Quantum computation (QC) provides well-known examples of hardware acceleration for specific problems, but is challenging to implement due to its sensitivity to small errors from noise or imperfect control. Fault-tolerance principles can allow computational acceleration with imperfect hardware, but they place strict requirements on the nature and correlation of errors.

For many qubit technologies, some challenges in achieving fault tolerance can be traced to correlated errors arising from the need to control qubits by injecting qubit resonances corresponding to microwave energy. HRL Laboratories, LLC, has published the first demonstration of universal control of encoded spin qubits. The experiment demonstrated universal control over their coded qubits, meaning the qubits can be successfully used for any kind of quantum computational algorithm implementation.

This newly emerging approach to quantum computing uses a new silicon-based qubit device architecture that traps single electrons in quantum dots. Three of these single electron spins have energy-degenerate qubit states governed by nearest-neighbor contact interactions that partially swap neighboring spin states.

Because the experiment showed that their encoded qubits could be universally controlled, any quantum computing technique could be effectively implemented using the qubits. The coded silicon/silicon germanium quantum dot qubits use three electron spins and a control technique in which voltages applied to metal gates partially swap the directions of those electron spins without ever aligning them in any particular order.

During the demonstration, dozens of these carefully calibrated voltage pulses were applied in rapid succession a few millionths of a second apart.

The isotopically enriched silicon used, the all-electric control of low-crosstalk partial switching operations, the configurable insensitivity of the coding to specific fault sources, and the quantum coherence they provide all work together to provide an essential path to scalable fault tolerance and computational advantage , which are crucial steps towards a commercial quantum computer.

HRL scientist and first author Aaron Weinstein said: *“In addition to the obvious design and manufacturing challenges, there was a lot of robust software to write, for example to tune and calibrate our control scheme. Much effort has gone into developing efficient, automated routines to determine which voltage applied led to what degree of partial swapping. Since thousands of such operations had to be performed to determine the level of error, each operation had to be accurate. We worked hard to make all that control work with high precision.”*

HRL group leader and co-author Mitch Jones said: *“This was really a team effort. The facilitating work of talented control software, theory, device growth and manufacturing teams was crucial. In addition, many measurements of devices were needed to understand enough of the internal physics and to develop routines to reliably control these quantum mechanical interactions. This work and demonstration is the culmination of those measurements, made even better by collaborating with some of the brightest scientists I’ve met.”*

Thaddeus Ladd, HRL group leader and co-author said: *“It’s hard to define what the best qubit technology is, but I think the qubit that just swaps silicon is at least the best balanced. Real challenges remain in improving errors, scale, speed, uniformity, crosstalk and other aspects, but none of these require a miracle. For many other types of qubits, at least one aspect still seems difficult.”*

If scaled up, quantum computers would differ from conventional supercomputers in that they would use the fragile property of quantum physics, known as quantum entanglement, to perform certain calculations that would normally take years or decades on traditional computers. The simulation of the behavior of large molecules is one of many conceivable applications.

**Magazine reference:**

- Aaron J. Weinstein et al., Universal logic with encoded spin qubits in silicon, Nature (2023). DOI: 10.1038/s41586-023-05777-3