Though quantum computers remain years away from practical application, researchers are making steady strides toward making them useful. Last week they announced an impressive advance against subatomic decay that renders most quantum computers unsuitable for anything other than research purposes.
Many companies are exploring how QCs can accelerate chemical reactions involved with drug development, while also exploring how they may enhance AI/ML systems, optimize factory floors/global supply chains/or model financial data.
1. Quantum entanglement
Scientists continue their search for an accessible quantum computer, with every new breakthrough bringing them one step closer. Recent successes could address two of the key hurdles – creating more qubits and decreasing errors.
Quantum computers differ from conventional ones in that their core units, known as qubits, can exist in various states rather than just binary 1 and 0. This allows them to perform complex calculations much more rapidly.
However, their quantum properties also make them challenging to develop and operate. Time-based errors can significantly interfere with computations, leading to failure. IBM claims they have found ways to manage such errors that could eventually be applied to larger quantum processors for reliability gains that outstrip classical ones – this process is known as discovery error mitigation.
2. Quantum error correction
Researchers have developed techniques to reduce errors in quantum computing, which uses qubits to store and process information. But an error-free quantum computer remains years or decades away.
However, several large companies and start-ups are making steady strides toward creating an effective quantum computer. Some, such as Helsinki-based Algorithmiq, claim they will demonstrate tangible advances in drug discovery and materials science within five years.
Other experts are focused on improving error correction, or the process of reducing the probability of incorrect measurements to zero by running quantum computation multiple times. Strategies include averaging, resampling and noise extrapolation (running multiple quantum computations with artificially variable noise levels and comparing results to find which one produces optimal results). All of these techniques could bring us one step closer towards quantum supremacy (when quantum computers can complete tasks that classical computers cannot in a realistic time frame).
3. Quantum memory
Google’s claim that quantum computing had cleared a major hurdle is hard to ignore; their evidence for their claims came in the form of an impressive demonstration involving a 54-qubit machine simulating molecular-level quantum mechanics.
Such simulations are an integral step towards creating quantum computers of our dreams, capable of performing tasks that conventional machines cannot. Scaling up the number of qubits (the units of information used in quantum computers) remains one of the key challenges to achieving this dream.
Scientists at Spain’s ICFO-Institut de Ciencies Fotoniques may have developed a breakthrough quantum memory technology that may assist with this goal. Their integrated quantum memory can store quantum states three orders of magnitude longer than was previously possible, and can even be used as part of a quantum repeater to allow data transmission over long distances – something vitally important since quantum communications rely on sharing entangled photons among distant parties.
4. Quantum algorithms
Quantum computers outperform classical computers in terms of speed. While both types operate using binary information (heads or tails), quantum computers take advantage of quantum computing’s unique properties for speed. Therefore, any algorithm designed for them must take full advantage of them to produce significant speed increases.
Thankfully, several powerful algorithms for quantum computing already exist, such as Shor’s algorithm for factoring and Grover’s algorithm which improves how computers search large unstructured databases or unordered lists.
While these quantum algorithms remain experimental, they provide a glimpse of quantum computing’s promise. As this technology matures further, it will likely enhance AI applications such as creating unhackable keys for cybersecurity or speeding up mapping genomes to reference DNA sequences to search for genes associated with cardiovascular disease or diabetes – opening the way to transformative new medical treatments as well as genomic technologies that aid understanding microbiology in new ways that would have previously been impossible.