Building the Quantum Stack for the NISQ Era

By Tim Hirzel

August 24, 2020

In October 2019, Google unveiled the first proof of quantum supremacy, demonstrating that a quantum computer could solve certain mathematical problems faster than a classical computer.

In March 2020, Honeywell announced that it would launch the first commercial quantum computer and in June announced the creation of the most powerful quantum computer yet. Coming in fast succession, these milestones indicate how far we’ve come since this technology was first postulated by Richard Feynman back in the 1980s.

In the next three to five years, such milestones will be reached with increasing frequency. Eventually, due to the inherent principles of the technology itself, the performance of quantum hardware will accelerate exponentially.

While future developments will unleash the full power of quantum computing, the reality is we can already harness some of that power today through the innovative orchestration of classical computers and existing quantum hardware.

John Preskill, professor of theoretical physics at the California Institute of Technology, called this existing hardware “noisy intermediate scale quantum.” Preskill called the technology “noisy” because we cannot yet adequately control the qubits – the “bits” used in quantum computing – however physically implemented. In the absence of greater control, the error rates involved when executing an algorithm across quantum gates – the logical circuits operating on a set of qubits – can be persistent and relatively high.

Preskill called the technology “intermediate scale” due to the number of qubits currently available on quantum devices. To achieve sustainable quantum supremacy, researchers estimate that we will need machines running between 208 and 420 qubits, depending on the type of circuit used. To put that in perspective, the most powerful machine unveiled by IBM boasts 53 qubits. Honeywell’s latest machine only has 6. This machine, however, has a stated “quantum volume” (a standard for measuring quantum power introduced by IBM) of 64, twice that of its closest competitor.

The question is: For organizations looking to build quantum computing capabilities in the NISQ era, what does the NISQ stack look like? In the following article, we will describe various aspects of this stack and provide a high-level overview of its implementation.

Hybrid by Necessity

Given the limitations of NISQ technology, the quantum stack will be hybrid by necessity, consisting primarily of classical computing components. These classical elements will handle a range of tasks from data preparation and parameter selection to post-processing and data analysis. The quantum elements of the workflow will be limited to very specific—albeit powerful—acceleration or co-processing roles for particular problems.

For the foreseeable future, quantum devices themselves will tend to be fairly specialized, with different types of devices (superconducting, trapped ion, photonic, and so on) particularly well-suited for different types of problems. The challenges posed by the hybrid nature of the stack require the implementation and management of workflows for the effective orchestration of the various components.

Future Compatible

Quantum technology will only continue to evolve, so the NISQ stack requires built-in flexibility to adapt to future innovation. The algorithms and IP developed today must both maximize the capabilities of NISQ and quantum-inspired devices while remaining open to emerging technologies, devices and approaches.

The quantum tools that industry and academia use today must be architected in a way that anticipates and accounts for this inevitable evolution. Creating high level workflows that can implement quantum algorithms on any hardware type represents one specific way to ensure future compatibility.

Replicable, Modular and Flexible at Scale

Working with quantum computing technology today involves trial and error. It is naturally iterative. Algorithms developed in the NISQ era, even those that theoretically can work on the “universal” quantum computers of the future, are heuristic in nature. As researchers and others refine their algorithms and workflows over time, they need to be able to replicate their current efforts on new technology and to experiment with evolving approaches.

The NISQ stack must support this iterative experimentation. Containerization has emerged as one way to provide flexibility, modularity and scalability, while also allowing plug-and-play options on backend devices (both classical and quantum).

The Importance of Workflow Management

The need to orchestrate both classical and quantum capabilities while accounting for their inherent differences benefit from containerization. The execution and composition of containers can be managed with workflows. This in turn calls for a comprehensive workflow management system to efficiently coordinate tasks and processes across the NISQ stack.

Isomorphic with the stack itself, these workflows must be future compatible (i.e., able to run across emerging hardware configurations). They must also be modular to facilitate experimentation and allow for ongoing optimization. Zapata Computing built Orquestra, a unified quantum operation environment, expressly for managing quantum workflows.

Visualizing the NISQ Stack

When thinking about the NISQ stack, it’s best to separate it into three separate functions.

On the front end are tools needed to create workflows along with the frameworks and libraries required to build quantum circuits (Cirq, Qiskit, PyQuill, etc.). Here you will also find specialized tools focused on the problem you are trying to solve (machine learning, optimization, modeling, chemical and molecular dynamics, and so on).

This part of the NISQ stack will be connected to your local infrastructure (e.g., your laptop for writing tasks and workflows in an editor, as well as managing workflows from your command line) through your workflow lifecycle management tool.

The next layer is where the hardware lives. This layer can include any of the existing quantum implementations – superconducting qubits, photonic qubits, ion traps – as well as quantum annealers. You will also find dedicated classical hardware here along with classically-based quantum circuit simulators.

Access to quantum hardware today is primarily cloud-based. For this reason, you will want to have containerized execution tools that connect to the relevant cloud environment. Your workflows will execute across this layer.

Finally, you need an analytics or data layer to analyze intermediate and final data from the workflows you run. This data will in turn inform iterations and replication of your workflows at scale.

From a workflow perspective, this layer will first and foremost house the data aggregation and correlation services responsible for collecting and organizing all the data created from a workflow run. It will also house your analytics tools, most commonly Jupyter Notebooks running Pandas in Python.

The last component consists of plotting and visualization tools: Matplotlib, Tableau or even Excel.

For the purposes of data management, this layer will also need to connect to a database, be it cloud-based or on prem.

Workflow Management: The Continuous Thread

While one might assume the quantum stack will change dramatically as quantum devices evolve, that is probably not the case. The quantum stack will be a quantum/classical hybrid for the foreseeable future. Existing technologies, from analytics and data visualization tools to high-performance computers, are and will continue to be perfectly suited to handle significant aspects of the quantum computing process.

Precisely because of its hybrid nature, the quantum stack will always require workflow management/orchestration. This layer will provide the necessary level of abstraction so that users can repeat, repurpose, and scale quantum processes while employing different quantum frameworks, languages, or hardware types. Given the central role that workflow management plays in the NISQ stack and beyond, it’s fair to say that it will serve as the fundamental enabler of the coming quantum revolution.

About the Author 

Tim Hirzel has a BA in Computer Science from Harvard University and an MS from MIT’s Media Lab. He brings extensive experience in managing teams working on performing data science, machine learning, quantum chemistry, and device simulation. Since 2005, Tim has been a software engineer and architect in science-based technology startups. Today he is focused on delivering a best in class quantum computing platform for Zapata and its customers.

 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight. Hyperion Research analyst and noted storage expert Mark No Read more…

GigaIO Gets $14.7M in Series B Funding to Expand Its Composable Fabric Technology to Customers

September 16, 2021

Just before the COVID-19 pandemic began in March 2020, GigaIO introduced its Universal Composable Fabric technology, which allows enterprises to bring together any HPC and AI resources and integrate them with networking, Read more…

What’s New in HPC Research: Solar Power, ExaWorks, Optane & More

September 16, 2021

In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

AWS Solution Channel

Supporting Climate Model Simulations to Accelerate Climate Science

The Amazon Sustainability Data Initiative (ASDI), AWS is donating cloud resources, technical support, and access to scalable infrastructure and fast networking providing high performance computing (HPC) solutions to support simulations of near-term climate using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 2 (CESM2) and its Whole Atmosphere Community Climate Model (WACCM). Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Quantum Computer Market Headed to $830M in 2024

September 13, 2021

What is one to make of the quantum computing market? Energized (lots of funding) but still chaotic and advancing in unpredictable ways (e.g. competing qubit tec Read more…

Amazon, NCAR, SilverLining Team for Unprecedented Cloud Climate Simulations

September 10, 2021

Earth’s climate is, to put it mildly, not in a good place. In the wake of a damning report from the Intergovernmental Panel on Climate Change (IPCC), scientis Read more…

After Roadblocks and Renewals, EuroHPC Targets a Bigger, Quantum Future

September 9, 2021

The EuroHPC Joint Undertaking (JU) was formalized in 2018, beginning a new era of European supercomputing that began to bear fruit this year with the launch of several of the first EuroHPC systems. The undertaking, however, has not been without its speed bumps, and the Union faces an uphill... Read more…

How Argonne Is Preparing for Exascale in 2022

September 8, 2021

Additional details came to light on Argonne National Laboratory’s preparation for the 2022 Aurora exascale-class supercomputer, during the HPC User Forum, held virtually this week on account of pandemic. Exascale Computing Project director Doug Kothe reviewed some of the 'early exascale hardware' at Argonne, Oak Ridge and NERSC (Perlmutter), while Ti Leggett, Deputy Project Director & Deputy Director... Read more…

Ahead of ‘Dojo,’ Tesla Reveals Its Massive Precursor Supercomputer

June 22, 2021

In spring 2019, Tesla made cryptic reference to a project called Dojo, a “super-powerful training computer” for video data processing. Then, in summer 2020, Tesla CEO Elon Musk tweeted: “Tesla is developing a [neural network] training computer called Dojo to process truly vast amounts of video data. It’s a beast! … A truly useful exaflop at de facto FP32.” Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

Esperanto, Silicon in Hand, Champions the Efficiency of Its 1,092-Core RISC-V Chip

August 27, 2021

Esperanto Technologies made waves last December when it announced ET-SoC-1, a new RISC-V-based chip aimed at machine learning that packed nearly 1,100 cores onto a package small enough to fit six times over on a single PCIe card. Now, Esperanto is back, silicon in-hand and taking aim... Read more…

Enter Dojo: Tesla Reveals Design for Modular Supercomputer & D1 Chip

August 20, 2021

Two months ago, Tesla revealed a massive GPU cluster that it said was “roughly the number five supercomputer in the world,” and which was just a precursor to Tesla’s real supercomputing moonshot: the long-rumored, little-detailed Dojo system. “We’ve been scaling our neural network training compute dramatically over the last few years,” said Milan Kovac, Tesla’s director of autopilot engineering. Read more…

CentOS Replacement Rocky Linux Is Now in GA and Under Independent Control

June 21, 2021

The Rocky Enterprise Software Foundation (RESF) is announcing the general availability of Rocky Linux, release 8.4, designed as a drop-in replacement for the soon-to-be discontinued CentOS. The GA release is launching six-and-a-half months after Red Hat deprecated its support for the widely popular, free CentOS server operating system. The Rocky Linux development effort... Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

Intel Completes LLVM Adoption; Will End Updates to Classic C/C++ Compilers in Future

August 10, 2021

Intel reported in a blog this week that its adoption of the open source LLVM architecture for Intel’s C/C++ compiler is complete. The transition is part of In Read more…

AMD-Xilinx Deal Gains UK, EU Approvals — China’s Decision Still Pending

July 1, 2021

AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…

Leading Solution Providers

Contributors

Hot Chips: Here Come the DPUs and IPUs from Arm, Nvidia and Intel

August 25, 2021

The emergence of data processing units (DPU) and infrastructure processing units (IPU) as potentially important pieces in cloud and datacenter architectures was Read more…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

HPE Wins $2B GreenLake HPC-as-a-Service Deal with NSA

September 1, 2021

In the heated, oft-contentious, government IT space, HPE has won a massive $2 billion contract to provide HPC and AI services to the United States’ National Security Agency (NSA). Following on the heels of the now-canceled $10 billion JEDI contract (reissued as JWCC) and a $10 billion... Read more…

Quantum Roundup: IBM, Rigetti, Phasecraft, Oxford QC, China, and More

July 13, 2021

IBM yesterday announced a proof for a quantum ML algorithm. A week ago, it unveiled a new topology for its quantum processors. Last Friday, the Technical Univer Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008

July 14, 2021

After more than a decade of planning, the United States’ first exascale computer, Frontier, is set to arrive at Oak Ridge National Laboratory (ORNL) later this year. Crossing this “1,000x” horizon required overcoming four major challenges: power demand, reliability, extreme parallelism and data movement. Read more…

Intel Unveils New Node Names; Sapphire Rapids Is Now an ‘Intel 7’ CPU

July 27, 2021

What's a preeminent chip company to do when its process node technology lags the competition by (roughly) one generation, but outmoded naming conventions make it seem like it's two nodes behind? For Intel, the response was to change how it refers to its nodes with the aim of better reflecting its positioning within the leadership semiconductor manufacturing space. Intel revealed its new node nomenclature, and... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire