Quantum computing is a new technology that uses quantum physics to address issues that are too difficult for traditional computers to solve.
Thousands of developers now have access to IBM Quantum’s true quantum hardware, which scientists just began to envisage three decades ago. At regular intervals, our engineers deploy ever-more powerful superconducting quantum processors, bringing us closer to the quantum computing speed and capacity required to revolutionize the world.
These machines are not like the traditional computers that have been around for almost half a century. Here’s a crash course in this game-changing technology.
Why do we need quantum computers?
Supercomputers aren’t perfect for all issues.
Supercomputers are used by scientists and engineers to solve challenging issues. These are extremely powerful traditional computers with thousands of CPU and GPU cores. Even supercomputers, however, have difficulty solving some problems.
If a supercomputer becomes stumped, it’s most likely because it was asked to handle a problem with a high level of complexity. When traditional computers fail, it’s usually because of their complexity.
Complex problems are those that involve a large number of variables interacting in complex ways. Because of all the different electrons interacting with one another, modeling the behavior of individual atoms in a molecule is a difficult task. It’s also difficult to figure out the best routes for a few hundred tankers in a worldwide shipping network.
A supercomputer might struggle to discover the best seating arrangement for even ten visitors at a dinner party if they don’t all want to sit next to one other, or to find the prime factors of a large number.
Why are quantum computers more efficient?
Consider the following scenario to see how quantum computers can thrive where traditional computers fail:
A supercomputer may be particularly useful for challenging tasks such as sorting through a large database of protein sequences. However, it will have difficulty detecting the small patterns in the data that influence how proteins act.
Proteins are lengthy chains of amino acids that fold into complicated forms to create helpful biological machineries. The difficulty of predicting how proteins will fold has major ramifications in biology and medicine.
A traditional supercomputer could try to fold a protein by brute force, using its numerous processors to check every potential way of bending the chemical chain before coming up with a solution. The supercomputer, however, stalls as the protein sequences become longer and more complex. A 100-amino-acid chain may hypothetically fold in any of billions of different ways. No computer has enough working memory to process all of the conceivable fold configurations.
Quantum algorithms use a novel approach to solving these types of difficult issues by generating multidimensional spaces in which patterns linking individual data points emerge. In the case of a protein folding problem, such pattern may be the folds that need the least amount of energy to generate. The solution to the problem is that combination of folds.
Because traditional computers are unable to build new computational regions, they are unable to detect these patterns. In the case of proteins, there are already early quantum algorithms that can uncover folding patterns in whole new, more efficient ways, without the time-consuming checks that classical computers require. As quantum hardware improves and these algorithms improve, they may be able to solve protein folding issues that are too difficult for any supercomputer to solve.
How are We Trying to Get it?
Qauntum computers are extremely difficult to construct. On the scale of single atoms, many possible qubit systems exist, and physicists, engineers, and materials scientists attempting to perform quantum operations on these systems must continually balance two competing criteria.
To begin, qubits must be shielded from the environment, which can degrade the sensitive quantum states required for processing. The “coherence time” of a qubit is determined by how long it stays in its preferred state. Isolation is appreciated from this standpoint. Second, qubits must be entangled, moved around physical architectures, and programmable on demand for algorithm execution.
The higher the “fidelity” of these processes, the better they can be carried out. It’s tough to strike the right balance between isolation and interaction, but after decades of research, a few systems are emerging as leading contenders for large-scale quantum information processing.
Some of the primary platforms for creating a quantum computer are superconducting systems, trapped atomic ions, and semiconductors. Each has its own set of benefits and drawbacks in terms of coherence, fidelity, and ultimate scalability to massive systems. To be robust enough to perform significant calculations, all of these platforms will need some type of error correction protocol, and how to develop and implement these protocols is a large area of research in and of itself.
Measurement-based computation, in which highly entangled qubits serve as the starting point, is another framework. Then, rather of manipulating qubits, single qubit measurements are carried out, leaving the desired single qubit in a defined state. Further measurements on other qubits are carried out as a result of the result, and finally an answer is found.