The realm of computing is fundamentally structured around binary logic. Silicon transistors, existing in two states, have led to the extensive development of mathematical and logical frameworks. Quantum computing, for the most part, mirrors this binary approach, utilizing qubits that, when evaluated, reveal one of two potential states.
There are instances where the binary nature is intrinsic to the system that encapsulates the qubit. A notable example is the dual-rail qubit technology, which assesses the value based on whether a photon resides in one of two connected resonators. However, numerous quantum systems possess more than just two states, such as the various energy levels an electron can occupy while orbiting an atom. While it’s possible to utilize only the lowest two energy states for qubits, nothing prevents the use of additional states.
In the latest edition of Nature, researchers announce their achievements in creating qudits, which serve as a broader category for systems that manage quantum information—this term, derived from “quantum digits,” encompasses states beyond the traditional two. Their study showcases the pioneering implementation of error correction for higher-order quantum memory, employing systems with three or four states, known as qutrits and ququarts respectively.
Developing Ququarts
The adoption of more sophisticated qudits has lagged among quantum computing hardware developers for several reasons. One primary issue is that some hardware is inherently limited to two states. Additionally, the energy gaps between any extra states can be minimal and challenging to differentiate. There are also complexities associated with conducting operations on qudits, which may necessitate a distinctly different programming framework compared to standard qubit-dependent operations.
Despite these challenges, there is a compelling argument for advancing beyond qubits. Such a transition could enable greater functionality with reduced hardware requirements. Currently, major quantum computing initiatives face limitations related to hardware capabilities, hampering efforts to build sufficient qubits and interlink them for effective error correction needed to perform meaningful calculations. If more information could be incorporated into a smaller hardware footprint, it could potentially accelerate the path to viable calculations.
