Sample Use Cases in Quantum Machine Learning, Optimization, and Simulation
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Sample Use Cases in Quantum Machine Learning, Optimization, and Simulation © Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only. For public disclosure of this material, consult Zapata Computing.
Zapata Computing
built Orquestra
The complexity of the problems our customers face often Zapata Computing
calls for the innovative orchestration of classical and quantum built Orquestra™
computing resources, which is precisely what Orquestra
— the first and only
makes possible. Built on interoperable, extensible and modular
classical-to-quantum software and hardware frameworks,
end-to-end, unified
Orquestra enables teams to compose, run and analyze the Quantum Operating
most complex quantum-enabled workflows™ at scale. Environment — to
meet the complex
Orquestra can be applied to numerous use cases, accelerating computational needs
the discovery of quantum solutions to problems in optimization, of our customers and
machine learning and simulation across industries, including ourselves.
finance, chemistry, materials discovery, life sciences
and pharmaceuticals, automotive, telecommunications,
transportation and logistics.
While the sample use cases discussed here pertain specifically
to finance, materials, pharmaceuticals, and logistics, thanks
to Zapata’s robust set of proprietary algorithms and workflow
libraries, Orquestra adapts to any other computationally
challenging problem your team faces.
2
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only. 2
For public disclosure of this material, consult Zapata Computing.FINANCE
Accelerated sampling for more efficient exotic
derivative pricing, valuation adjustments, risk analysis/
stress testing (for CCAR and Dodd-Frank compliance)
APPLICATION WORKFLOW DEEP DIVE
Financial modeling and analysis Algorithms: Starting with a customer’s proprietary pricing model for
the underlying assets, teams can use Zapata’s proprietary algorithms
SOLUTION for derivative pricing. These algorithms yield far shallower circuits than
conventional approaches such as quantum amplitude estimation.
Improved efficiency of models for asset value fluctuation
Compose
through quantum techniques that accelerate sampling Software: The customer’s proprietary pricing model can be
from probability distributions. implemented, for example, using the QuantLib C++ API. For certain
applications that require fitting of model parameters, one can employ
USE CASE CONTEXT SQL queries against the customer’s database through pandas and
• Since the 2008 Financial Crisis, large banks and financial pyodbc. Proprietary derivative pricing algorithm implementation can
institutions have been subject to regulatory frameworks be provided by Zapata.
requiring both Comprehensive Capital Analysis and Review
Back-end devices: The fitting of asset pricing model parameters
(CCAR) as well as Dodd-Frank Act stress testing (DFAST).
and the classical part of the derivative pricing algorithm can be
Compliance with some of these regulations requires running
deployed via an Orquestra installation on classical computing
Conduct
billions of Monte Carlo simulations on a broad range of
resources (e.g., AWS cloud or on premises). The quantum subroutines
scenarios. Simulating these scenarios across lengthy time
can be sent to an application-specific superconducting device, which
horizons (e.g., nine quarters) presents financial institutions
solves the problem in a more resource-efficient way than generic
with significant computational and resource challenges.
quantum devices.
• Compliance requirements aside, a broad set of financial
Data management: Analysts can use Orquestra’s command-line
instruments demands more complex modeling for accurate
Record
tools to download CSV files containing relevant workflow results.
pricing and valuation.
These files can then be analyzed in Excel, or other tools such as
Jupyter Notebook or Tableau.
BENEFITS
More efficient credit risk modeling, accelerated analytics
in compliance with industry regulations.
3
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.MATERIALS
Materials discovery for Li-ion batteries
through more accurate modeling
APPLICATION
Advanced machine learning
SOLUTION
Acceleration of high-throughput screening of electrolyte additives for high-voltage Li-ion batteries,
with the goal of improving the energy density of batteries.
USE CASE CONTEXT
• The energy density of Li-ion batteries plays a significant role in the • Because of the complexity of electrolyte degradation mechanisms
cost and range of electric vehicles. One approach to increasing the and the limited amount of training data available, achieving accurate
energy density is to operate batteries at higher voltages. However, predictions with conventional machine learning approaches may
current battery materials undergo irreversible reactions when prove challenging. Therefore, it is often desired to capture more
operated at high voltages resulting in poor cycle life. New electrolyte correlation in the dataset in order to improve prediction accuracy.
formulations could potentially allow Li-ion batteries to operate at Quantum computers have shown to generate correlations that
high voltages without compromising cycle life. are hard to recreate classically (via the Quantum Supremacy
experiment). This suggests that quantum computers may be
• Because the number of possible electrolyte formulations is able to capture more correlations than classical computers.
extremely large, machine learning techniques can greatly accelerate
the materials discovery pipeline. Such an approach might use
a combination of experimental data, atomistic simulations, and
structural descriptors to predict the oxidative stability of an
electrolyte.
BENEFITS
Better prediction of the performance of different battery material candidates, improved oxidative stability of electrolyte formulations,
potential concrete reduction in the marginal cost of producing electric vehicles.
4
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.MATERIALS
Materials discovery
Continued
WORKFLOW DEEP DIVE
Algorithms: A hybrid quantum-classical classifier algorithm Back-end devices: An Orquestra installation on an on-prem
can be used to identify electrolyte additives that are likely high-performance classical computer cluster can be used for the
to improve the oxidative stability of selected electrolyte classical nodes of the hybrid classifier as well as for the parameter
Conduct
formulations. The implementation can use a customer’s initialization calculations. The latter can take advantage of GPUs
proprietary ansatz, tailored for electrolyte additive screening, on the cluster to accelerate tensor network calculations. The
coupled with Zapata’s proprietary parameter initialization quantum nodes can use an ion-trap device, such as Honeywell’s,
Compose
method to avoid being trapped in local minima and barren for quantum circuit execution, taking advantage of unique
plateaus of the parameter optimization landscape. measurement feedback features.
Software: Users can author their proprietary ansatz using Data management: Workflows can be integrated into a customer’s
Qiskit, which can then be deployed in a hybrid quantum- in-house, high-throughput screening platforms, which launches
classical classifier implemented with PennyLane and workflows for training and testing, using classifiers through REST
Record
TensorFlow. The parameter initialization calculations can APIs. Data analysis can be performed in Excel, using an ODBC
be performed using a Zapata proprietary implementation. connection to retrieve results from the classifier. (Note: Microsoft’s
Open Database Connectivity (ODBC) allows applications to access
data in Database Management Systems (DBMS) using SQL as a
standard for accessing the data.)
5
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.PHARMACEUTICALS
Promoting drug discovery by simulating
the quantum mechanical behavior
APPLICATION
Molecular modeling; drug simulation and analysis
SOLUTION
More accurate approach to computing the binding energy of small molecular drugs to
protein targets to increase the accuracy of binding energy simulations.
USE CASE CONTEXT
• Rational drug design relies on identifying the binding properties • Predicting the optimal orientation for molecules is key to producing
of small molecules on biomolecular targets, such as proteins, and stable molecular complexes. When accurately computing the
either activating or inhibiting their function. binding energy of small, molecular drugs on specific protein targets
at the electron level, you are essentially dealing with quantum
• One variant of this approach is structure-based drug design, in which mechanics. Because efficient modeling of these molecular
the structure of the binding site is identified, and chemists then use interactions is critical to the drug discovery process, quantum
models to design drugs that can bind with high affinity and selectivity computing can provide a distinct advantage.
to the target.
BENEFITS
More accurate and efficient modeling of drug interactions at the molecular level by taking advantage of the
relative strengths of quantum and classical computing devices.
6
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.PHARMACEUTICALS
Drug discovery
Continued
WORKFLOW DEEP DIVE
Algorithms: To tackle this problem, teams would use a Back-end devices: MM tasks can be deployed on a
Conduct
Quantum Mechanics/ Molecular Mechanics (QM/MM) customer’s private Azure cloud for the Q-Chem calculations.
approach based on a Janus or an ONIOM model. The binding A superconducting device, e.g., IBM, would be used for quantum
site and small molecule interactions here are treated as QM, circuit execution.
while rest of the system is treated as MM. Initial geometry would
be obtained from a previous docking and conformational study. Data management: Data analysis can be performed in Jupyter
Record
A quantum computing algorithm (e.g., VQE) would be used Notebooks. A python API provided by Orquestra returns pandas
as the QM solver, while a standard computational chemistry DataFrames containing workflow results, which scientists can then
Compose
package deployed on a classical computer would be used as plot using the Python library of their preference (e.g., matplotlib).
the MM solver.
Software: MM calculations can be performed using Q-Chem
while QM calculations can be performed using Zapata’s
proprietary implementation of VQE. The Orquestra workflow
builds the QM Hamiltonian and the problem description of
the MM region. The workflow then executes Zapata’s VQE
implementation and Q-Chem as separate tasks. Results are
combined to obtain final energy.
7
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.LOGISTICS
Improving the gross service capacity of logistic
systems for maximizing distribution and sales
APPLICATION
Logistics and supply chain optimization
SOLUTION
Analysis of complex delivery systems to uncover valuable opportunities for process and operational improvement
USE CASE CONTEXT
• Mathematicians have puzzled over The Traveling Salesman problem • Optimizing logistics systems to maximize overall throughput—
for over 100 years. The problem is simply stated: If a salesman has including routing, scheduling, and predictions—is a notoriously
a set number of targets to visit, what is the most efficient way to do complex undertaking. What makes this problem particularly
it? For companies involved in delivery and distribution, this problem challenging is the fact that, with the addition of each new target
is far from academic. The type of logistics optimization problems delivery location, the number of potential routes increases
that arise in practice is often far more complex variants of Traveling exponentially. If a customer needs to make hundreds or thousands
Salesman. However, improving the solutions to such problems can of deliveries a day, calculating and analyzing all potential routes
mean saving millions in fuel costs and people hours alone, as well exceeds available computing power on classical machines. There
as increasing revenue from improved inventory management and are still tremendous opportunities for improvement using solutions
lasting operational improvements. based on novel computing paradigms such as quantum or quantum-
inspired algorithms.
BENEFITS
Cost savings and revenue growth, driven by reducing the number of miles traveled and improving the service capacity of the
overall logistic system.
8
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.LOGISTICS
Gross service capacity of logistic systems
Continued
WORKFLOW DEEP DIVE
Algorithms: The customer could use an in-house technique for Back-end devices: The discrete optimization problem transformed
mapping their operations on to a Capacitated Vehicle Routing from the original CVRPTW could be deployed on an Orquestra
Conduct
Problem with Time Windows (CVRPTW). A Zapata proprietary installation on the customer’s private Azure cloud, with the CVRPTW
algorithm is then used to efficiently map the CVRPTW problem mapping being backed by the customer’s Azure Hadoop cluster. The
to an Ising Hamiltonian, subject to the connectivity and locality optimization itself could be performed using digital annealers, e.g.,
Compose
constraints of the hardware backend. Hitachi or Fujitsu devices.
Software: The customer develops PySpark code to map Data management: To integrate with the customer’s existing
the current state of their operations system on to a CVRPTW logistics planning system, Orquestra workflows could be launched
problem. The mapping to a different combinatorial optimization through a REST API. Data analysis can be performed in Tableau
Record
problem, if appropriate, is performed by a proprietary code Desktop, which connects directly to Orquestra’s Data Management
provided by Zapata. Service via the MongoDB BI Connector. Operations engineers can
deploy dashboards summarizing daily delivery system performance to
an on-prem Tableau Server instance.
9
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.Orquestra: The Unified
Quantum Operating Environment
Orquestra is the accelerator at the center of the entire ALGORITHMS AND TASKS
quantum computing ecosystem. Through deep partnerships • Built for use cases across machine
with the leading quantum hardware makers, and an even learning, optimization, and simulation
deeper background in the actual science, we have succeeded • Compose workflows from your own
tasks, Zapata’s proprietary tasks, and
in creating a unified space for building and executing quantum open-source libraries
workflows, a space that does not require a new language
or framework.
With Orquestra, you don’t have to recreate the wheel every DEVICE COMPATIBILITY
time you shift from one quantum framework to another. You • Gate model quantum devices, including
can use the software and hardware combinations suited to superconducting qubits, trapped ions,
and photonic devices
your particular use case. • Quantum annealers
• Analog quantum simulators
Orquestra offers a complete set of power tools for developing
• Classical HPC resources
and running quantum and quantum-inspired workflows.
• More coming soon
REQUEST EARLY ACCESS
INFRASTRUCTURE SUPPORT
• Enterprise scale and security
• Azure, AWS and proprietary clouds
• Containerization (via Kubernetes)
10
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only. 10
For public disclosure of this material, consult Zapata Computing.orquestra.io
Zapata Computing, Inc. is the quantum software zapatacomputing.com
company empowering enterprise teams to accelerate
quantum solutions, innovation and transformation.
With its introduction of Orquestra™, the first and only end-to-end, unified
Quantum Operating Environment available today, Zapata is spearheading a new
quantum development paradigm. Built on interoperable, extensible and modular
classical-to-quantum software and hardware frameworks, Orquestra enables teams
to compose, run and analyze complex, quantum-enabled workflows™ and challenging
computational solutions at scale. Orquestra is purpose-built for quantum machine
learning, optimization and simulation problems across industries.
Working in close collaboration across the quantum ecosystem, including partnerships
with Amazon, Google, Honeywell, IBM, Microsoft, Rigetti and others, Zapata is backed
by Prelude Ventures, Comcast Ventures, The Engine, Pillar VC, BASF Venture Capital,
Pitango Ventures, Robert Bosch Venture Capital and Honeywell Ventures.
11
© Zapata Computing, Inc., 2020. Proprietary and Confidential. Internal distribution only.
For public disclosure of this material, consult Zapata Computing.You can also read
