10 research outputs found
Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks
The expressive capacity of quantum systems for machine learning is limited by
quantum sampling noise incurred during measurement. Although it is generally
believed that noise limits the resolvable capacity of quantum systems, the
precise impact of noise on learning is not yet fully understood. We present a
mathematical framework for evaluating the available expressive capacity of
general quantum systems from a finite number of measurements, and provide a
methodology for extracting the extrema of this capacity, its eigentasks.
Eigentasks are a native set of functions that a given quantum system can
approximate with minimal error. We show that extracting low-noise eigentasks
leads to improved performance for machine learning tasks such as
classification, displaying robustness to overfitting. We obtain a tight bound
on the expressive capacity, and present analyses suggesting that correlations
in the measured quantum system enhance learning capacity by reducing noise in
eigentasks. These results are supported by experiments on superconducting
quantum processors. Our findings have broad implications for quantum machine
learning and sensing applications.Comment: 7 + 21 pages, 4 + 12 figures, 1 tabl
Deep Neural Network Discrimination of Multiplexed Superconducting Qubit States
Demonstrating a quantum computational advantage will require high-fidelity
control and readout of multi-qubit systems. As system size increases,
multiplexed qubit readout becomes a practical necessity to limit the growth of
resource overhead. Many contemporary qubit-state discriminators presume
single-qubit operating conditions or require considerable computational effort,
limiting their potential extensibility. Here, we present multi-qubit readout
using neural networks as state discriminators. We compare our approach to
contemporary methods employed on a quantum device with five superconducting
qubits and frequency-multiplexed readout. We find that fully-connected
feedforward neural networks increase the qubit-state-assignment fidelity for
our system. Relative to contemporary discriminators, the assignment error rate
is reduced by up to 25% due to the compensation of system-dependent
nonidealities such as readout crosstalk which is reduced by up to one order of
magnitude. Our work demonstrates a potentially extensible building block for
high-fidelity readout relevant to both near-term devices and future
fault-tolerant systems.Comment: 18 Pages, 9 figure
Cryogenic Memory Architecture Integrating Spin Hall Effect based Magnetic Memory and Superconductive Cryotron Devices
One of the most challenging obstacles to realizing exascale computing is
minimizing the energy consumption of L2 cache, main memory, and interconnects
to that memory. For promising cryogenic computing schemes utilizing Josephson
junction superconducting logic, this obstacle is exacerbated by the cryogenic
system requirements that expose the technology's lack of high-density,
high-speed and power-efficient memory. Here we demonstrate an array of
cryogenic memory cells consisting of a non-volatile three-terminal magnetic
tunnel junction element driven by the spin Hall effect, combined with a
superconducting heater-cryotron bit-select element. The write energy of these
memory elements is roughly 8 pJ with a bit-select element, designed to achieve
a minimum overhead power consumption of about 30%. Individual magnetic memory
cells measured at 4 K show reliable switching with write error rates below
, and a 4x4 array can be fully addressed with bit select error rates
of . This demonstration is a first step towards a full cryogenic
memory architecture targeting energy and performance specifications appropriate
for applications in superconducting high performance and quantum computing
control systems, which require significant memory resources operating at 4 K.Comment: 10 pages, 6 figures, submitte
Efficient quantum microwave-to-optical conversion using electro-optic nanophotonic coupled resonators
Cryogenic Memory Architecture Integrating Spin Hall Effect based Magnetic Memory and Superconductive Cryotron Devices
© 2020, The Author(s). One of the most challenging obstacles to realizing exascale computing is minimizing the energy consumption of L2 cache, main memory, and interconnects to that memory. For promising cryogenic computing schemes utilizing Josephson junction superconducting logic, this obstacle is exacerbated by the cryogenic system requirements that expose the technology’s lack of high-density, high-speed and power-efficient memory. Here we demonstrate an array of cryogenic memory cells consisting of a non-volatile three-terminal magnetic tunnel junction element driven by the spin Hall effect, combined with a superconducting heater-cryotron bit-select element. The write energy of these memory elements is roughly 8 pJ with a bit-select element, designed to achieve a minimum overhead power consumption of about 30%. Individual magnetic memory cells measured at 4 K show reliable switching with write error rates below 10−6, and a 4 × 4 array can be fully addressed with bit select error rates of 10−6. This demonstration is a first step towards a full cryogenic memory architecture targeting energy and performance specifications appropriate for applications in superconducting high performance and quantum computing control systems, which require significant memory resources operating at 4 K