# Open Hardware in Quantum Technology

Nathan Shammah  Email<EMAIL_ADDRESS>Unitary Fund  Anurag Saha Roy
Qruise GmbH, 66113, Saarbruecken, Germany  Carmen G. Almudever  Technical
University of Valencia, Spain  Sébastien Bourdeauducq  M-Labs Limited, North
Point, Hong Kong  Anastasiia Butko  Applied Mathematics and Computational
Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,
USA  Gustavo Cancelo  Fermi National Accelerator Laboratory, Batavia, IL
60510, USA  Susan M. Clark  Sandia National Laboratories, Albuquerque, New
Mexico 87123, USA  Johannes Heinsoo  IQM Quantum Computers, Espoo 02150,
Finland  Loïc Henriet  PASQAL, 7 rue Leonard de Vinci, 91300 Massy  Gang Huang
Accelerator Technology and Applied Physics Division, Lawrence Berkeley
National Laboratory, Berkeley, CA 94720, USA  Christophe Jurczak
Quantonation, 75010 Paris, France  Janne Kotilahti  IQM Quantum Computers,
Espoo 02150, Finland  Alessandro Landra  IQM Quantum Computers, Espoo 02150,
Finland  Ryan LaRose  Center for Quantum Computing, Science, and Engineering,
Michigan State University [East Lansing, MI 48824, USA  Andrea Mari  Unitary
Fund  School of Science and Technology, Università di Camerino, 62032
Camerino, Italy  Kasra Nowrouzi  Applied Mathematics and Computational
Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,
USA  Caspar Ockeloen-Korppi  IQM Quantum Computers, Espoo 02150, Finland  Guen
Prawiroatmodjo  Microsoft Quantum, Redmond, Washington 98052, USA  Irfan
Siddiqi  Quantum Nanoelectronics Laboratory, Department of Physics, University
of California at Berkeley, Berkeley, CA 94720, USA  Applied Mathematics and
Computational Research Division, Lawrence Berkeley National Laboratory,
Berkeley, CA 94720, USA  William J. Zeng  Unitary Fund  Quantonation, 75010
Paris, France

###### Abstract

Quantum technologies such as communications, computing, and sensing offer vast
opportunities for advanced research and development. While an open-source
ethos currently exists within some quantum technologies, especially in quantum
computer programming, we argue that there are additional advantages in
developing open quantum hardware (OQH). Open quantum hardware encompasses
open-source software for the control of quantum devices in labs, blueprints
and open-source toolkits for chip design and other hardware components, as
well as openly-accessible testbeds and facilities that allow cloud-access to a
wider scientific community. We provide an overview of current projects in the
OQH ecosystem, identify gaps, and make recommendations on how to close them
today. More open quantum hardware would accelerate technology transfer to and
growth of the quantum industry and increase accessibility in science.

##  I Introduction

The free and open exchange of scientific tools has become more and more
important as automation and devices increase their role into scientific
fields. The past five years have witnessed an explosion of open-source tools
for programming quantum computers. The open nature of these software tools has
substantially increased the number of users of quantum computers, and created
a new genre of programmer: the “quantum software engineer” [1, 2, 3, 4, 5, 6].
This has shaped the development of quantum computing as a whole, leading to
the creation of new organizations, job categories, and career paths.

Less attention has been paid to the tools and components actually used to
build and control quantum computers – and quantum technologies such as
communications and sensing more broadly – as well as efforts making quantum
computers more accessible in non-commercial ways. We use the term “open
quantum hardware” (OQH) to cover them, and intend for it to explicitly
encompass several steps related to the openness associated to hardware in
quantum technology, including: (1) open-source software (OSS) for designing
quantum processors and other hardware components (used for computation, but
also for simulation, sensing, and communication), (2) foundries and facilities
for fabricating quantum processors, (3) software for controlling, analyzing
and characterizing quantum devices, and (4) software and hardware for making
the infrastructure that enables various levels of open-access – from cloud-
accessible quantum processors to collaborative testbeds providing non-
commercial access and testing, from remotely accessible research labs to the
related cloud infrastructure. These steps covering the life cycle of OQH and
their overlapping relations are sketched in Fig. 1.

Figure 1: Overarching diagram of the Open Quantum Hardware steps and their
interconnection. 1: Design phase, which can involve a loop between simulation
of the Hamiltonian and electromagnetic (EM) simulations used to define the QPU
design and layout. 2: Fabrication step, e.g., through a foundry. 3:
Installation and bring-up, which includes testing and characterization;
information collected at this step can be fed back to step 1 to modify
designs. 4: Sustained operation, with overlaps (with step 3), and is composed
of data acquisition (with control and calibration) and can involve interfacing
with infrastructure to provide cloud access to the device or experiment.

In this article, we provide an overview of efforts in each of these categories
of open quantum hardware (OQH), highlight notable projects in each category,
identify gaps, and provide recommendations for closing them. A major theme in
this review is that there is much opportunity to promote interoperability,
reduce cost, and increase the number of users of quantum technologies.

Enabling the open quantum hardware ecosystem is both the natural complement to
the open quantum software ecosystem (which is itself quite robust and
sophisticated [2]), as well as the natural extension of efforts to open up
classical hardware. This ecosystem also represents a prime opportunity for
entities building hardware (of all kinds) to engage in the kind of beneficial,
pre-competitive activity which boosts the ecosystem as a whole (and
consequently, those builders themselves).

A first generation of projects in open quantum hardware, such as ARTIQ and
pyEPR [7, 8], has pioneered a free and open dissemination of tools. We are now
witnessing the beginning of a more robust and sophisticated OQH ecosystem [9,
10] which can enable and accelerate scientific progress and discovery on a
wider scale. On the one hand, this ecosystem can further extend beyond quantum
computing, on which it is mostly focused, to encompass quantum technologies
such as quantum sensing and communications. On the other hand, it can further
integrate and benefit from the adoption and integration of existing non-
quantum open hardware projects and frameworks [11]. What’s more, given the
substantial investments made around the world [12] – especially in Australia
[13], Eurasia [14, 15, 16, 17, 18, 19], and North America [20, 21, 22, 23]– to
support the development of a quantum technologies industry, ensuring those
investments yield the best possible fruit is of great importance. One way to
do so is through encouraging the development of open communities and
ecosystems [24, 25, 26, 27, 28, 29, 30, 31, 32, 33] around quantum technology.

To describe the potential of OQH, we can look at the open quantum software
ecosystem, which has flourished within the past five years, thanks to seeds
planted over ten years ago. As a result, researchers (theorists and
experimentalists alike) as well as quantum software engineers worldwide can
use open-source software to advance quantum technology and quantum science in
many directions, generally building upon an existing stack. An illustrative
case is that of the Quantum Toolbox in Python (QuTiP) [34, 35], first released
in 2012, which enables the exploration of the effects of noise on a variety of
quantum systems interacting with the environment. Building on top of QuTiP,
several other tools have emerged, focusing on specific niches such as
nontrivial system-environment dynamics [36, 37], notably getting closer and
closer to the simulation of QPUs and their diagnostics [38, 39].

When looking at similar efforts in the classical open hardware space, the
astounding success of the Arduino project [40] (microcontrollers) as well as
the Raspberry Pi [41] (single-board computers) speaks to the power of putting
open hardware in the hands of end-users. We can reasonably expect similar
benefits within the quantum hardware ecosystem. Compared to only open-source
software, an open hardware ecosystem also provides legibility, transparency,
and reproducibility of hardware devices, a crucial consideration for supply
chains and their management.

Finally, the pre-competitive activity which can take place by opening up
quantum hardware extends these benefits by mobilizing institutional momentum
from key players who can help the ecosystem adopt a broad ethos of openness,
interchangeability, interoperability, and benchmarking, as fostered by bodies
like the Quantum Economic Development Consortium (QED-C) in the USA and
similar entities in other geographies [42, 43, 44]. In doing so, this allows
for additional adoption and use of quantum hardware in academic labs and
testbeds (a point returned to in Sec. II.4.2).

This article is organized as follows: Sec. II gives a high-level overview of
the state of the art in OQH, divided among blueprints and software for
hardware design (Sec. II.1), and software for control and data acquisition
(Sec. II.2), itself divided into data acquisition (§ II.2.1), pulse-level
control on hardware (§ II.2.2), pulse-level simulation (§ II.2.3), and optimal
control, calibration and characterization (§ II.2.4). In Sec. II.3 we provide
an outlook on the current status and specific need for OQH for quantum error
correction. In Sec. II.4 we review facilities for OQH, such as remotely
accessible labs (Sec. II.4.1), Testbeds (§ II.4.2) and Foundries (§ II.4.3).
Throughout Sec. II’s subsections, we focus on some example projects to bring
definiteness to the discussion. In Section III we discuss gaps in OQH and make
recommendations to close them. Finally, in Section IV, we give our
conclusions.

##  II Open quantum hardware today

Open quantum hardware encompasses open-source software that is used for
designing, analyzing, building, controlling, and programming quantum chips,
foundries which build quantum chips, and cloud-accessible labs and testbeds
that provide alternative, non-commercially-driven access to them. In this
section, we review the state of open quantum hardware along each of these
categories. It should be noted that within each of these categories, there can
exist multiple stacks – configurations of components which are all inter-
operable with one another, but not necessarily with components used in a
different stack. Ensuring inter-operability of components across stacks would
help ensure a more robust and sophisticated ecosystem. In Table 1 we summarize
the different categories, providing examples of existing open hardware
projects. The particular choices of components is again meant to be
representative.

Functionality |  Examples
---|---
Processor Design |  DASQA [45, 46], KQCircuits [47], PainterQubits/Devices.jl, pyEPR [8], Qiskit Metal [48], QuCAT [49]
Simulation and diagnostics |  KQCircuits [47], Pulser [50], Qiskit Metal [48], QuTiP [34, 35], QuTiP-QIP [39], sc-qubits [38]
Control and data acquisition |  ARTIQ [7], Duke-ARTIQ [51], Qua111partially open-source [52], QCoDeS [53], QICK [54], Quantify [55], QubiC [56], qupulse [57], Sinara Open Hardware [58, 59]
Remotely Accessible Labs222excluding commercial providers |  Forschungszentrum Jülich through OpenSuperQ [60], Quantum Inspire [61]
Testing (testbeds) |  Lawrence Berkeley National Lab’s AQT, Sandia National Labs’ QSCOUT [62], Sherbrooke’s Distriq DevTeQ
Fabrication (foundries) |  LPS Qubit Collaboratory, UCSB quantum foundry, QuantWare333private company with support for Qiskit Metal
Table 1: OQH examples grouped by functionalities. The different
functionalities in open quantum hardware and examples of representative
projects: Processor design, §II.1; simulation and diagnostics, §II.2; and
control and data acquisition, §II.2.4. We also list programs related to
facilites in OQH: Remotely accessible labs (where here we exclude here the
many commercial providers and cloud services, that are discussed in §II.4.1);
testing (testbeds), §II.4.2; and fabrication (foundries), §II.4.3.

###  II.1 Blueprints and software for hardware design

Blueprints for hardware design are classic examples of “open hardware” [63,
64, 65, 66, 67, 68, 69, 70, 71, 46]. In modern device design, software tools
have been developed to tackle various aspects of this task, known as computer-
aided design (CAD). In quantum computing research, while quantum device design
is often published in peer-reviewed papers and micrographs are included in the
supporting materials and figures, generally, CAD drawings are not shared in
the open. Notable exceptions include pyEPR [8], Qiskit Metal [48], and
KQCircuits [47], three projects enabling various capabilities for chip design
of superconducting-circuit (SC) qubits.

Superconducting circuits incorporating non-linear devices, such as Josephson
junctions and nanowires, are among the leading platforms for emerging quantum
technologies. Using pyEPR, one can design and optimize SC circuits and control
dissipative Hamiltonain parameters in a systematic, simple and robust
approach. This reduces the number of required ab-initio simulations. pyEPR has
been used on a variety of circuit quantum electrodynamics (cQED) devices and
architectures, from 2D to 3D, including “2.5D" (flip-chip), demonstrating 1%
to 10% agreement for non-linear coupling and modal Hamiltonian parameters over
five-orders of magnitude and across a dozen samples.

Finite Element Methods (FEM) simulations, as those shown in Figure 2, can be
obtained with PyEPR using the energy participation ratio approach (EPR). EPR
unifies the design of dissipation and Hamiltonians around a single concept —
the energy participation, a number between zero and one — in a single-step
electromagnetic simulation. After the FEM simulations have validated the qubit
properties (and possibly its coupling to a cavity), one is ready to fabricate
the SC QPU. To this end, a photomask is generated to place the SC qubits on
the substrate of a processor. You can find a typical CAD drawing of a
superconducting flip chip QPU and an example mask generated by KQCircuits [47]
in Figure 3 and more information about KQCircuits in the frame below (_Example
1_).

Figure 2: Finite Element Methods (FEM) chip simulations Design and FEM
simulations of a cavity (left) and qubit (right) modes, using PyEPR [8]. The
contour plot of cavity modes displays the intensity of the electric field at
9.0 GHz. The Current-density magnitude of a transmon qubit, showing transmon
pads connected by a Josephson junction [adapted from Ref. [8]].

Figure 3: Photomask layout and chip design. An open-source photomask (left)
provided in KQCircuits as a demo example. An individual SC quantum processor
layout (right) based on flip chip technology, where blue is one substrate, red
is the other substrate and in green denotes the bump bonds. The chip contains
four transmons capacitively coupled and two buses for multiplexed readout. The
code for the mask can be accessed here here and for the chip here.

_Example 1. HARDWARE DESIGN._
KQCircuits: KLayout Python library for integrated quantum circuit design.
KQCircuits [47] is an open-source Python library created by IQM, a full-stack
quantum computing startup, for designing superconducting quantum circuits. It
automates the layout and simulation part of the design process, outputting
chip layouts and photomask files in OASIS or GDSII formats, standard formats
for the specification of data structures of photomasks, and integrated circuit
layout. The layout files are sent to a mask manufacturer to produce a physical
mask which is then used for the fabrication process. As a part of the mask
export process, KQCircuits also produces other files such as netlists, to help
with design verification. While KQCircuits itself cannot be used to perform
simulations, it can be used to export automated simulation scripts and files
for popular electromagnetic field simulation toolkits, such as Ansys HFSS/Q3D
[72], pyEPR [8] (Energy Participation Ratio simulation), Sonnet [73], and
Elmer [74]. These simulations use the parameterized geometry as the physical
mask layouts produced by KQCircuits. A comparison between KQCircuits and
Qiskit Metal is provided in Table 2. KQCircuits generates multi-layer
2-dimensional-geometry representing common structures in quantum processing
units (QPU). An important part of KQCircuits are the definitions of
parameterized geometrical objects, or “elements”, and a framework for easily
defining new ones. Generating the designs using code makes it easy to quickly
create different variations of designs and helps to avoid costly human errors.
The combination of many elements into a full QPU design is made easier by
features such as named reference points used for automatic positioning of
elements relative to each other and Graphical User Interface (GUI) editing.
Furthermore, KQCircuits includes a library of premade chips, many of which
have been manufactured and used for testing at IQM. KQCircuits works on top of
KLayout [11], an open-source layout design software that is mainly used for
classical electronics. Advantageously, existing KLayout functionalities can be
used for quantum hardware other than classical, while KQCircuits only needs to
add features specific to QPU design. This connection with KLayout can also
help to bring together the wider electronics open-source hardware community
and open quantum hardware community.

In terms of remaining challenges for chip design tools, we note that
computational electromagnetic simulation of quantum circuits is still heavily
reliant on the use of proprietary and expensive tools such as Ansys HFSS or
Sonnet or CST Studio (full-wave and capacitance simulations in particular).
Open-source tools such openEMS or MEEP or Scuff-EM which are used elsewhere in
the field of microwave simulations find very little adoption and application
in quantum device design. Notable open-source exceptions are Elmer [74] and
Palace [75]. Elmer is an open-source parallel multi-physics Finite Element
Methods (FEM) software used for quantum device simulation on desktop and High
Performance Computing (HPC), e.g., 3D layouts capacitance matrices, cross
section of layouts, London equations, which in the framework of OpenSuperQPlus
(European Open-Access Quantum Computer Project [60]) is developed with the
partnership between CSC and IQM. Elmer along with Gmsh [76] has also been
integrated as a backend for mesh generation and FEM simulation in Qiskit
Metal. Palace (Parallel, Large-scale Computational Electromagnetics) [75], is
an open-source parallel FEM software capable of full-wave electromagnetics
simulations developed at AWS, with out-of-the-box support for use of large
scale cloud HPC resources.

Software |  Purpose |  Simulations |  Circuit analysis |  Input/output (I/O)
---|---|---|---|---
Qiskit Metal |  Full-stack quantum processor design. |  Ansys HFSS/Q3D, Ansys, GDS, etc. by plugins. Work in progress on open-source EM renders. |  EPR, impedance, quasi-lumped LOM, lumped. |  Python based. Connects to Ansys, GDS, etc. by plugins. Work in progress on open-source EM renders.
KQCircuits |  Design parametrization and automation for quantum processors. Extensive components, chips and masks library. |  Ansys HFSS/Q3D, Sonnet, Elmer (open-source) supported. |  EPR, cross sectional EM properties, London equations and netlist export. |  Import/export GDSII and OASIS format. Netlist export. Automated simulation scripts and files export.
Table 2: Open hardware design of SC qubit processors. Qiskit Metal, supported
by IBM, and KQCircuits, developed at IQM, are two software packages for open
hardware design for SC qubit processors. We highlight their purpose,
integration with FEM simulation software, circuit analysis characteristics and
input and output file formats. EPR: Energy Participation Ratio, GDS: Graphic
Design System, HFSS: High-Frequency Structure Simulator, LOM: Lumped
Oscillator Model.

###  II.2 Control and data acquisition

In this section, we discuss the features and components involved in the
execution and readout of quantum experiments. This field is not easily
defined, as the operation of a QPU or quantum technology experiment is
informed already at the highest level with the definition of abstract
instructions, e.g., for quantum programming, gates in the form of a quantum
circuit in a high-level software development kit (SDK). To narrow the field,
we focus here on control that gets “closer to the metal", e.g., lower than
gate-level quantum circuit compilation in quantum computing. This field is
already populated by a number of projects, spanning from pulse-level
simulation of devices for diagnostics, characterization and calibration, to
pulse-level control and data acquisition during experiments runs [77, 78, 79,
39, 34, 80, 81, 82, 7, 54, 38, 83, 53, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93]. We highlight that core to these tasks is the exchange of data through
application programming interfaces (APIs).

####  II.2.1 Data acquisition

Quantum hardware for control and readout of quantum systems includes a user
interface to operate the instrument, for instance, through a touch-based front
panel or remotely accessible GUI. However, to implement more complex tasks
such as characterization, tuning, calibration and general operation of QPU
elements that involve coordination and timing between several different pieces
of programmable hardware, it is essential to add a software layer that runs on
a control computer and communicates with hardware directly through a
programming interface. This software layer orchestrates the flow of control
and data acquisition commands and is responsible for the overall operation of
the QPU via the quantum hardware.

On the software side, there exist quite a few alternatives for QPU control and
data acquisition, starting with QCoDeS [53], which is a Python framework
developed by Microsoft Quantum. Another example of Control and Data
acquisition software is ARTIQ [7], which is also used to control hardware, as
discussed in Sec. II.2.4. Control and data acquisition software encompasses
various elements and instruments: the quantum device, the firmware that
interconnects with the instrument drivers, parameters that are controlled and
measured to generate datasets, which are stored, analyzed and explored via
data visualization tools, as depicted in Fig. 4.

To achieve frictionless control of a QPU, the software typically abstracts
away the hardware elements using drivers. For example in the QCoDeS library
[53], an instrument driver is a Python class that implements methods to
operate the hardware using commands that are sent to the device via the
programming interface. Namely, a quantum hardware instrument, such as an
arbitrary waveform generator, can be used to generate a radio frequency pulse
for a rotation of a qubit, e.g., an X gate. In order to program this pulse,
the driver implements parameters such as frequency, amplitude, phase and pulse
time. The driver will then use these values to construct the right programming
commands to send to the instrument to set up the pulse. The QCoDeS
documentation [53] contains driver examples to control over 50 instruments.

Figure 4: Control and data acquisition. The diagram shows some of the most
important elements in the software and hardware stack involved in a quantum
experiment. The hardware part of the stack includes the device under test or
operation, the measurement instrument and the firmware. A driver provides
interaction between the software and hardware. Drivers implement parameters
that can be controlled (set) or read (get) over several measurements. The
result of each measurement is stored into a dataset, that is read for data
analysis, visualization and storage.

The data acquisition sofware uses instrument drivers to form an abstraction
layer to the several pieces of hardware that control a QPU. Parameters can
also be used in higher levels of abstraction on top of instrument parameters
to represent QPU device elements that are controlled and read via the hardware
layer, such as a qubit readout circuit. The parameters are then used in
automated measurement routines with the goal to characterize, calibrate or
program a QPU or its different elements. This requires saving, analyzing and
visualizing the data that is recorded by these instruments in a dataset. Most
data acquisition software frameworks therefore also include data storage
abstractions and data visualization tools.

In order to control the QPU elements using quantum programming instructions,
the control signals need to be calibrated for optimal gate and read-out
fidelity. As shown in Fig. 1, calibration values are obtained via device
testing and calibration measurements using the data acquisition software,
analysis routines and data storage in the Cloud. More details can be found in
section II.2.4 on optimal control, calibration and characterization for high-
fidelity quantum operations.

A way to approach the software-hardware interface is through an Instruction
Set Architecture (ISA) [94] as it is done in classical computing. The ISA acts
as a contract between hardware and software by explicitly describing the set
of available operations, machine instruction format, supported data types,
registers, and memory addressing. A well-designed ISA can present a very
compact and efficient method to access the specialized features of a computing
device. Graphical Processor Units (GPUs) [95] and their success in becoming an
integral part of many modern devices is an example of instruction set design
and standardization leading to widespread specialized architecture adoption.

There are a few quantum ISA implementations [96, 97, 98], with some that
explore this control and data acquisition approach, such as QUASAR [99]
developed at Advanced Quantum Testbed (AQT), Lawrence Berkeley National
Laboratory (LBNL). QUASAR is based on the RISC-V ISA [100] \- an open-source
architecture that revolutionized the classical computing field and continues
expanding toward emerging technology applications. QUASAR has been
demonstrated in conjunction with QubiC [83] (QubiC is discussed separately in
section II.2.2, below), executing experiments, including mid-circuit
measurement and feed-forward, on superconducting quantum processors (QPU) at
AQT, LBNL.

_Example 2. CONTROL AND DATA ACQUISITION_
QUASAR: A Quantum Instruction Set Architecture
The QUASAR development started in 2017 and went through several iterations. It
is a project defining a quantum Instruction Set Architecture (ISA) to
interface software with hardware. The first version was a tightly-coupled
extension that required significant changes to the processor micro-
architecture. Later, the implementation moved towards a more decoupled
approach. The current implementation is a RISC-V Rocket Core [101] extended
with the QUASAR co-processor (note that RISC-V is a very popular open standard
ISA in classical open hardware. RISC-V is based on the established reduced
instruction set computer (RISC) principles, and the Rocket Core is a processor
that implements RISC-V.) In contrast to classical instruction sets, quantum
ISAs operate on different types of computations. Traditional general-purpose
operations are supplemented with a set of basic quantum gates applied on
direct-addressed qubits and/or qubit registers. The RISC-V core executes the
main program; when a quantum instruction occurs at the fetch pipeline stage,
it forwards it via the RoCC interface [102] to be decoded and executed by the
QUASAR co-processor. Such an architectural solution allows the main core to
continue performing computations while the quantum backend is generating the
control pulses. Moreover, the RISC-V core can perform complex computations,
such as floating-point arithmetic for phase estimation, and make conditional
branching during algorithm execution based on the qubit measurement data. That
makes the QUASAR implementation flexible enough to accommodate complex hybrid
experiments for classical-quantum computations. The RISC-V Rocket core runs a
Linux kernel that facilitates remote communication with the user host machine
and provides additional functionalities for the quantum software stack.

####  II.2.2 Pulse-level control with hardware integration

In recent years, several pulse-level control software packages have been
developed with the idea of providing end-users with a tool to program all the
relevant device-specific physical parameters of the system [7, 54, 77, 79, 39,
103]. This approach allows for a finer level of control over pulses during the
application of gates, and also makes it possible to directly use the
Hamiltonian of the system as a resource for computation. Within the gate-level
framework of quantum circuits, pulse-level control and simulation enables a
greater level of flexibility and the ability to implement optimal control
schemes. Within the analog quantum simulation framework, pulse-level control
notably allows practitioners to take advantage of the mimetic capabilities of
the hardware. Exposing the hardware controls at such a low level is intended
to help quantum developers design software procedures while having the
specific characteristics of the hardware in mind [104].

Control hardware, e.g., field programmable gate arrays (FPGAs), arbitrary
waveform generators (AWGs), sequencers, have historically been closed-source,
either provided by industrial manufacturers or by in-house developed systems.
The ARTIQ/Sinara project is a notable exception, focused on the fast control
of ion-based quantum processors through FPGAs, as detailed in the box below
(_Example 3_).

_Example 3. CONTROL AND DATA ACQUISITION._
ARTIQ: Advanced Real-Time Infrastructure for Quantum physics.
The ARTIQ experiment control and data acquisition system was initiated in 2013
at the NIST Ion Storage Group, in partnership with M-Labs Ltd, to address the
deficiencies observed with control systems developed in-house by physicists or
based on existing commercial solutions. A key feature of the ARTIQ system is a
high-level Python-based programming language that helps describe complex
experiments, which is compiled and executed on dedicated FPGA hardware with
nanosecond timing resolution and sub-microsecond latency. Using the
abstractions provided by Python, ARTIQ has the capability to handle the entire
control stack from quantum circuits to pulse-level programming. ARTIQ also
supports connecting several FPGAs together and synchronizing their clocks in
the sub-nanosecond regime, which greatly expands the input/output (I/O)
scalability of the system. Initial versions of the ARTIQ system ran on
physicist-designed hardware based on FPGA development kits with custom I/O
expansion boards. In order to improve the quality, availability and
reproducibility of the hardware, the Sinara project [59] was started in
collaboration with Warsaw University of Technology. The Sinara project
developed a modular system with carrier FPGA cards (Kasli, Kasli-SoC)
controlling various so-called Eurocard Extension Modules (EEMs) catering to
the needs of each experiment – such as digital I/O, Analog to Digital
Converter (ADC), Digital to Analog Converter (DAC), Direct Digital Synthesis
(DDS), Phase-Locked Loop (PLL) synthesizer, AWG. There is ongoing work by Duke
University on firmware [51] and by Warsaw University of Technology on hardware
[58] to port ARTIQ to the RF-SoC platform, with similar capabilities to QICK.
Combined with the ARTIQ software, the Sinara hardware has encountered
substantial success, with almost a thousand quantum physics experiments
relying on ARTIQ/Sinara systems. ARTIQ, including its gateware, the firmware,
and the ARTIQ tools and libraries are licensed as LGPLv3+. The Sinara hardware
designs are licensed under CERN OHL. This ensures that a user of ARTIQ or
Sinara hardware designs obtains broad rights to use, redistribute, study, and
modify them.

More recently, other FPGA-based projects have emerged beyond ARTIQ, for SC
qubits control, such as QubiC [56] and QICK [54], two projects based on
software, firmware and hardware composed of radio-frequency system-on-chip
(RF-SoC) boards. Open-source FPGA-based control efforts for SC qubits started
in 2018 with QubiC, developed at the Advanced Quantum Testbed (AQT) based at
LBNL (more information on this facility is given in Sec. II.4.2, discussing
Testbeds). The first version of QubiC was implemented on the XilinX VC707
platform [105, 83] informed by actual quantum information science experiments
at AQT. To reduce cost, complexity, and space requirements, customized
hardware components were designed and fabricated, such as in-phase and
quadrature (I/Q) mixing modules integrating filters, amplifiers, and bias_Ts
on printed circuit boards (PCBs) with Electromagnetic Interference (EMI)
shielding, the design of which was published and open-sourced to benefit the
community [106]. Additionally, automated single- and two-qubit gate
calibration protocols were developed using QubiC [56].

To take advantage of the growing capabilities of recent RF-SoC platforms,
QubiC was then ported to the XilinX ZCU216 platform [107], capable of direct
generation of control pulses at higher frequencies (up to 10GHz, in the second
Nyquist zone). Additionally, a novel Distributed Processor design was created
and implemented to enable distributed decision-making and branching, such as
mid-circuit measurement and feed-forward [108]. The different iterations of
QubiC implementations are shown in Figure 5. To form a prototype full-stack
quantum control system with increased potential for future experiments, QubiC
and QUASAR (described above in II.2.1) have been integrated and applied to
quantum computing experiments at AQT, LBNL [109].

Figure 5: QubiC, through the years: QubiC 1.0, implemented initially on XilinX
VC707, in its chassis with auxiliary components (left), the Analog Front End
chassis for QubiC 1.0 (middle), and QubiC 2.0 implemented on XilinX ZCU216
with a custom SMA fanout board. A customized Surface Mount Assembly (SMA) fan-
out board has been designed and recently fabricated to fully utilize all the
channels of the ZCU216 board. Separately, a low-cost DAC extension was
developed for the VC707 platform, to meet the varying frequency needs of
different superconducting QPU architectures. The QubiC team has recently
demonstrated heterogeneous synchronization of two sets of VC707 boards with
low-cost DACs and two ZCU216 platforms [107].

Boards from the QICK project have reached over 40 labs in the USA and abroad
after a little more than two years. Using QICK helps reduce equipment costs
and increases the performance of quantum information science experiments with
SC qubits [110]. All of QICK implementations have been on Zynq boards, with
the firmware provided by PYNQ (Python for Zynq), compatible with multiple
generations of RF-SoC FPGAs. An image of a QICK board is shown in Figure 6.

Figure 6: The Quantum Instrumentation Control Kit (QICK). The QICK consists of
two pieces of hardware: a commercial RFSoC evaluation board (left), which
connects to the QICK RF&DC custom board (right) which can be used for
additional signal amplification and filtering. QICK supports the AMD-Xilinx
ZCU111 [111] (shown in the picture with its companion custom board) and the
AMD-Xilinx ZCU216 [112] (not shown) with a new companion board under
fabrication and testing.

The usage of QICK has already expanded from SC qubits to atomic, molecular,
and optical physics qubits and spin qubits (Nitrogen-Vacancy-center qubits),
on the hardware side. On the application side, QICK is used not only for
quantum computing but also for quantum sensing experiments, e.g., for dark
matter candidates detection [113], as well as for RF-SoC control in particle
physics detection beyond quantum technology.

####  II.2.3 Pulse-level simulation

Simulation is important to design an experiment before running it on hardware
and then to validate and interpret the results after data collection. Pulse-
level simulation can be employed for quantum optics experiments, quantum
simulation, and both for gate-level (digital) quantum computing and analog
quantum computing. In quantum computing, although gate-level instructions are
the most common formalism on the user-side to write quantum algorithms, there
can be a compiler that transforms discrete operations into pulses. Examples of
pulse-level simulators include Pulser [79] and Bloqade.jl, two toolboxes for
neutral-atom QPUs respectively developed in Python and Julia. qutip-qip is a
simulation toolkit that provides the freedom to design any QPU and simulate
pulse level execution, integrating it QuTiP’s noisy time evolution [39]. We
summarize information on Pulser in _Example 4_.

_Example 4. PULSE LEVEL SIMULATION._ Pulser: Library for pulse-level/analog
control of neutral atom devices.
The Pulser framework is an open-source software library for designing pulse
sequences for neutral-atom QPUs [50]. In such devices, individual atoms are
placed in arrays of microscopic traps [114, 115, 116] and their quantum state
is manipulated through the application of laser or microwave pulses. Using
Pulser, developers can control all the relevant physical parameters of the
qubit register and the driving channels. The central object in Pulser is
called a sequence, which is linked to a device. The device contains
information about the hardware constraints, such as the maximal number of
qubits available, the minimal pairwise distance between qubits which can be
reached or the number of lasers and the maximal amount of laser power
available for each of them. These constraints are enforced upon the register,
where the neutral-atom array is defined, and upon the pulses that are added to
the sequence. Each pulse is defined by its phase, amplitude and detuning
waveforms, and pulses are sequentially added to the channels that are
available on the device. The resulting program can subsequently be sent to
QPUs and executed on the hardware after a device-specific calibration-aware
compilation step. In addition to providing an interface to hardware, Pulser
includes a built-in emulator relying on QuTiP [34] that faithfully reproduces
the hardware behavior. To this extent Pulser acts also as a simulator for QPU
design.

####  II.2.4 Optimal control, calibration and characterization

High fidelity operations require high fidelity optimized controls. The process
of generating optimal pulses traditionally involves a model-based open-loop
optimal control step (in simulation) such as GRAPE, Krotov, GOAT algorithms
[117, 118, 119] followed by a model-free closed-loop calibration step (in
hardware). The former relies on building a sophisticated model of the system
and the optimality of the generated pulses is constrained by the model’s
capability to faithfully reproduce the imperfections of open quantum systems.
The latter usually involves the use of a gradient-free optimization algorithm,
e.g., Nelder-Mead or CMA-ES to optimize the parameters of an open-loop optimal
pulse on the quantum hardware benchmarked with a fidelity measure such as
ORBIT [120]. Open-source libraries such as JuliaQuantumControl, QuOCS and
C3-Toolset [80, 81] aim to provide a broad range of the optimal control
functionalities previously discussed, with easy to use interfaces in Python or
Julia. These tools also incorporate automatic differentiation (AD) techniques
which have made it possible to obtain gradients of arbitrary order for free,
even with the most complex numerical simulations. Such gradients are essential
for the optimization of pulse parameters in open-loop control. AD capabilities
are provided by the use of standard Machine Learning frameworks such as
TensorFlow or JAX [121, 122].

In order to improve the fidelity of the pulse obtained through open-loop
control, both the model parameters and the model itself need refinement. One
approach for refining model parameters is the technique of data-driven system
identification also known as characterisation. This is achieved through
learning model parameters from data collected during experiments performed on
the hardware, e.g., the calibration step previously outlined. Refining the
model translates to building a complex digital twin that models not only the
quantum dynamics but also all of accessory classical electronics and their
non-ideal behavior. These two techniques — Model Learning and Quantum-
Classical Digital Twin — are tightly integrated as a unified solution in the
C3-Toolset package [80], as discussed below (_Example 5_).

_Example 5. OPTIMAL CONTROL, CALIBRATION AND CHARACTERIZATION._
C3: An integrated tool-set for Control, Calibration and Characterization.
The C3-Toolset package provides an API for directly interfacing with hardware
to use the pulses generated by optimal control and further optimize them using
closed-loop calibration algorithms. Users have access to a high-level Qiskit
interface that allows them to run gate or pulse level quantum circuits on the
full-physics noisy high-fidelity differentiable simulator. In recent years, a
variety of machine learning for quantum control techniques have been closely
integrated in the quantum device bring up process. Besides the previously
mentioned process of learning model parameters, reinforcement learning (RL)
has been particularly useful in a variety of applications. The open-source
library rbqoc [123] details a technique for noise-robust control using RL
while C3-Toolset uses RL for both learned optimizers and Bayesian experiment
design. Calibration is generally needed for most platforms. Depending on the
architecture, this involves different control signals. A difference between
academic research labs and industry is the tendency to automate and
standardize calibrations tasks. Tests are run periodically to validate the
status of the system before running an experiment and during an experiment. On
a schematic level, these can be seen as continuous integration (CI) tests,
involving hardware control but similar to open-source testing for software
projects. If the job does not pass a test, automatic re-calibration is
prompted.

###  II.3 Designs for quantum error correction

Many applications of quantum computing, networking, and sensing technologies
will require a significant reduction in error rates. In the long term,
developing quantum hardware with built in error correction can address this
need through fault-tolerant quantum computing. In this section, we review some
of the open-source software available today for studying and simulating
quantum error correcting codes. This software is becoming increasingly
important as quantum error correction moves from theory into practice.

Quantum error correcting codes are often based on stabilizer subsets of
quantum computation and so can benefit from specialized simulators. A leading
open-source package for stabilizer simulation today is Stim [124] which
improves on existing stabilizer simulators available in Qiskit, Cirq, or other
packages.

Other libraries sit on top of simulators like stim and allow users to explore
quantum error correcting code designs. These packages help inform longer term
planning for quantum processor architecture. Examples include plaquette [125]
and stac [126]. Both of these are Python libraries that have built in examples
of quantum error correcting codes and allow you to generate logical circuits
to study performance. There are also tools that act as compilers, translating
logical circuits into physical circuits that can be run directly on capable
hardware, e.g. the suite of tools developed at latticesurgery.com for
compiling to the surface code.

Finally, there are open implementations of the decoding algorithms used to
decide how to best correct error detected by different quantum codes.
PyMatching [127, 128] is an example open-source decoder. This software can
help inform the design of processors specifically adapted to run quantum error
correction. For example, stim was used to simulate novel superconducting qubit
designs to support surface code parity measurements in [129].

There remain many important opportunities to improve quantum error correction.
For example, fast classical control of QPUs is essential for feedback loops in
quantum error correction implementation. Tailored tools could reduce the
existing gap between hardware operation and software instructions. More
generally, increasing the ecosystem of open-source software available in this
field will both support new breakthrough ideas as well as accelerate the
transition from theory to practice in building fault-tolerant quantum
computers.

###  II.4 Open quantum hardware facilities

In these section we review the state of the art with respect to facilities
that can enable a robust OQH ecosystem. We find three qualitatively different
categories: open-access to QPUs and remotely accessible research labs,
collaborative testbeds, and facilities for fabrication such as foundries.

####  II.4.1 Remotely accessible labs and cloud-connected labs

The idea of open remote labs, i.e., laboratories that can be remotely accessed
by users to perform real experiments is not new in the context of scientific
research and education [130, 131]. For example, the educational tool HYPATIA
[132] can be used to perform particle-physics experiments with real data
produced by the ATLAS experiment at CERN. In a similar way, quantum computers
can be used as quantum remote labs, i.e., advanced laboratories in which
quantum experiments can be performed for purely scientific purposes without
any computational motivation [133, 134, 135, 62, 136, 137, 138, 139]. For
example in Ref. [140] a quantum computer was used for testing quantum
fluctuation theorems, while in Ref. [141] a quantum computer was used to
prepare a many-body quantum system in a time-crystalline phase.

Superconducting-circuit quantum computers originally became available online
from IBM Quantum with the IBM Quantum Experience, Rigetti Computing with the
Rigetti Quantum Cloud Services, and other providers. These have had an impact
on the way research is done in quantum computing and quantum optics as well as
enabling access for students for outreach activities, due to the fact that
experiments can be done much more easily (even by theorists), from the cloud.
We are now in a second phase with more providers putting their devices online,
encompassing more technologies – ion-based (IonQ, Quantinuum), atom-based
(e.g., Infleqtion, Pasqal, QuEra), photonics-based (e.g., Quandela, Xanadu),
quantum-dot based, etc.

Turning the attention to institutional frameworks, the European OpenSuperQ
project (and the follow on OpenSuperQPlus project) [60] is similarly aimed at
developing public-owned full-stack open-access superconducting systems.
Multiple remotely accessible QPUs are online or currently being set up as part
of this project at the Delft University of Technology, the Wallenberg Centre
for Quantum Technology (Chalmers), the Walther-Meissner-Institut (Munich), and
at the Forschungszentrum Juelich (from phase 1). A hybrid form of cloud access
is also emerging, in which cloud providers give access to different quantum
processor providers. Further sustaining open access to QPU providers can be
important for scientific discovery.

Over the years, the level of control over device properties in compilation,
optimization, qubit mapping, etc., has increased, spilling notably into remote
pulse level control, both for digital (e.g., Qiskit pulse [77]) and analog
quantum computing (e.g., Pasqal’s Pulse studio [115]). The deeper the access,
the more advanced the control researchers have over hardware and the greater
the opportunity for integration of open-hardware features with cloud-access.
An example of novel interaction between cloud providers and OQH is given by
the Amazon AWS application developed for QICK, called SideQICK, which is being
integrated into a more general cloud queue for quantum devices [142].

We also witness examples of a hybrid model of industry-public interaction
enabling open access. Quantum computers are being shipped to research centers
and further integrated with HPC centers, which also act as simulator
infrastructure (such as the EU HPCQS). This provides new avenues for industry-
academic collaborations and purpose-specific hardware customization, use cases
and access. It will be important to foster academic research labs putting
their quantum devices online, building upon the open-source tools described in
the previous sections. This could further change the research landscape,
enabling more researchers to test research ideas on more devices, novel
architectures and platforms, and in turn facilitate technology transfer.

####  II.4.2 Deeply collaboratively testbeds

While cloud-based platforms allow scientists and the public alike to submit
their circuits and receive the results, deeper, customized access to the full
stack to enable more involved experiments and R&D efforts is generally not
possible on these platforms. To provide such access to users in Academia,
Industry, and National Labs, the US Department of Energy has funded two
testbed programs: QSCOUT (Quantum Scientific Computing Open User Testbed),
based on trapped ions and located at Sandia National Laboratories, and AQT
(Advanced Quantum Testbed), based on superconducting qubits and located at
Lawrence Berkeley National Laboratory. A similar network called Qu-Test [143]
managed by TNO at Delft, Netherlands has recently been kicked off with the
goal of providing a federated network of testbeds by bringing together 13
service providers and 11 industrial users from the European quantum community.
These platforms allow low-level access to the full stack of quantum hardware
(including programming languages [144, 145], gate-pulse shaping [103], noise
injection, unique qubit/qudit states, specialized calibrations, comparison of
compilation techniques, etc.) allowing users to probe how the machines
actually work and how to make them work better. The testbeds foster deep
collaborations between the host laboratory and the users.

Collaborations on these testbeds have so far resulted in demonstrations of
scientific applications of near-term quantum computers, and the development of
many tools to benchmark quantum computers as well as to characterize and
mitigate errors on them QPUs. This could further change the research
landscape, enabling more researchers to test research ideas on more devices.
Moreover, the existence of testbeds would be crucial to enable reproducible
benchmarks, for quantum hardware and for application-driven tasks. Testbeds
are key to facilitate startup creation in the quantum space, as they lower the
barrier for testing prototypes with expensive equipment (such as dilution
fridges).

####  II.4.3 Fabrication and foundries

As described in Section II.1, open-source tools exist to facilitate the design
and creation of quantum processors _in silico_. However, translating those
designs into actual hardware can be difficult and expensive. Current
arrangements for producing hardware on the basis of designs fall into four
categories: partnerships, the construction of research-grade fab facilities,
the use of academic foundries, and “quantum-fab-as-a-service".

Several companies in recent years have begun partnerships with major
manufacturers to procure supply lines for their hardware designs. For example,
photonic startups PsiQuantum and Xanadu have both signed agreements with
semiconductor manufacturers GlobalFoundries [146, 147]. Trapped-ion startup
IonQ has sourced some of its traps from Sandia National Laboratories [62].
These sorts of partnerships are typically only available to startups or other
corporations, and allow for the use of use of existing manufacturing
techniques and scalable processes. However, whether rapid prototype of
hardware can be achieved is unclear.

An alternative approach is to stand up one’s own fabrication facility. Startup
Rigetti Computing is notable in this space for their “Fab-1" facility [148],
which allows the company to prototyping new hardware. However, such a facility
can be expensive to construct, and requires access to large amounts of
capital. In the United States, in recognition of the need for fabrication
facilities, various foundries are being stood up around the country, including
the UCSB quantum foundry [149], MonArk quantum foundry [150], and the LPS
Qubit Collaboratory [151]. The European Commission has established a somewhat
similar program called Qu-Pilot [143] under the umbrella of the Quantum
Flagship consisting of 21 partners from 9 different countries, with the goal
of developing and providing access to federated European production facilities
linking existing infrastructure. The overall coordination of the project is
managed by VTT, Finland.

Finally, some startups have leveraged this need toward the development of new
businesses. For example, startup Quantware in the Netherlands [152] helps
designing, developing, and fabricating hardware for customers. In Canada, the
NSERC CREATE programs have partnered with CMC Microsystems for novel
superconducting circuits workshops at the Stewart Blusson Quantum Matter
Institute. In the future, more industry players, industry consortia, startup
incubators, and academic partnerships could enable even more facilities for
processor fabrication, inspired by existing electronics industry frameworks,
such as the Efabless [153] and the Google Silicon project [154].

##  III Discussion: Current Gaps & Future Recommendations

In Sec. II we reviewed OQH today, with an overview of the various OQH
categories and deep dives into selected projects. From this overview it is
possible to draw an overarching picture and outline some major topics across
the OQH ecosystem. In the sections below we list these topics, identifying
gaps and making recommendations to close them.

OQH growth and maintenance across technologies and architectures. Currently,
most of the OQH projects focus on tools for quantum computers. There are
considerable opportunities for expanding OQH projects and tools to accelerate
scientific discovery and tech transfer in quantum communication, quantum
metrology, and quantum sensing. Moreover, given the early stage of the field,
for specific functionalities, tooling supporting only a given set of
architectures may be currently available. For example, the open hardware tools
for chip design are mostly revolving around SC qubits (and partially ion
traps). There is room for tools for other QPU architectures, such as atom-
based, spin-based, and photonics-based QPUs. With respect to control, open
hardware projects including firmware and hardware, such as FPGAs, have been
developed mostly for ion traps and SC qubits and can more broadly applied to
other architectures. Moreover, while it is possible to find multiple open-
source FPGA (and general open-hardware electronics projects with many being
applicable to physics), one would be hard-pressed to find anything that comes
closest to an optical frequency comb in photonics tooling.

APIs and standards for instruction sets. We note that interoperability is a
major challenge in the OQH ecosystem. From APIs in the software stack to
software-hardware integration via ISA, there is work to be done. Currently,
there is considerable duplication on higher software stack and one needs to do
conversions of quantum programs in order to run them on different QPUs.
Frameworks such as the Quantum Intermediate Representation (QIR) project and
OpenQASM (Open Quantum Assembly Language) [155, 156] can help at the higher
level. Broader support for common frameworks getting further down at the
hardware control level would be desired, such as QUASAR and QUA.

Benchmarks. Open-source benchmarking suites can have strong impact on the
scientific community, providing information on the state of the art of current
hardware architectures. So far we note that quantum technologies have been
confronted with a lack of standardized benchmarks. Benchmarks are useful for
evaluating the performance of quantum devices and algorithms, as well as
assessing the relative performance compared to purely-classical solutions in
relevant applications or non-conventional computers [157]. Thankfully, efforts
in the standardization of benchmarks, such as those provided by the QED-C, a
U.S.-based industry consortium [42, 43, 44] have been occurring. Pipelines for
crowd-sourced benchmark results is also needed: https://metriq.info/ is one
attempt, inspired by projects such as Papers With Code and MLCommons in
machine learning. Pipelines including OSS and OQH can simplify the
accomplishment of standardized benchmarks.

Open access to hardware. Currently, there exist several cloud-accessible QPUs
and cloud service frameworks. Providers have made QPUs accessible in a variety
of ways, including direct and free access, as well as through partnerships
within institutional frameworks (e.g.,ORNL’s Quantum Computing User Program
(QCUP) for national labs and federally-funded consortia). However, the
research community would benefit from further access enabled by grants and
other awards. Ensuring such frameworks and access points increase as soon as
possible is necessary for creating a more equitable research landscape.
Moreover, while several research labs have infrastructure to enable remote
access and data acquisition of experiments, this is generally for internal
use. Sometimes, access is extended to partnering organizations or
collaborating researchers. However, there is a general lack of research labs
who have put their device online for cloud access to a wider community of
users and researchers. This is due to the overhead in infrastructure involved
in making such devices operational and maintained. OQH can help with this
regard, by providing researchers with existing tools that can be plugged in to
bring up experiments from research labs.

Reproducibility. In order to facilitate reproducibility of results, journals
and academic guidelines can further encourage (and mandate where appropriate)
the sharing of open software along with research publications. Funding
agencies, universities and other research stakeholders can facilitate these
efforts by recognizing software artifacts as important, citable outcomes for
measuring research impact.

OQH policy and intellectual property. Implementing policy that is open-
hardware aware can favor the growth of a OQH ecosystem. For example, learning
from existing open hardware projects beyond quantum technology can help avoid
known pitfalls. A practical example is the adoption of an integrated toolchain
fostering collaboration between academic labs, software developers, and
industry. An example at the facility level is the investment into the
establishment of shared facilities, such as quantum foundries, which can
provide a flywheel effect for startups. Policy makers and quantum experts have
the opportunity to work together, in order to ensure that informed decisions
are made about the application of export control and intellectual property
protection laws to quantum hardware. On the one hand, this could ensure that
policy makers provide clear guidance about how efforts to open up quantum
hardware will be treated from the perspective of these laws. On the other
hand, researchers can develop expertise on the correct use of licenses for
open-source software and open hardware. This guidance can include implementing
processes through which one can more easily assess what hardware-based
technology should be open-sourced, and what should not, making strategic calls
at a narrow level (researcher, company) and wider level (common good for
society). Researchers need to balance against intellectual property (IP) &
strategic competitive considerations for certain components and technologies
that could provide commercial exploitation. At the same time, they can
consider when OSS can be used in business models, helping grow a community of
users and creators around products.

Support of OQH in education and academia. We believe it is important to foster
OSS and OQH adoption in education and research. One actionable item is to
encourage schools and universities to include in course material and labs the
usage of open-source software [158] and open hardware [159, 160, 161].
Programs that prepare the future quantum workforce are fundamental to broaden
the acquisition of talents [24, 5, 6]. This includes developing communities
across domain knowledge.

A bottom-up approach to strengthening OQH is by encouraging academic labs to
pull together their systems with existing open-source tools, so they can
create do-it-yourself small-scale testbeds. If open-source tools are not
available, researchers can consider developing and open-sourcing new tools or
using less-closed tools (e.g., proprietary but with a layer of open APIs).
Fostering the creation of new open-hardware projects includes engaging in
activities, programs and material that facilitates the open-sourcing of
existing projects, such as Ref. [162], which provides information about
starting a scientific OSS project. Starting an OQH project includes general
challenges found by other OSS projects (testing, documentation, distribution,
maintenance, community relations) and some specific ones, such as how to
distribute physical devices to users, or work with experimental labs on
ensuring device control.

An example of a top-down approach to supporting open-source projects (and open
hardware) is provided by funding agencies programs acknowledging the impact of
OSS projects in science. One notable example is the NSF POSE program. Further
tailored funding in programs focused in OQH could strengthen the ecosystem
addressing some of its characteristics.

Community building: projects support and governance. Similarly to other OSS
projects, also OQH projects need to interact with their community of users,
engage feedback, create guidelines to foster code contributions and meetings.
They can benefit from non-profit organization support for their governance, as
typically scientific OSS projects start in a research lab or group and then
grow in scope and need for representation of more parties and partners. There
is a need to simplify the bureaucracy in compliance at institutional at
government level to contribute to open-source projects and open hardware,
e.g., by equipping national labs with default policies that would allow
researchers to contribute to open-source projects with permissive licenses
approved by the Open Source Initiative (OSI). Often the originating
organization such a national lab has restrictions that prevent an open-source
project from growing independently. This highlights the need for alternatively
supporting organizations to incubate and house open-source projects. Examples
of non-profit organizations that do this in the classical space are NumFOCUS,
the Linux Foundation, and the Open Source Hardware Association (OSHA). Unitary
Fund is a 501(c)(3) non-profit organization that offers this support
specifically to quantum technology projects.

##  IV Conclusions

Quantum computers – and quantum devices in general – provide a fascinating
prospect to bridge a platform for fundamental science, technology transfer in
engineering, and novelties in cloud computing and actuators. An open hardware
approach in quantum technology will preserve, and possibly expand, the pre-
competitive space necessary to make ideas flourish and enable accessible
benchmarks and evaluations that have already characterized quantum software.

As we have illustrated, quantum technology provides unique challenges and
features with regards to the usability and feasibility of open-hardware stack:
from process design toolkit to foundries, from simulation software to control
and data acquisition, from cloud infrastructure to testbeds. All these layers
can benefit from collaborative innovation. Learning from lessons learned in
the early days of quantum software, and from other open hardware verticals in
conventional computing and tooling, as well as from a snapshot of current gaps
in the quantum ecosystem, we hope that the quantum industry can efficiently
implement innovation, build lively communities, and expand its workforce.

##  V Acknowledgments

This work stemmed from a workshop [10] hosted at IEEE QCE 2021. The authors
thank Gary Steele and Zlatko Minev for fruitful discussions. NS thanks the
organizers of IEEE QCE 2021 for the opportunity to start the discussion of OQH
which culminated in this manuscript, and many of the co-authors for their
contributions at that workshop. This material was funded in part by the U.S.
Department of Energy, Office of Science, Office of Advanced Scientific
Computing Research Quantum Testbed Program. Sandia National Laboratories is a
multimission laboratory managed and operated by National Technology &
Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell
International Inc., for the U.S. Department of Energy’s National Nuclear
Security Administration under contract DE-NA0003525. This paper describes
objective technical results and analysis. Any subjective views or opinions
that might be expressed in the paper do not necessarily represent the views of
the U.S. Department of Energy or the United States Government.
SAND2023-09347O.

This work was supported by the U.S. Department of Energy, Office of Science,
Office of Advanced Scientific Computing Research, Accelerated Research in
Quantum Computing under Award Number DE-SC0020266. AM acknowledges support
from the PNRR MUR project PE0000023-NQSTI.

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