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Blog
The latest news from Google AI
Announcing an Open Source ADC board for BeagleBone
Wednesday, July 20, 2016
Posted by Jason Holt, Software Engineer
(Cross-posted on the
Google Open Source Blog
)
Working with electronics, we often find ourselves soldering up a half baked electronic circuit to detect some sort of signal. For example, last year we wanted to measure the strength of a
carrier
. We started with traditional analog circuits —
amplifier
,
filter
,
envelope detector
,
threshold
. You can see some of our prototypes in the image below; they get pretty messy.
While there's a certain satisfaction in taming a signal using the physical properties of capacitors, coils of wire and transistors, it's usually easier to digitize the signal with an
Analog to Digital Converter
(ADC) and manage it with
Digital Signal Processing
(DSP) instead of electronic parts. Tweaking software doesn't require a soldering iron, and lets us modify signals in ways that would require impossible analog circuits.
There are several standard solutions for digitizing a signal: connect a laptop to an oscilloscope or
Data Acquisition System
(DAQ) via USB or Ethernet, or use the onboard ADCs of a maker board like an
Arduino
. The former are sensitive and accurate, but also big and power hungry. The latter are cheap and tiny, but slower and have enough RAM for only milliseconds worth of high speed sample data.
That led us to investigate single board computers like the
BeagleBone
and
Raspberry Pi
, which are small and cheap like an Arduino, but have specs like a smartphone. And crucially, the BeagleBone's
system-on-a-chip
(SoC) combines a beefy ARMv7 CPU with two smaller Programmable Realtime Units (PRUs) that have access to all 512MB of system RAM. This lets us dedicate the PRUs to the time-sensitive and repetitive task of reading each sample out of an external ADC, while the main CPU lets us use the data with the GNU/Linux tools we're used to.
The result is an open source
BeagleBone cape
we've named
PRUDAQ
. It's built around the Analog Devices AD9201 ADC, which samples two inputs simultaneously at up to 20 megasamples per second, per channel. Simultaneous sampling and high sample rates make it useful for
software-defined radio
(SDR) and scientific applications where a built-in ADC isn't quite up to the task.
Our open source electrical design and sample code are available on
GitHub
, and
GroupGets
has boards ready to ship for $79. We also were fortunate to have help from Google intern Kumar Abhishek. He added support for PRUDAQ to his
Google Summer of Code
project
BeagleLogic
that performs much better than our sample code.
We started
PRUDAQ
for our own needs, but quickly realized that others might also find it useful. We're excited to get your feedback through the
email list
. Tell us what can be done with inexpensive fast ADCs paired with inexpensive fast CPUs!
Towards an exact (quantum) description of chemistry
Monday, July 18, 2016
Posted by Ryan Babbush, Quantum Software Engineer
“
...nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical...
” -
Richard Feynman
,
Simulating Physics with Computers
One of the most promising applications of quantum computing is the ability to efficiently model quantum systems in nature that are considered intractable for classical computers. Now, in collaboration with the
Aspuru-Guzik group at Harvard
and researchers from Lawrence Berkeley National Labs, UC Santa Barbara, Tufts University and University College London, we have performed the first completely scalable quantum simulation of a molecule. Our experimental results are detailed in the paper
Scalable Quantum Simulation of Molecular Energies
, which recently appeared in
Physical Review X
.
The goal of our experiment was to use quantum hardware to efficiently solve the
molecular electronic structure problem
, which seeks the solution for the lowest energy configuration of electrons in the presence of a given nuclear configuration. In order to predict chemical reaction rates (which govern the mechanism of chemical reactions), one must make these calculations to extremely high precision. The ability to predict such rates could revolutionize the design of solar cells, industrial catalysts, batteries, flexible electronics, medicines, materials and more. The primary difficulty is that molecular systems form
highly entangled quantum superposition states
which require exponentially many classical computing resources in order to represent to sufficiently high precision. For example, exactly computing the energies of methane (CH
4
) takes about one second, but the same calculation takes about ten minutes for ethane (C
2
H
6
) and about ten days for propane (C
3
H
8
).
In our experiment, we focus on an approach known as the
variational quantum eigensolver
(VQE), which can be understood as a quantum analog of a neural network. Whereas a classical neural network is a parameterized mapping that one trains in order to model classical data, VQE is a parameterized mapping (e.g. a quantum circuit) that one trains in order to model quantum data (e.g. a molecular wavefunction). The training objective for VQE is the molecular energy function, which is always minimized by the true ground state. The quantum advantage of VQE is that quantum bits can efficiently represent the molecular wavefunction whereas exponentially many classical bits would be required.
Using VQE, we quantum computed the energy landscape of molecular hydrogen, H
2
. We compared the performance of VQE to another quantum algorithm for chemistry, the
phase estimation algorithm
(PEA). Experimentally computed energies, as a function of the H - H bond length, are shown below alongside the exact curve. We were able to obtain such high performance with VQE because the neural-network-like training loop helped to establish experimentally optimal circuit parameters for representing the wavefunction in the presence of systematic control errors. One can understand this by considering a hardware implementation of a neural network with a faulty weight, e.g. the weight is only represented half as strong as it should be. Because the weights of the neural network are established via a closed-loop training procedure which can compensate for such systematic errors, the hardware neural network is robust against such imperfections. Likewise, despite systematic errors in our implementation of the VQE circuit, we are still able to learn an accurate model for the wavefunction. This robustness inspires hope that VQE may be able to solve classically intractable problems without
quantum error correction
.
While the energies of molecular hydrogen can be computed classically (albeit inefficiently), as one scales up quantum hardware it becomes possible to simulate even larger chemical systems, including classically intractable ones. For instance, with only about a hundred reliable quantum bits one could model the process by which
bacteria produce fertilizer
at room temperature. Elucidating this mechanism is a famous open problem in chemistry because the way
humans produce fertilizer
is extremely inefficient and consumes 1-2% of the world's energy annually. Such calculations could also assist with breakthroughs in fundamental science, for instance, in the understanding of
high temperature superconductivity
.
Though many theoretical and experimental challenges lay ahead, a quantum enabled paradigm shift from qualitative / descriptive chemistry simulations to quantitative / predictive chemistry simulations could modernize the field so dramatically that the examples imaginable today are just the tip of the iceberg.
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