9 research outputs found
Optimizing quantum gates towards the scale of logical qubits
A foundational assumption of quantum error correction theory is that quantum
gates can be scaled to large processors without exceeding the error-threshold
for fault tolerance. Two major challenges that could become fundamental
roadblocks are manufacturing high performance quantum hardware and engineering
a control system that can reach its performance limits. The control challenge
of scaling quantum gates from small to large processors without degrading
performance often maps to non-convex, high-constraint, and time-dependent
control optimization over an exponentially expanding configuration space. Here
we report on a control optimization strategy that can scalably overcome the
complexity of such problems. We demonstrate it by choreographing the frequency
trajectories of 68 frequency-tunable superconducting qubits to execute single-
and two-qubit gates while mitigating computational errors. When combined with a
comprehensive model of physical errors across our processor, the strategy
suppresses physical error rates by compared with the case of no
optimization. Furthermore, it is projected to achieve a similar performance
advantage on a distance-23 surface code logical qubit with 1057 physical
qubits. Our control optimization strategy solves a generic scaling challenge in
a way that can be adapted to other quantum algorithms, operations, and
computing architectures
Measurement-induced entanglement and teleportation on a noisy quantum processor
Measurement has a special role in quantum theory: by collapsing the
wavefunction it can enable phenomena such as teleportation and thereby alter
the "arrow of time" that constrains unitary evolution. When integrated in
many-body dynamics, measurements can lead to emergent patterns of quantum
information in space-time that go beyond established paradigms for
characterizing phases, either in or out of equilibrium. On present-day NISQ
processors, the experimental realization of this physics is challenging due to
noise, hardware limitations, and the stochastic nature of quantum measurement.
Here we address each of these experimental challenges and investigate
measurement-induced quantum information phases on up to 70 superconducting
qubits. By leveraging the interchangeability of space and time, we use a
duality mapping, to avoid mid-circuit measurement and access different
manifestations of the underlying phases -- from entanglement scaling to
measurement-induced teleportation -- in a unified way. We obtain finite-size
signatures of a phase transition with a decoding protocol that correlates the
experimental measurement record with classical simulation data. The phases
display sharply different sensitivity to noise, which we exploit to turn an
inherent hardware limitation into a useful diagnostic. Our work demonstrates an
approach to realize measurement-induced physics at scales that are at the
limits of current NISQ processors
Non-Abelian braiding of graph vertices in a superconducting processor
Indistinguishability of particles is a fundamental principle of quantum
mechanics. For all elementary and quasiparticles observed to date - including
fermions, bosons, and Abelian anyons - this principle guarantees that the
braiding of identical particles leaves the system unchanged. However, in two
spatial dimensions, an intriguing possibility exists: braiding of non-Abelian
anyons causes rotations in a space of topologically degenerate wavefunctions.
Hence, it can change the observables of the system without violating the
principle of indistinguishability. Despite the well developed mathematical
description of non-Abelian anyons and numerous theoretical proposals, the
experimental observation of their exchange statistics has remained elusive for
decades. Controllable many-body quantum states generated on quantum processors
offer another path for exploring these fundamental phenomena. While efforts on
conventional solid-state platforms typically involve Hamiltonian dynamics of
quasi-particles, superconducting quantum processors allow for directly
manipulating the many-body wavefunction via unitary gates. Building on
predictions that stabilizer codes can host projective non-Abelian Ising anyons,
we implement a generalized stabilizer code and unitary protocol to create and
braid them. This allows us to experimentally verify the fusion rules of the
anyons and braid them to realize their statistics. We then study the prospect
of employing the anyons for quantum computation and utilize braiding to create
an entangled state of anyons encoding three logical qubits. Our work provides
new insights about non-Abelian braiding and - through the future inclusion of
error correction to achieve topological protection - could open a path toward
fault-tolerant quantum computing
3D Hand Tracking in a Stochastic Approximation Setting
Abstract. This paper introduces a hand tracking system with a theoretical proof of convergence. The tracking system follows a model-based approach and uses image-based cues, namely silhouettes and colour constancy. We show that, with the exception of a small set of parameter configurations, the cost function of our tracker has a well-behaved unique minimum. The convergence proof for the tracker relies on the convergence theory in stochastic approximation. We demonstrate that our tracker meets the sufficient conditions for stochastic approximation to hold locally. Experimental results on synthetic images generated from real hand motions show the feasibility of this approach.
3D Hand Tracking in a Stochastic Approximation Setting
This paper introduces a hand tracking system with a theoretical proof of convergence. The tracking system follows a model-based approach and uses image-based cues, namely silhouettes and colour constancy. We show that, with the exception of a small set of parameter configurations, the cost function of our tracker has a well-behaved unique minimum. The convergence proof for the tracker relies on the convergence theory in stochastic approximation. We demonstrate that our tracker meets the sufficient conditions for stochastic approximation to hold locally. Experimental results on synthetic images generated from real hand motions show the feasibility of this approach
Using an adaptive VAR model for motion prediction in 3D hand tracking
A robust VAR-based (vector autoregressive) model is introduced for motion prediction in 3D hand tracking. This dynamic VAR motion model is learned in an online manner. The kinematic structure of the hand is accounted for in the form of constraints when solving for the parameters of the VAR model. Also integrated into the motion prediction model are adaptive weights that are optimised according to the reliability of past predictions. Experiments on synthetic and real video sequences show a substantial improvement in tracking performance when the robust VAR motion model is used. In fact, utilising the robust VAR model allows the tracker to handle fast out-of-plane hand movement with severe self-occlusion