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    Home»Technology»Toward Quantum Advantage: Qunova’s HiVQE Algorithm Transforms Quantum Chemistry
    Technology

    Toward Quantum Advantage: Qunova’s HiVQE Algorithm Transforms Quantum Chemistry

    By QunovaOctober 14, 2024No Comments5 Mins Read
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    Qunova Computing has successfully tested its HiVQE algorithm on multiple quantum devices, achieving chemical accuracy crucial for chemistry applications, indicating a potential quantum advantage with as few as 40-60 qubits. Credit: SciTechDaily.com

    Qunova Computing’s recent breakthrough using its HiVQE algorithm not only achieved chemical accuracy on several NISQ quantum computers but also accelerated computations by 1,000 times.

    This advancement significantly narrows the gap to achieving a quantum advantage in chemistry, offering the potential to revolutionize how we approach complex chemical problems with quantum computing. This achievement marks a major leap towards practical and scalable quantum applications in the chemical industry.

    Groundbreaking Results in Quantum Computing

    Qunova Computing, a company developing quantum software for the chemical, pharmaceutical, and industrial engineering sectors, announced today that its algorithm has achieved unprecedented levels of accuracy in tests on three different Noisy Intermediate-Scale Quantum (NISQ) era quantum computers, each with varying numbers of qubits. In each test, the algorithm produced results with an accuracy below the 1.6 millihartrees threshold required for practical quantum chemistry applications, a standard referred to as ‘chemical accuracy’. This is the first time such accuracy has been reached on a commercially available quantum computer.

    Pioneering Achievements Demonstrated Live

    “This is a very exciting result for our team, and indeed for the quantum computing community more broadly,” said June-Koo Kevin Rhee, CEO and Founder of Qunova Computing. “These results show that we are able to meet the requirements of industrial users on existing NISQ machines. We anticipate that running a similar demonstration on a NISQ machine with as few as 40 qubits could provide industrial users with a real quantum advantage. To that end, our team will spend the coming months preparing experiments to confirm if this theory is correct.”

    Qunova at Quantum Korea
    A crowd gathers to learn more about how Qunova’s algorithm has helped achieve chemical accuracy on quantum computers for the first time. Credit: Qunova Computing

    Hardware-Agnostic Algorithm Performance

    During the Quantum Korea 2024 event, Qunova demonstrated chemical accuracy using a 20-qubit IQM machine. This demonstration was performed successfully for 3 days in a row, to produce energy estimations of three different geometries of lithium sulfide (Li2S) for an hour each day, live at the event. Previous to that, in a 24-qubit experiment using an IBM Quantum Eagle processor, Qunova also demonstrated its algorithm could reach a computational accuracy of 0.1 millihartrees in modeling the ground state energy of lithium sulfide, which is well beyond what is required for chemical accuracy. The company has also recently achieved comparable results using the IBEX Q1 quantum computer, an ion-based machine from AQT that supports up to 20 qubits.

    These results indicate that the quantum algorithm Qunova has developed is hardware-agnostic. These tests were conducted on a range of different molecules including lithium sulfide, hydrogen sulfide, water, and methane.

    Industry Partnerships and Future Applications

    “The results Qunova has demonstrated mark a significant milestone for end-users aiming to use quantum hardware for applications in the field of chemistry. IQM is pleased to have supplied the hardware on which this demonstration was run repeatedly, over multiple days, during this summer’s Quantum Korea event. Our commercial quantum system ran reliably and, together with Qunova’s advanced algorithm, demonstrated that we are now entering the era when quantum computing can deliver real value for users in the form of new business applications,” said Dr. Peter Eder, Head of Strategic Partnerships at IQM Quantum Computers.

    “At AQT, our aim is to solve challenges beyond classical computing capabilities, pushing boundaries to address business needs. Providing quantum hardware on which Qunova was able to achieve chemical accuracy is an excellent example of the kind of value we aim to deliver with our partners. The results from this experiment, using our 20-qubit trapped-ion system, show that Qunova’s solution is truly hardware agnostic, which is an impressive achievement. Through our cloud solution, ARNICA, we remain committed to accelerating quantum discovery and making this transformative technology readily available,” added Dr. Thomas Monz, CEO at AQT.

    Kevin Rhee at Quantum Korea
    Kevin Rhee, CEO and Founder of Qunova Computing, speaks with attendees at Quantum Korea in Seoul. Credit: Qunova Computing

    A New Era of Quantum Efficiency

    Unlike simulations done on classical computers using traditional Variational Quantum Eigensolvers (VQEs), which are not scalable, the Qunova solution functions on all types of quantum computers and provides computational accuracy sufficient to carry out advanced computations for chemistry. Meanwhile, VQEs run on quantum systems have thus far failed to achieve chemical accuracy. Qunova has achieved this using its new kind of simplified VQE, dubbed “HiVQE” or “Handover Iteration VQE”.

    The results show that using this HiVQE solution reduces the computational resources required to compute these problems by 1,000 times or more, when compared with traditional VQEs. Qunova therefore estimates that its algorithm has the potential to deliver a quantum advantage for chemical computations, over classical computers, using a NISQ machine with as few as 40-60 qubits.

    The key to this breakthrough was to develop a computational method without carrying over errors in the quantum computing procedure. “Pauli word measurements” were therefore removed from the traditional VQE algorithm to simplify problems and harvest only essential data related to the orbitals of each molecule. Then, those outcomes were fed into classical machines to calculate the result with the lowest energy very quickly, which allows chemical accuracy to be achieved. This also enabled the computations to run 1,000x more efficiently.

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