Which quantum computer will actually scale? In Part 2 of our
quantum computing deep dive, Lester Nare and Krishna Choudhary move
from theory to hardware—comparing superconducting qubits, trapped
ions, neutral atoms, and silicon spin qubits before going inside
the new Nature cover paper Krishna co-authored with the HRL Quantum
Team and collaborators. The episode begins with a simple question:
what makes a good quantum computer? We evaluate each architecture
using three criteria: qubit quality, qubit control, and scalability
and economics. Superconducting qubits offer extremely fast
operations, but scaling them introduces challenges involving
microwave control, frequency crowding, cryogenic wiring, physical
size, and cooling. Trapped ions preserve quantum information for
extraordinary lengths of time, but their slower gates and
increasingly complex optical systems introduce a different set of
tradeoffs. Neutral atoms can be arranged in dense, reconfigurable
arrays using optical tweezers and entangled through Rydberg
interactions, while raising questions involving atom loss,
correlated noise, readout, and execution time. Then we get to
silicon. Beginning with the Loss–DiVincenzo proposal, Krishna
explains how individual electron spins can be confined inside
semiconductor quantum dots, manipulated through exchange
interactions, and measured using single-electron transistors. We
then explore exchange-only qubits, where three electron spins
encode a single qubit and quantum gates can be performed using
electrical control. That leads to the Nature cover paper, A
digitally controlled silicon quantum processing unit. The HRL
system integrates 18 encoded qubits built from 54 quantum dots with
cryogenic control electronics, a superconducting interconnect,
automated calibration, and an engineered silicon-germanium
heterostructure. Krishna also explains his own work using machine
learning to automate quantum-device tuning—an essential problem if
spin-qubit systems are ever going to grow from dozens of components
to millions. The larger thesis is about manufacturing. The
semiconductor industry has spent decades learning how to fabricate
silicon devices at enormous scale. If quantum processors can
inherit that infrastructure, the architecture that ultimately wins
may not be the one that reaches the finish line first—but the one
humanity already knows how to manufacture. Nature paper:A digitally
controlled silicon quantum processing unitDOI:
10.1038/s41586-026-10754-7https://www.nature.com/articles/s41586-026-10754-7
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