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Introduction: Quantum Computing Challenges and NISQ
The field of Quantum Computing (QC) has made significant strides in recent years, with advancements in the number of qubits that can be realized and the development of new quantum algorithms for search and optimization. However, several challenges hinder the practical application of QC to real-world problems. These challenges include scalability, environmental interactions, input/output, qubit connectivity, quantum memory limitations, and more. While experimental quantum devices will require years of research to become a reality, Noisy Intermediate Scale Quantum (NISQ) computers are already here. NISQ computers consist of hundreds of noisy qubits, which perform imperfect operations with limited coherence time. The concept of NISQ was introduced by theoretical physicist John Preskill in 2017 to describe this new era in quantum technology.
For in-depth understanding on Quantum Computing technology and applications please visit: Quantum Computing Technology: Advancements, Applications and Engineering
NISQ’s Potential and DARPA’s Exploration
NISQ devices have already demonstrated processors with over 100 qubits and exciting developments. Under the DARPA ONISQ program, researchers achieved a qubit-circuit depth product exceeding 2,000 and are actively working on systems with qubit-circuit depth products greater than 10,000. These NISQ systems have the potential to solve computationally complex problems in fundamentally different ways compared to classical systems. While these results are preliminary and applied to a limited set of test problems, they have opened the door to exploring hybrid quantum/classical computational systems.
IMPAQT ARC Opportunity: Bridging Quantum and Classical Computing
NISQ devices, which have already demonstrated processors with over 100 qubits, are making significant progress. Under the DARPA ONISQ program, researchers have achieved a qubit-circuit depth product exceeding 2,000 and are actively working on systems with qubit-circuit depth products greater than 10,000. These NISQ systems have shown that hybrid classical/quantum algorithms behave fundamentally differently compared to purely classical systems when tackling complex computational problems. This highlights their potential to address certain problems more efficiently, even with their current limitations, and lays the groundwork for more advanced quantum technologies in the future.
DARPA’s IMPAQT ARC Opportunity seeks to address a fundamental question: What are the applications for a quantum system with Nq > 10,000 when used as a co-processor alongside classical computational systems? The technical objective of the IMPAQT ARC is to explore novel algorithms and applications that leverage the potential of quantum systems expected to be demonstrated in the next few years.
IMPAQT aims to bridge the gap between quantum computing researchers and domain experts in classical solutions, fostering collaboration. Research teams participating in IMPAQT will work to identify ways to effectively integrate quantum systems with Nq greater than 10,000 into a hybrid quantum/classical computational framework. This includes defining problems that can benefit from this hybrid approach, specifying quantum algorithms, and identifying any additional classical resources needed.
The ultimate goal of the IMPAQT program is to validate these quantum algorithms through available cloud-based quantum processor resources or to quantify the resources required for testing. As quantum technology continues to evolve, the IMPAQT program holds the potential to revolutionize problem-solving across various domains, providing a glimpse into the future of computing.
Rigetti Computing Awarded DARPA IMPAQT Contract
Rigetti Computing, Inc. (Nasdaq: RGTI), a pioneer in full-stack quantum-classical computing, has announced its successful award of a Defense Advanced Research Projects Agency (DARPA) project under the Imagining Practical Applications for a Quantum Tomorrow (IMPAQT) program. The project, aptly named “Scheduling Problems with Efficient Encoding of Qubits” (SPEEQ), is designed to advance quantum algorithms for solving combinatorial optimization problems. SPEEQ aims to develop an innovative and efficient method for encoding optimization problems onto qubits, with the ultimate objective of enabling larger problems to be processed by today’s Noisy Intermediate Scale Quantum (NISQ) era quantum computers. The specific focus will be on scheduling problems, a widely recognized and complex category of combinatorial optimization challenges encountered across various industries.
Present quantum algorithms face limitations in problem-solving capabilities due to the finite number of qubits available on Quantum Processing Units (QPUs). SPEEQ seeks to overcome this limitation by enhancing the potential for quantum algorithms to tackle larger problems effectively, allowing for more meaningful comparisons with classical heuristic algorithms. Current hybrid quantum-classical algorithms are only capable of solving problems approximately 100 times smaller than those solvable by classical algorithms, making it challenging to assess their performance at a relevant scale.
The SPEEQ project evolved from Rigetti’s participation in the DARPA ONISQ program, which involved “Scheduling Applications with Advanced Mixers” (SAAM) in collaboration with NASA and USRA. The team is currently developing hybrid quantum-classical algorithms for solving binary optimization problems, demonstrating improved algorithmic performance with an increasing number of quantum operations. However, classical heuristic algorithms can still efficiently solve problem sizes with up to 10,000 variables. SPEEQ will leverage the insights and benchmarks from the SAAM project to address a fundamental question concerning the trade-off between the number of qubits used and the number of quantum operations, a crucial aspect in the design of new algorithms.
Dr. Subodh Kulkarni, CEO of Rigetti, emphasized the company’s commitment to developing practical quantum computing applications. The DARPA IMPAQT program represents a valuable opportunity to further their algorithm research in optimization problems, which have a broad societal impact. The advancement of algorithms for hybrid quantum-classical quantum computing systems is pivotal in achieving quantum advantage in specific domains. The qubit-efficient encoding method proposed in this project has the potential to deliver benefits extending beyond scheduling problems, potentially revolutionizing complex operations in industries such as supply chains and logistics.
Infleqtion Chosen for DARPA’s IMPAQT Program
Infleqtion, a leading quantum information company, has been selected by the Defense Advanced Research Projects Agency (DARPA) to participate in the Imagining Practical Applications for a Quantum Tomorrow (IMPAQT) program. The IMPAQT program aims to develop practical applications for quantum computing, and Infleqtion’s technology will be used to develop new machine learning algorithms that can be used to solve complex problems in areas such as genomics, finance, and materials science.
Infleqtion’s quantum machine learning technology is based on the company’s proprietary quantum algorithms and software. These algorithms are designed to take advanta
BlueQubit’s Algorithm Chosen by DARPA: Leveraging GPU Simulators and Infrastructure to Advance Quantum AI
BlueQubit, a company building quantum software and infrastructure, has been chosen for a prestigious DARPA project called Imagining Practical Applications for a Quantum Tomorrow (IMPAQT). This recognition highlights BlueQubit’s dedication to revolutionizing quantum computing, specifically by developing quantum AI/ML algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices.
DARPA selected BlueQubit due to its focus on addressing challenges faced by classical computers, particularly in Gibbs sampling for crucial applications like cybersecurity and training large AI models. With the rise of quantum hardware boasting increased qubits and gates, new solutions are emerging. BlueQubit is venturing into hybrid computing environments, emphasizing quantum/classical hybrid algorithms like QAOA as game-changers. These algorithms are designed to maximize the potential of NISQ devices and find solutions without requiring fault-tolerant quantum computers.
A key aspect of the project is BlueQubit’s collaboration with QuEra, a renowned quantum hardware company with expertise in neutral-atom quantum computers. This expertise, known for its scalability and improved gate fidelity, is central to the project’s success. BlueQubit, along with its esteemed researchers and tech maestros, is poised to overcome classical limitations and achieve quantum advantage through its innovative hybrid computing approach.
QuEra Computing
QuEra Computing, a company specializing in neutral-atom quantum computers, has secured two grants from DARPA under the Imagining Practical Applications for a Quantum Tomorrow (IMPAQT) program. The first grant focuses on “Quantum Reservoir Learning using Neutral Atoms and its Applications,” extending QuEra’s quantum machine learning work to tackle real-world issues like image recognition and natural language processing.
The second grant, “Error-Corrected Quantum Architectures Based on Transversal Logical Gates,” aims to enhance the reliability and scalability of quantum computation using specialized logical gates.
QuEra’s technology, based on large-scale arrays of neutral atoms, offers advantages like high coherence and flexible qubit array configuration. For example, QuEra can create qubits with high coherence, which means they can preserve their quantum state for longer times. QuEra can also reconfigure the layout of the qubits using its field-programmable qubit array (FPQA) technology, which allows for more flexibility and efficiency in quantum computation. Additionally, five partners have received DARPA grants for projects utilizing QuEra’s neutral-atom hardware.
Conclusion: A Quantum Leap Forward in Problem-Solving
While challenges remain in harnessing quantum computing’s full potential, programs like IMPAQT are driving the development of practical hybrid quantum/classical systems that could transform computational problem-solving for years to come.
