Global Quantum computing race is heating up among nations as well as between different organizations. In a major development, the University of Science and Technology of China (USTC) in June 2021 demonstrated what researchers claim is the world’s fastest quantum computing processor, surpassing the previous and unofficial record held by Google’s 53-qubit device since 2019. USTC’s 66-qubit processor performed a complex calculation in 1.2 hours that would have taken today’s supercomputers 8 years to complete.
Goups including IBM, Google, MIT Lincoln Lab are at the threshold of producing scalable general-purpose quantum computers. The leader of Google’s quantum computing lab, has predicted that he can build chips with about 100 reliable qubits in a couple of years. In October 2019, researchers from Google claimed to have achieved a milestone known as quantum supremacy. They had created the first quantum computer that could perform a calculation that is impossible for a standard computer. “Quantum supremacy,” a term coined by John Preskill, a physicist at the California Institute of Technology in Pasadena, to denote a quantum computer that can do something beyond the ken of a classical computer, such as simulate molecular structures in chemistry and materials science, from code-breaking and cyber security to big data analysis and machine learning.
D-Wave Systems Inc., the leader in quantum computing systems, software, and services, today announced in Sep 2020 the general availability of its next-generation quantum computing platform, incorporating new hardware, software, and tools to enable and accelerate the delivery of in-production quantum computing applications. Available today in the Leap™ quantum cloud service, the platform includes the Advantage™ quantum system, with more than 5000 qubits and 15-way qubit connectivity, in addition to an expanded hybrid solver service that can run problems with up to one million variables. The combination of the computing power of Advantage and the scale to address real-world problems with the hybrid solver service in Leap enables businesses to run performant, real-time, hybrid quantum applications for the first time. The technology could usher in a much-anticipated era of quantum computing, which researchers say could help scientists run complex simulations and produce rapid solutions to tricky calculations.
Quantum computing and quantum information processing are next revolutionary technology expected to have immense impact. Quantum computers could accelerate the discovery of new materials, chemicals and drugs. They could dramatically reduce the current high costs and long lead times involved in developing new drugs. Computational platforms provide users access quantum computers to perform quantum computations via cloud. Building a new quantum computer is a highly expensive investment for many companies that is why computational platforms make sense for companies to experiment with quantum compting. The solution is provided by quantum computer companies for developers to test their code on real quantum computers.
However, many hurdles exist before the technology can become a practical alternative for businesses. For example, software developers will need to learn new ways of writing programs for quantum computers. Quantum software plays a critical role in exploiting the full potential of quantum computing systems.
Quantum computers are based on the principles of quantum mechanics, such as superposition and entanglement, and they seek to boost computational power exponentially. Many problems that have until now been impossible to solve, in practical terms, might very well be able to be addressed by means of quantum computing. To make such applications become reality, quantum algorithms must be specially coded for these extremely different computers.
Since the quantum computing works with a different mechanism than classical computing, the software approach for quantum computing is also different. There are 2 types of quantum software: quantum software for optimization problems and classical computing software enabling quantum computing.
2 types of quantum software are:
- Software running quantum algorithms: Quantum software development kits and computational platforms provide solutions for end-users. These help end users develop and test their quantum algorithms. A forthcoming quantum leap in information technology depends essentially on our improved understanding of quantum algorithms and complexity.
- Software enabling quantum computers to perform: Quantum computers have performance issues due to random errors and error-correcting software is built to correct such errors. An error-correcting software or firmware is a low-level program that increases the stability of quantum computers.
Although some well-known quantum algorithms already exist, the need for quantum software will increase dramatically in the next years. In that context, quantum software has to be produced in a more industrial and controlled way, i.e., aspects such as quality, delivery, project management, or evolution of quantum software must be addressed. We are sure that quantum computing will be the main driver for a new software engineering golden age during the present decade of the 2020s.
For in-depth understanding on Quantum Software Development and applications please visit: Quantum Software Development: Tools and Techniques
Quantum Error-Correcting Software and Firmware
As with classical computing, quantum computing requires low-level programming and error-correcting algorithms. Q-CTRL is one of the companies working on Quantum firmware. Quantum computers by making error-correcting to make more efficient qubits. Quantum Benchmark is a company working on quantum error-correcting software and firmware. Quantum Benchmark provides software solutions that enable error characterization, error mitigation, and error correction and performance validation for quantum computing hardware.
Sydney quantum start-up Q-CTRL launches world-first product with quantum control software
Sydney start-up Q-CTRL has launched its inaugural product – Black Opal – which it describes as “the world’s first commercially available software suite designed to improve the performance of quantum computing hardware”. Quantum systems are highly susceptible to decoherence. The states of quantum bits, or qubits, in quantum computers are quickly randomised by interference from the environment. Q-CTRL’s toolkit help teams design and deploy control for their quantum hardware in order to suppress these errors.
“Quantum control has for a long time been thought of as a bit of black art. Those of us who were deeply ingrained in it understood the capabilities it brings, but many others would only dip their toes in the water and that was enough,” explains Q-CTRL founder University of Sydney Professor Michael Biercuk. “We’re aiming to remove those barriers, remove the friction points that have prevented teams from taking advantage of everything that’s possible,” he adds. The controls in the toolkit were described by Biercuk as being able to “effectively turn back the clock” on decoherence. “So all the randomisation that occurs, unwinds; it’s like unmixing the soup,” he told Computerworld last year.
While many of the major players in quantum computing will have quantum control teams working towards similar outcomes, Black Opal provides the same in a SaaS offering. The SaaS model means Q-CTRL will add new features on a “roughly weekly basis” Biercuk says. In the coming months the team will also launch Boulder Opal, focused on automation and integration of control solutions into professional workflows. Enterprise versions and API access to developers are also planned.
“Black Opal helps our team directly leverage Q-CTRL’s deep expertise in quantum control to solve some of our toughest problems building a new class of application-specific quantum computers,” said early Q-CTRL customer Dr Alexei Marchenkov, founder and CEO of Bleximo, a quantum technology company based in California. “This software – and its focus on high-quality visualisations – enables us to build intuition for very complicated concepts outside of our core areas of expertise,” Marchenkov added.
QUANTUM INSTRUCTION SETS
These are used to turn complex algorithms into physical instructions that can be performed on quantum processors and vary depending on the qubit modality of the quantum architecture (superconducting/silicon-based/trapped ions etc) of the hardware platform.
Examples of this type include cQASM, Quil, OpenQASM, and Blackbird.
QUANTUM SOFTWARE DEVELOPMENT KITS
Quantum software development kits (SDKs) offer various tools to design and exploit quantum programs, while also providing the user with the ability to simulate the quantum programs or prepare them to be run using cloud-based quantum devices.
The Current SDKs with access to quantum processors and/or Quantum Development Kits available are Ocean, Qiskit, ProjectQ, Forest, t|ket>, Strawberry Fields, PennyLane, and Cirq.
QUANTUM PROGRAMMING LANGUAGES
According to Nature Reviews, quantum programming languages are used to:
- manage existing physical devices
- predict quantum algorithms’ execution costs on possible devices
- examine quantum computing concepts (qubits, superposition, entanglement)
- test and verify quantum algorithms and their implementations
Current quantum programming languages and compilers are mainly focused on optimizing low-level circuits consisting of quantum gates. Quantum gates are the building blocks of quantum circuits. They are similar to reversible logic gates such as Fredkin gate, Toffoli gate, interaction gate, and switch gate. However, the smallest classical reversible gate has to use three bits, whereas the smallest quantum gate needs to use only two bits.
Most quantum programming is done in 3 types of languages: Quantum Programming Languages can be divided into language categories such as Imperative and Functional. Imperative programming is when the software utilizes statements that change a program’s state, while Functional programming is constructed by applying and composing functions.
For in-depth understanding on quantum programming languages please visit:
Imperative quantum programming languages
The most popular quantum imperative languages are:
- QCL: Quantum Computing Language, one of the first implemented quantum programming languages. It resembles C language in regards of syntax and data types.
- QMASM: Quantum Macro Assembler, published in 2016. It is a low-level language specific to quantum annealing. The significance of QMASM is that it relieves the programmer from having to know system-specific hardware details while still allowing programs to be expressed at a low level of abstraction.
- Silq is originally published in 2020. Silq is a high-level programming language written in D language which has 482 stars and 10 contributors on github and is regularly updated as of 2021.
Other imperative Q languages include Quantum pseudocode, Q|SI>, Q language, qGCL, and Scaffold.
Functional quantum programming languages
Functional languages don’t rely on step-by-step instructions, instead they depend on mathematical functions which means inputs are converted into output using mathematical transformations. Functional languages are less popular than imperative ones because they don’t support flow controls (e.g. loop statements) or conditionals (e.g. if/else statements). However, due to these features, they benefit from:
- Fewer bugs
- Programmers writing and reviewing a functional code claim to spot errors more easily because there are fewer places for surprising behavior to hide
- Functional errors are reported to be easier to fix
- Nested functions
- Lazy evaluation:
- delays the evaluation of an expression until its value is needed
- avoids repeated evaluations
Top functional languages for quantum computers are:
- QML: published in 2007, a Haskell-like quantum programming language based on strict linear logic. It can integrate reversible and irreversible quantum computations
- Quantum Lambda Calculus: it is based on classical lambda calculus introduced in 1930 and was first defined for quantum calculations in 1996. It uses high-order functions (λx.x^3) Therefore, it is stronger than the standard quantum computational models, such as quantum Turing machine or the quantum circuit model.
- QFC and QPL: Semantically QFC and QPL are equivalent. However, in QFC, quantum programs are represented using flowchart syntax, but in QPL syntactic structure of quantum programs are represented using textual representations.
Other functional languages include LIQUi|> and Quipper.
There are also multi-paradigm languages that are domain-specific such as Q# for Microsoft and Strawberry Fields for XanduAI.
Quantum Software Development Kits
Whether the developer uses imperative, functional, or multi-paradigm languages to write the quantum algorithm, a quantum software is required to create and manipulate the quantum program, and SDKs are required to run quantum circuits on prototype quantum devices, as well as on simulators. These software environments for quantum programs are usually open source and utilize python language.
Quantum Software Development Kit is a tool for developing quantum algorithms that can be used in quantum computers or simulators and emulators. A quantum simulator is an implementation of quantum gates by using classical gates. For example, Intel provides a Quantum Simulator that can simulate general quantum gates in order to test their software in simulation.
Some companies like Microsoft, IBM, Google and Rigetti, are developing open-source development kits. They provide the tools necessary for software developers to solve their own problems and enable them to access to simulators or quantum computers to implement their quantum algorithms through the cloud.
Some of the companies and their products are the leading computational platforms for quantum computing are Riverlane- DeltaFlow; Qutech- Quantum Inspire; IBM- IQ Experience; Strangeworks- Quantum Computing Platform; Google – Quantum Playground; QC Ware- Forge and Microsoft -LIQUi|>.
Researchers at Microsoft are working on an entirely new topological quantum computer, which uses exotic materials to limit errors. At the same time as Microsoft is working to build a quantum computer, it is also creating the software that could run on it. The goal is to have a system that can begin to efficiently solve complex problems from day one. Microsoft has released a preview version of their Quantum Development Kit that appears to supercede their earlier LIQUi|> software. This kit features a newly named quantum programming language called Q#, integration with their Visual Studio development environment, simulators that run on either a local system or their powerful Azure cloud platform, and rich libraries and code samples that can be used as building blocks.
“Similar to classical high-performance computing, we need not just hardware but also optimized software,” Troyer said. To the team, that makes sense: The two systems can work together to solve certain problems and the research from each can help the other side. “A quantum computer is much more than the qubits,” Reilly said. “It includes all of the classical hardware systems, interfaces and connections to the outside world.”
These kits often allow the use of classic programming languages, such as Python, or quantum software languages such as Q# developed by Microsoft. Some examples of quantum development kits are D-Wave-Ocean; Rigetti- Forest;IBM- Qiskit;Google- Cirq;Microsoft- QDK; Zapata- Orquestra; 1QBit- 1QBit SDK; Amazon- Braket SDK; ETH Zurich- ProjectQ; Xanadu- Strawberry Fields and Riverlane- Anian
Quantum Algorithm Zoo
Stephen Jordan from NIST has cataloged dozens of different algorithms that could theoretically offer substantial speedup when run on a quantum computer. Each algorithm is described in a single paragraph that also includes an estimate of the speedup and links to references and technical papers that described the algorithm in more detail. The link to this comprehensive catalog is here.
Quantum Programming Languages
Python is a good programming language as many packages like QuTip etc are available for it, which allows working with quantum systems even easier. Probably the easiest case for using this is that it’s easy to learn and a lot of the quantum frameworks have been designed with this language specifically in mind.
ProjectQ is an open-source software framework for quantum computing implemented in Python. It allows users to implement their quantum programs in Python using a powerful and intuitive syntax. ProjectQ can then translate these programs to any type of back-end, be it a simulator run on a classical computer or an actual quantum chip including the IBM Quantum Experience platform. Other hardware platforms will be supported in the future. Links to all the code and documentation as well as well as a library called FermiLib to analyze fermionic quantum simulation problems can be found at the ProjectQ web site here.
2. QISKIT (OPEN-SOURCE PROGRAMMING TOOL)
Qiskit was IBM’s gift to the quantum programming world in 2017. An open-source Software Development Kit for working with quantum computers at the level of circuits, pulses, and algorithms, Qiskit was developed by IBM Research and the wider Qiskit community and provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Quantum Experience or on simulators on a local computer.
3. OCEAN™ (QUANTUM COMPUTING PROGRAMMING SUITE)
Ocean™ software is a suite of open-source Python tools accessible via the Ocean Software Development Kit on both the D-Wave GitHub repository and within the Leap quantum cloud service. D-Wave, a pioneer in the quantum computing industry, designed Ocean to allow developers to experiment with and leverage the power of D-Wave’s Advantage quantum computer to solve complex problems.
4. Q# (QUANTUM COMPUTING PROGRAMMING ALGORITHM)
Next up is Q# — the # is pronounced ‘sharp’ — by Microsoft. Used in conjunction with the Quantum Development Kit, Q# first appeared in 2017 and is a domain-specific programming language used for expressing quantum algorithms. One advantage of this quantum programming language is it supports general classical flow control during the execution of an algorithm. In particular, classical flow control is based on quantum measurement outcomes, which makes it much easier to write things that depend on intermediate measurements.
5. CIRQ (GOOGLE AI PROGRAMMING LANGUAGE)
Developed by the team at Google Quantum AI announced (public alpha) at the International Workshop on Quantum Software and Quantum Machine Learning in the summer of 2018, Cirq is an open-source framework for noisy intermediate scale quantum (NISQ) computers. The package comes with built-in simulators, both for wave functions and for density matrices, which can deal with noisy quantum channels using monte carlo or full density matrix simulations. Cirq also works with a wavefunction simulator, qsim.
Zapata boasts of quantum optimisation breakthrough
Harvard University spin-out Zapata Computing claimed that using Google’s Cirq quantum framework it has implemented a new algorithm dubbed CUSP which could reduce complex problems to a level where they can be implemented on near-term minimal-cubit quantum processors.
CUSP is a quantum circuit optimiser that dramatically improves quantum algorithm efficiency. This algorithm and others like it will hasten breakthroughs and enable the next generation of discoveries in chemistry, materials, and artificial intelligence.’
Designed for algorithms too complex to fit on the smaller quantum computers we can build with today’s technology and too large to be optimised by hand, Zapata’s CUSP could potentially allow for a leapfrog in quantum computing technology by allowing scientists to implement workloads that would otherwise have had to wait for higher-qubit hardware to become available.
QCI launches Quantum Software for Logistics and Supply Chain
Quantum Computing Inc. (OTCQB:QUBT) (QCI), a technology leader in quantum-ready applications and tools, in Jan 2021 launched QikStart™ Program. The program partners QCI with selected participants to accelerate the adoption of quantum computing for solving mission-critical problems for business. QikStart will provide access to QCI’s industry-leading quantum acceleration platform, expert resources, and funding to explore and push the boundaries of quantum computing for delivering practical business results. QCI focuses on solving a set of some of the most complex computational problems, known as constrained optimization. Solutions to these problems optimize critical applications for business such as supply chain, logistics, drug discovery, cybersecurity, transportation and others.
Quantum Computing Inc. has introduced Qatalyst, a quantum application accelerator designed to execute complex computations to optimize supply chain, logistics, and other applications. Using one of six simple API calls, organizations can submit their computational problems using the same familiar constructs their subject-matter experts (SMEs), programmers, workflows, and applications use currently. QCI has made Qatalyst and both QPU and CPU resources available in the cloud and does not require any on-premise installs.
Qatalyst uses the power of quantum techniques to solve optimization problems. It supports the QikStart Program, QCI’s initiative to accelerate the real-world use cases for quantum computing. The Qatalyst software was formerly known as Mukai, the earlier version of the software that was used by initial adopters and quantum experts.
QCI reported in a June 2020 released scientific paper that QCI qbsolv, a component of its Mukai software execution platform for quantum computers, has delivered on its promise of immediate performance benefits from quantum-ready methods running on classical computers.
Optimization problems can occur in logistics routing, where timely delivery, reduced fuel consumption, and driver safety all come into play. Optimization solutions can significantly mitigate the impact to revenue or business operations posed by events such as flooding or power outages. Companies can leverage the robust and diverse solutions offered by Mukai to minimize disruptive high-impact events in real-time. Optimization can also be achieved in R&D contexts like drug design, where better predicted protein folding can speed the design process, increase the efficacy of drugs, and guide the search for patient cohorts who might benefit. Optimization of business processes generated by solvers like Mukai can result in savings of hundreds of billions of dollars annually.
The technical study used MIT’s MQlib, a well-established combinatorial optimization benchmark, to compare QCI qbsolv performance with those of a variety of solvers. QCI qbsolv delivered better quality or “energy” of results for most problems (27 of 45) and often ran more than four times faster than the best MQlib solver (21 of 45 problems).
In terms of diversity of results—finding, for example, logistics routes that are quite different from each other—QCI qbsolv often found dozens of binary results that were different in more than 350 different positions (i.e., route segments). Known also to researchers as Hamming distance, diversity of results is another important advantage expected of quantum computing. The paper, QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation, describes the full results and discusses their impact, and is available at arxiv.org/abs/2005.11294.
“These results demonstrate that Mukai-powered applications can exploit quantum computing concepts to solve real-world problems effectively using classical computers,” noted QCI CTO, Mike Booth. “More importantly, the quality, speed, and diversity of solutions offered by Mukai means government and corporate organizations can use Mukai to adopt quantum-ready approaches today without sacrificing performance. Mukai is also hardware-agnostic, enabling adopters to exploit whichever hardware delivers the quantum advantage. We’re confident that leading companies can leverage Mukai today to achieve a competitive advantage.”
“To be sure, we are very early in the quantum computing and software era,” continued Booth. “Just as the vectorizing compilers for Cray’s processors improved radically over time, we are planning to introduce further performance improvements to Mukai over the coming months. Some of these advancements will benefit application performance using classical computers as well as hybrid quantum-classical scenarios, but all will be essential to delivering the quantum advantage. We expect Mukai to play an integral role in the quantum computing landscape by enabling organizations to tap into quantum-inspired insights today to better answer their high-value problems.”
The Mukai software execution platform for quantum computers enables users and application developers to solve complex discrete constrained-optimization problems that are at the heart of some of the most difficult computing challenges in industry, government and academia. This includes, for example, scheduling technicians, parts and tools for aircraft engine repair, or designing proteins for coronavirus vaccines and therapies. QCI earlier announced version 1.1 of Mukai, which introduced higher performance and greater ease-of-use for subject-matter experts who develop quantum-ready applications and need superior performance today. Local software connects users to the Mukai cloud service for solving extremely complex optimization problems. It enables developers to create and execute quantum-ready applications on classical computers today that are ready to run on the quantum computers of tomorrow when these systems achieve performance superiority.
Google new open-source software will help developers experiment with the machines, including Google’s own super-powerful quantum processor.
Google has just released Cirq, a software toolkit that lets developers create algorithms without needing a background in quantum physics. Cirq is an open-source initiative, which means anyone can access and modify the software. Google likens it to its popular TensorFlow open source toolkit that has made it easier to build machine-learning software. For now, developers can use Cirq to create quantum algorithms that run on simulators. But the goal is to have it help build software that will run on a wide range of real machines in the future.
The tech giant has also released OpenFermion-Cirq, a toolkit for creating algorithms that simulate molecules and properties of materials. Indeed, chemistry is among the applications in which quantum computers are likely to be of most use in the short term. One of the companies that worked with Google on Cirq’s development is Zapata Computing, whose early focus is on software for chemistry and materials (see “The world’s first quantum superstore—or so it hopes—is here”).
Raytheon BBN Open Source Software
Raytheon BBN is make available three open source software programs related to Quantum Computing.
Qlab – A MATLAB control framework for superconducting qubit systems.
PySimulator – A python/C++ framework for master equation simluation of qubit systems.
PyQLab – A python framework for superconducting qubit systems. Includes Quantum Gate Language (QGL) for compactly writing QIP pulse programs
QX Quantum Computing Simulator
The QX Simulator is a universal quantum computer simulator developed at QuTech. The QX allows quantum algorithm designers to simulate the execution of their quantum circuits on a quantum computer. The simulator defines a low-level quantum assembly language namely Quantum Code which allows the users to describe their circuits in a simple textual source code file. The source code file is then used as the input of the simulator which executes its content.
IBM makes available five qubit general purpose quantum computer for developing Quantum software
IBM Research has also made quantum computing available to members of the public, who can access and run experiments on IBM’s quantum processor. IBM scientists have built a quantum processor that users can access through a first-of-a-kind quantum computing platform delivered via the IBM Cloud onto any desktop or mobile device. The cloud-enabled quantum computing platform, called IBM Quantum Experience, will allow users to run algorithms and experiments on IBM’s quantum processor, work with the individual quantum bits (qubits), and explore tutorials and simulations around what might be possible with quantum computing.
At the IBM Quantum Experience website there are four modules; a short tutorial that explains the basics of quantum computation and instructions on how to use it, a quantum composer that allows one to configure quantum gates for the qubits, a simulator which allows one to simulate their configuration before running it on the actual machine, and finally access to the machine itself which allows one to run their configuration and view the results.
IBM have developed the Quantum Information Software Kit (QISKit), which is a full-stack library to write, simulate and run quantum programs. Recently, they have divided it in four pieces: Terra, which allows to program at the level of quantum gates and pulses (quantum gates are implemented with sequences of pulses); Aqua, a higher level of programming to run algorithms used in quantum chemistry, optimization problems and A.I.; Ignis, to characterize errors and improve gate implementation; Aer, to study the limits of quantum computation using simulations in classical devices. QISKit translates quantum programs into a lower level language called QASM, which is its quantum instruction language
Super.tech Announces Quantum Software Platform SuperstaQ in August 2021
SUPER.TECH LABS INC. announced SuperstaQ, a hardware-agnostic software platform that connects applications to quantum computers from IBM Quantum, IonQ, and Rigetti. SuperstaQ delivers performance gains via optimizations that span the entire system stack, down to the analog pulses that control techniques that operate on quantum hardware. These software optimizations boost the performance of underlying quantum computers and aim to accelerate the commercial viability of quantum computing.
“SuperstaQ will play a unique role in bridging the gap from quantum hardware to practical applications,” said Pranav Gokhale, Co-founder and CEO of Super.tech. “By setting new records for quantum performance, SuperstaQ showcases the vital role that software will play in the path toward commercially-relevant quantum speedups.”
The first adopters of SuperstaQ’s beta launch include engineers and researchers from EPiQC, Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed (AQT at Berkeley Lab), and Morningstar. SuperstaQ’s beta launch comes shortly after Super.tech was awarded a Small Business Innovation Research grant from the National Science Foundation and was accepted into the inaugural cohort of Duality, the nation’s first startup accelerator dedicated to quantum technology.
“The Advanced Quantum Testbed’s user program is an open hub for cutting-edge R&D, and our joint work with Super.tech has led to significant improvements in quantum algorithm performance on noisy hardware via an optimized compilation of quantum circuits,” said Irfan Siddiqi, Berkeley Lab’s AQT Director.“Improving performance will be a key metric on the road to Quantum Advantage and we look forward to continue collaborating with Super.tech to push Quantum performance boundaries,” said Aparna Prabhakar, Vice President, Partner Ecosystem, IBM Quantum.
SuperstaQ is integrated with the IBM Qiskit ecosystem through the open source qiskit-superstaq repository. Users can also interact with SuperstaQ through Cirq via cirq-superstaq, or through the OpenAPI at superstaq.super.tech/api.
SuperstaQ provides a library of sophisticated speedups and optimizations, including a technique that recently achieved the top-ranking submission in an IBM Quantum Open Science Prize. Combined with anticipated increases in qubit counts and reductions in error rates, Super.tech’s quantum software will enable businesses across industries like energy, finance, and logistics to discover solutions to high-value computational problems.