A fast-developing technology called quantum computing uses the principles of quantum physics to solve issues that are too complicated for conventional computers. A technology that scientists had only just begun to envisage thirty years ago is now made accessible to thousands of developers thanks to IBM Quantum. Every so often, our engineers release superconducting quantum processors that are ever more potent, progressing toward the speed and capacity of quantum computing required to revolutionize the world. These devices differ significantly from the traditional computers that have been in use for more than 50 years. Here is an introduction to this revolutionary technology.
Why do we need quantum computers?
Supercomputers aren't all that super for some issues. Supercomputers are used by scientists and engineers when they are faced with challenging tasks. These are enormous classical computers that frequently have thousands of cores for both the CPU and GPU. However, some types of issues are difficult for even supercomputers to solve. When a supercomputer struggles, it's often because the large classical machine was given a challenging problem to answer. Complexity is frequently the cause of failure for conventional computers. Multiple variables that interact in intricate ways are considered complex problems. Because there are so many different electrons interacting with one another, modeling the behavior of individual atoms in a molecule is a challenging task. It is difficult to determine the best paths for a few hundred tankers in a vast transportation network.
Why quantum computers are faster
Let's look at an illustration of how quantum computers can outperform traditional computers:
When it comes to challenging jobs like sorting through a vast library of protein sequences, a supercomputer may excel. However, it will find it difficult to spot the minute patterns in that data that govern how those proteins act. Long chains of amino acids called proteins fold into intricate forms to form vital biological machinery. Understanding protein folding is a challenge with significant biological and medical ramifications.
A traditional supercomputer would use brute force to attempt protein folding, using its numerous processors to examine every potential configuration of the chemical chain before concluding. The supercomputer, however, stops as the protein sequences grow longer and more complicated. Theoretically, a chain of 100 amino acids may fold in one of many trillions of different ways. No computer has enough working capacity to store all the different ways that individual folds could be combined. To solve these kinds of difficult problems, quantum algorithms create multidimensional spaces where the relationships between the many data points begin to take shape. That pattern might be the combination of folds that requires the least amount of energy to create in the case of a protein folding issue. The problem's solution is the arrangement of folds.
Because traditional computers cannot build these computational spaces, they are unable to detect these patterns. Early quantum algorithms are now available to uncover protein folding patterns in wholly novel, more effective methods without the time-consuming verification steps of classical computers. These algorithms may be able to solve protein folding issues that are beyond difficult for any supercomputer when quantum hardware scales and develops.
How complexity stumps supercomputers
Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.
A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.
Quantum computers are built for complexity
Quantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.
How do quantum computers work?
Compared to supercomputers, quantum computers are more elegant machines that use less energy. A laptop's wafer is comparable in size to an IBM Quantum processor. A quantum hardware system, which is roughly the size of a vehicle and consists primarily of cooling devices to maintain the superconducting processor at its extremely low operational temperature, is also of this size. Bits are used by a traditional processor to carry out its functions. A quantum computer uses qubits (CUE-bits) to run multidimensional quantum algorithms.
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 achieve this, we use super-cooled superfluids to create superconductors.
At those ultra-low temperatures, certain materials in our processors exhibit another 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.
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.
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 is a quantum mechanical effect that correlates the behavior of two separate things. When two qubits are entangled, changes to one qubit directly impact the other. Quantum algorithms leverage those relationships to find solutions to complex problems.