a quantum computer works by preparing a superposition of all possibile computational states. A quantum circuit, prepared by the user, uses interference selectively on the components of the superposition according to an algorithm. Many possible outcomes are cancelled out through interference, while others are amplified. The amplified outcomes are the solutions to the computation.

IBM's quantum computers are programmed by using Qiskit, our open-source, python-based quantum SDK. Qiskit has modules that cover applications in finance, chemistry, optimization and machine learning.

• Check out the documentation to get started quickly and learn more about our suite of developer tools.
• Build research and development-level code to run on simulators or real hardware.
• Join our growing community of 400,000+ users.

Ready for larger workloads? Execute at scale with Qiskit Runtime, our quantum programming model for efficiently building and scaling workloads. Qiskit Runtime enables users to deploy custom quantum-classical applications with easy access to HPC hybrid computations on the highest performing quantum systems in the world.

Qiskit Runtime provides an execution environment for weaving together quantum circuits with classical processing, natively accelerating the execution of certain quantum programs. This means faster iteration, reduced latency and more uninhibited compute time on the world's leading quantum systems: Qiskit Runtime's cloud-based execution model demonstrated a 120x speedup in simulating molecular behavior.