NISQ Computing: What Is It and Why Does It Matter?

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NISQ Computing: What Is It and Why Does It Matter?



In the realm of technology, quantum computing stands tall as an awe-inspiring and promising frontier. Unlocking the potential to conquer intricately knotted enigmas that elude classical computers, it paves the way for groundbreaking solutions in cryptography, optimization, machine learning, chemistry, physics, and beyond. Nonetheless, this cutting-edge field bears the weight of arduous challenges and a hefty price tag in practical implementation. The current phase of quantum computing, known as the noisy intermediate-scale quantum (NISQ) era, finds itself constrained due to the size and quality of available quantum processors.



Within the confines of this article, we embark on an expedition into the realm of NISQ computing. Diving deep into its essence, we shall unravel the enigma of its applications, grapple with the challenges it presents, and peer into the horizon of its future prospects. Moreover, we shall cast the spotlight on the eminent personalities and key contenders who partake in the NISQ computing market.



A NISQ processor with qubits and gates in different colors


What Is NISQ Computing?



NISQ stands for noisy intermediate-scale quantum. The term was coined by John Preskill in 2018 to describe the first generation of quantum processors that have emerged in recent years. 


These processors have some key features that distinguish them from both classical computers and ideal quantum computers:



Within their arsenal, lie qubits in a moderate count, spanning from approximately 20 to just beyond the 100 mark. At the core of quantum information, qubits reign as the elemental entities, capable of occupying a superposition of both states, 0 and 1, while simultaneously forging connections of entanglement with their fellow qubits.



In the realm of quantum computing, these qubits bear the burden of noise, rendering them susceptible to errors and decoherence as a consequence of their interaction with the surrounding environment. These errors can affect the accuracy and reliability of the quantum computations.



They are not fault-tolerant, which means that they cannot correct or prevent errors using techniques such as error correction codes or logical qubits. Fault tolerance is essential for scaling up quantum computers to larger sizes and more complex tasks.



They are not universal, which means that they cannot perform any arbitrary quantum algorithm or operation. Contrary to a one-size-fits-all approach, their design is tailored to cater to particular algorithms or applications, thereby harnessing their unique advantages and offsetting any limitations they might possess.



In the domain of NISQ processors, a defining feature is their quantum volume, serving as a yardstick to gauge their prowess. This metric assesses their performance through a trinity of factors: the qubit count, the gate fidelity (indicating the likelihood of a gate functioning accurately), and the circuit depth (measuring the succession of gates in a circuit). As the quantum volume ascends, so does the processor’s capability.



A NISQ processor simulating a molecule


What Are the Applications of NISQ Computing?



Despite their limitations, NISQ processors can still offer some advantages over classical computers for certain problems or domains. Some of the most promising applications of NISQ computing are:



Quantum Chemistry: With finesse, NISQ processors delve into the quantum realm, emulating the intricate behaviors and characteristics of molecules and materials. This opens doors to a realm of groundbreaking discoveries and fresh innovations across diverse domains like drug design, catalysis, energy storage, and nanotechnology.



Quantum Optimization: Harnessing their capabilities, NISQ processors exhibit mastery in tackling arduous optimization conundrums that entail sifting through numerous potential solutions to identify the optimal one. Tasks like scheduling, routing, portfolio management, machine learning, and an array of other challenges fall within their grasp.



Quantum Machine Learning: Within the realm of machine learning, NISQ processors possess the power to amplify and expedite certain facets of the process. From data encoding and feature extraction to model training, inference, and classification, their prowess extends across various crucial dimensions of the machine learning journey.



A NISQ processor enabling quantum cryptography


Quantum Cryptography: With their cutting-edge capabilities, NISQ processors pave the way for secure communication and data safeguarding through the implementation of quantum protocols like quantum key distribution (QKD). This ingenious method enables two entities to share an impregnable secret key, rendering it immune to interception or eavesdropping by any third-party intruders.



In the realm of NISQ processors, researchers have crafted specialized algorithms designed to cater specifically to these devices. Known as variational or hybrid algorithms, they seamlessly blend both quantum and classical computation in an iterative dance. The crux lies in leveraging the quantum processor to generate a trial solution for a given problem, be it an energy state of a molecule or a candidate solution for an optimization challenge. Subsequently, the classical processor steps in to evaluate and refine the solution based on predefined criteria, such as an objective or cost function. This process repeats itself until convergence or termination is achieved, ensuring the most effective outcome.



A NISQ processor solving an optimization problem


Some examples of variational or hybrid algorithms And Use Cases for NISQ computing are:



Variational Quantum Eigensolver (VQE): This algorithm can find the lowest energy state (ground state) of a quantum system (such as a molecule) by using a parametrized quantum circuit (ansatz) to prepare trial states and measuring their energies using a classical computer.



Quantum Approximate Optimization Algorithm (QAOA): This algorithm can find approximate solutions for combinatorial optimization problems (such as traveling salesman problem or max-cut problem) by using a parametrized quantum circuit (ansatz) to prepare trial states and measuring their costs using a classical computer.



Quantum Neural Networks (QNNs): These are quantum analogs of classical neural networks, which can learn from data and perform tasks such as classification, regression, or generative modeling. QNNs can use parametrized quantum circuits (ansatz) to encode, process, and output data, and use classical computers to train and update the parameters using gradient descent or other methods.




What Are the Challenges of NISQ Computing?



As NISQ computing takes its tentative first steps, it finds itself confronted with an array of challenges and limitations that demand conquering to unlock its boundless potential. Among the primary hurdles are:



Error mitigation: As previously alluded, NISQ processors grapple with the issue of noise and lack of fault tolerance, exposing their computations to errors that can potentially impact the accuracy and integrity of their outcomes. To reduce the impact of errors, researchers have developed various techniques such as post-processing, error extrapolation, error correction codes, or error-aware algorithms. However, these techniques are not perfect and may introduce additional overhead or complexity.



Scalability: NISQ processors are limited and cannot accommodate large or complex problems requiring more qubits or deeper circuits. To increase the scalability of NISQ processors, researchers have explored various approaches such as qubit connectivity, modular architectures, distributed systems, or quantum-classical hybrid systems. However, these approaches may also introduce additional challenges such as communication latency, synchronization, or coherence preservation.



Benchmarking: NISQ processors are diverse and heterogeneous, which means that they have different specifications, architectures, designs, and performance. To compare and evaluate NISQ processors objectively and consistently, researchers have proposed various metrics and benchmarks such as quantum volume, quantum supremacy, quantum advantage, or quantum readiness. However, these metrics and benchmarks are not universally agreed upon or standardized and may depend on the context or application of interest.



Interoperability: NISQ processors are developed by different vendors and organizations, which may have different protocols, formats, languages, or interfaces for accessing and using them. To enable interoperability and compatibility among NISQ processors and users, researchers have developed various frameworks and platforms such as OpenQASM, Qiskit, Cirq, or Q#. However, these frameworks and platforms are not fully compatible or integrated with each other, and may require additional effort or adaptation to use.



What Is the Future of NISQ Computing?



NISQ computing is a rapidly evolving and expanding field with many opportunities and challenges. Some of the possible directions and trends for the future of NISQ computing are:



Beyond-NISQ computing: Quantum computing aims to achieve fault-tolerant and universal quantum computers that can perform any quantum algorithm or operation with high accuracy and reliability. To reach this goal, researchers are working on developing new technologies and methods such as topological qubits, surface codes, logical qubits, or error correction codes. These technologies and methods may enable the transition from NISQ computing to beyond NISQ computing in the future.



Quantum cloud computing: The access and availability of NISQ processors are still limited and expensive for most users. To democratize and popularize NISQ computing, researchers are working on providing quantum cloud computing services that allow users to access and use NISQ processors remotely via the internet. Some examples of quantum cloud computing providers are IBM Quantum Experience, Amazon Braket, Microsoft Azure Quantum, Google Quantum AI Cloud, or Alibaba Cloud Quantum Development Platform.



Quantum software engineering: The development and deployment of NISQ applications require many skills and expertise in quantum physics and computer science. To facilitate and streamline the NISQ software engineering process, researchers are developing new tools and methods such as quantum programming languages, quantum compilers, quantum debuggers, quantum simulators, quantum libraries, or quantum testing frameworks.



Quantum education: The growth and success of NISQ computing depend on the availability and quality of human resources who can understand and utilize NISQ technology. To educate and train the next generation of quantum professionals and enthusiasts, researchers are working on developing new resources and platforms such as quantum textbooks, quantum courses, quantum tutorials, quantum games, or quantum contests.



A chart comparing quantum volume


Who Are the Main Players in the NISQ Computing Market?



NISQ computing is a highly competitive and collaborative market that involves many players from several and various sectors such as academia, industry, government, or non-profit. Some of the main players in the NISQ computing market are:



Academia: Globally, a multitude of universities and research institutes are actively at the forefront of pioneering research in diverse realms of NISQ computing. From delving into its theoretical underpinnings to crafting innovative algorithms, exploring applications, and pushing the boundaries of hardware capabilities, these institutions stand out as beacons of progressive epitomes. Noteworthy examples include several organizations including MIT, Stanford, Caltech, Harvard, Oxford, Cambridge, ETH Zurich, and the University of Waterloo.



Industry: A dynamic ecosystem of companies and startups is fervently engaged in the development and delivery of various NISQ products and services. Ranging from cutting-edge quantum processors to cloud platforms, software tools, and innovative applications, they cater to the burgeoning quantum landscape. Distinguished industry players such as IBM, Google, Microsoft, Amazon, Intel, Alibaba, Rigetti, IonQ, Xanadu, and Zapata lead the charge, shaping the NISQ computing landscape with their ingenuity and vision.



Government: Many governments and agencies are supporting and funding NISQ research and development through various programs and initiatives such as the National Quantum Initiative in the US, the Quantum Flagship in the EU, the Quantum Information Science and Technology Program in China, or the Quantum Technology Innovation Hub in India.



Non-profit: Many non-profit organizations and foundations are promoting and advancing NISQ education and outreach through various activities and resources such as the Quantum Computing Report, the Qiskit Global Summer School, the Q# Coding Contest, or the Quantum Open Source Foundation.




NISQ Startups and Vendors



Amidst the NISQ era, a remarkable surge of startups and vendors has emerged, zealously committed to crafting and delivering cutting-edge NISQ devices and services. Noteworthy contenders in this dynamic arena include:



IBM: Steering the vanguard in superconducting qubit technology, IBM has etched its name among the pioneers. Back in 2016, IBM Quantum Experience took flight, extending an opportunity for users to harness the power of IBM’s NISQ devices through seamless cloud services. IBM has also developed Qiskit, an open-source framework for programming quantum computers. IBM has announced its quantum roadmap, which aims to reach 1000 qubits by 2023 and introduce modular quantum computers that can connect multiple processors together.



Google: Google has also been advancing its superconducting qubit technology and has claimed to achieve quantum supremacy in 2019, with its 53-qubit Sycamore processor. Google has also developed Cirq, an open-source framework for programming quantum computers, and TensorFlow Quantum, an extension of TensorFlow for quantum machine learning. In pursuit of quantum supremacy, Google has charted a visionary quantum roadmap. This ambitious trajectory sets its sights on achieving error-corrected logical qubits by the year 2029.



Microsoft: Diverging from the conventional path, Microsoft embarks on an alternative route to quantum computing, centered around topological qubits. These specialized qubits hold the promise of heightened coherence and reduced error rates, outshining other qubit variants. Although still in the experimental phase, topological qubits are poised to revolutionize the field. In tandem with this innovative pursuit, Microsoft has crafted Q#, a dedicated programming language tailored for quantum computing. Furthermore, Azure Quantum is a cloud service extending access to a plethora of quantum devices and software tools curated by Microsoft and its esteemed partners.



Amazon: Making its foray into the quantum computing arena in 2019, Amazon stepped into the limelight with the introduction of Amazon Braket. A groundbreaking cloud service, Amazon Braket empowers users with unfettered access to an array of quantum devices and software tools carefully curated by Amazon and its esteemed partners. Amazon has also established the Amazon Quantum Solutions Lab , a program that helps customers explore and implement quantum solutions for their problems. In pursuit of quantum advancements, Amazon’s commitment shines through with the inauguration of the AWS Center for Quantum Computing. This innovative research center stands as a beacon, devoted to the development of cutting-edge quantum hardware and software, propelling the field into new frontiers.



Intel: Embarking on a dual journey, Intel delves into the development of two distinct qubit technologies: superconducting qubits and silicon-based spin qubits. Of the two, the latter emerges as a promising contender, boasting advantages in scalability and seamless integration with pre-existing semiconductor technology. Intel has also developed Qiskit Aer, a high-performance simulator for quantum circuits, and QATrack, a tool for benchmarking and optimizing quantum algorithms.



Rigetti Computing: Rigetti is a startup that focuses on superconducting qubit technology and cloud services. Rigetti has developed Forest, a cloud platform that allows users to access Rigetti’s NISQ devices and software tools. Rigetti has also launched Quantum Cloud Services, a platform that enables users to run hybrid quantum-classical applications on Rigetti’s devices and AWS servers. Rigetti has also announced its quantum roadmap, which aims to reach 128 qubits by 2023 and introduce modular quantum computers that can connect multiple processors together.



IonQ: IonQ is a startup that focuses on trapped ion qubit technology, which offers high fidelity and long-range connectivity among qubits. IonQ has developed two NISQ devices with 11 and 32 qubits, respectively, which are available through cloud services such as Amazon Braket, Microsoft Azure Quantum, and Google Cloud. IonQ has also announced its quantum roadmap, which aims to reach 64 qubits by 2023 and introduce modular quantum computers that can connect multiple processors together.



Xanadu Quantum Technologies: Xanadu is a startup that focuses on photonic qubit technology, which uses light as the carrier of quantum information. Photonic qubits have advantages in speed, scalability, and integration with existing optical communication networks. Xanadu has developed Xanadu Quantum Cloud, a cloud service that allows users to access Xanadu’s photonic NISQ devices and software tools. Xanadu has also developed PennyLane, an open-source framework for quantum machine learning, optimization, and chemistry.



PsiQuantum: PsiQuantum is a startup that aims to build a large-scale fault-tolerant quantum computer using photonic qubits. PsiQuantum claims to have achieved several milestones in developing silicon photonic chips that can generate, manipulate, and measure millions of qubits. PsiQuantum has partnered with GlobalFoundries, a semiconductor manufacturer, to produce its chips at scale.


Conclusion



NISQ computing is a new and exciting field that offers many opportunities and challenges for quantum technology. NISQ processors can perform some tasks that are very hard or almost impossible for classical computers, such as quantum chemistry, quantum optimization, quantum machine learning, or quantum cryptography. However, NISQ processors also have several limitations and drawbacks that need to be overcome before they can achieve their full potential and accuracy. To address these issues, researchers are working on developing new technologies and methods such as beyond-NISQ computing, quantum cloud computing, quantum software engineering, or quantum education. NISQ computing is a highly competitive and collaborative market that involves several players from different sectors such as academia, industry, government, or non-profit. In the dynamic realm of NISQ computing, an ever-changing landscape gradually unfolds, brimming with countless avenues and trends that pave the way for a really exciting and expansive future.



I trust that you found this article a delightful read. If any queries or feedback arise regarding NISQ computing or the content herein, do not hesitate to reach out. Your attention and engagement are greatly appreciated. 😊



References:



(1) Noisy intermediate-scale quantum era – Wikipedia. 



(2) NISQ Computing: Where Are We And Where Do We Go? – Springer. 



(3) What Is NISQ Quantum Computing? – The Quantum Insider. 



(4) NISQ Computers – Can We Escape The Noise? | Quantum Computing Inc.




FAQs On Quantum Computing And NISQ Computing Answered:



What is meant by quantum computing?



Venturing into the realm of computer science lies quantum computing, harnessing the enigmatic principles of quantum mechanics to execute computations. Underpinning this field, quantum mechanics unravels the behavior of matter and energy at the tiniest scales, where they manifest characteristics of both particles and waves. At the heart of quantum computing lie qubits, utilizing subatomic entities like electrons or photons to store and manipulate information in a quantum manner. This cutting-edge technology holds the potential to outpace classical computing in solving certain problems, as classical computing relies on bits as its fundamental unit of information.




What can quantum computers do more efficiently than regular computers?



Unveiling new vistas of computational power, quantum computers delve into tasks that prove challenging or even insurmountable for traditional computers. From simulating quantum systems to resolving optimization problems, enriching machine learning, and even breaking encryption, quantum computers wield unparalleled potential. This prowess emerges from their utilization of quantum bits, or qubits, which can coexist in a state of superposition and entanglement. By harnessing these quantum phenomena, they delve into multiple possibilities concurrently, their interference yielding constructive or destructive outcomes. Furthermore, quantum computers exploit these quantum phenomena to employ specialized quantum algorithms, unlocking speedups and advantages that outshine classical algorithms.




What are the 3 types of quantum computers?



There are different ways to classify quantum computers based on their physical implementation, their computational model, or their application domain. One possible way to classify them based on their physical implementation is:



Superconducting quantum computers: In the realm of quantum computing, a notable category emerges, housing superconducting quantum computers that employ superconducting circuits as qubits. These specialized electrical circuits showcase an intriguing trait—achieving zero resistance when subjected to extremely low temperatures. By harnessing microwave pulses, superconducting qubits become amenable to manipulation, complemented by coupling with capacitors or inductors. IBM Quantum, Google Quantum AI, Intel Quantum, and Rigetti Computing stand as noteworthy exemplars in the domain of superconducting quantum computers.



Trapped ion quantum computers: These are quantum computers that use trapped ions as qubits. Trapped ions are charged atoms that are confined by electromagnetic fields in a vacuum chamber. Trapped ion qubits can be manipulated by laser pulses and coupled by shared vibrations. Some examples of trapped ion quantum computers are IonQ, Honeywell Quantum Solutions, and Alpine Quantum Technologies.



Photonic quantum computers: These are quantum computers that use photons as qubits. Photons are particles of light that can travel in free space or optical fibers. Photonic qubits can be manipulated by optical elements such as mirrors, beam splitters, or phase shifters, and coupled by interference or detection. Some examples of photonic quantum computers are Xanadu Quantum Technologies, PsiQuantum, and Amazon Braket.




What does NISQ stand for?



NISQ stands for noisy intermediate-scale quantum. This term was coined by John Preskill in 2018 to describe the current state of quantum technology, which is characterized by quantum processors that have up to 1000 qubits but are not advanced enough for fault tolerance or large enough to achieve quantum supremacy. NISQ processors are noisy, which means that they are prone to errors and decoherence due to their interaction with the environment. NISQ processors are not universal, which means that they cannot perform any arbitrary quantum algorithm or operation. Instead, they are designed for specific types of algorithms or applications that can exploit their advantages and mitigate their limitations.




What is a NISQ device?



In the realm of quantum computing, a NISQ device occupies a unique position, operating within the NISQ regime. This entails a moderate qubit count, spanning approximately 20 to just beyond 100, alongside a noteworthy error rate of around 0.1% to 10%, and a comparatively low circuit depth of about 10 to 1000. Emphasizing its capabilities, a NISQ device is gauged by its quantum volume, a pivotal metric that evaluates performance through the number of qubits, gate fidelity, and circuit depth. As the quantum volume scales higher, so does the device’s potency and potential.

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