Algorithmiq Achieves Quantum Computing Breakthrough in Drug Discovery at IBM Summit
In Brief
Algorithmiq showcased groundbreaking error mitigation outcomes from a large-scale experiment conducted on IBM quantum hardware.
Algorithmiq recently presented impressive error mitigation results during a large-scale experiment conducted on IBM quantum hardware at the IBM Summit. The company, being a quantum algorithm scaleup, concentrates on tackling intricate challenges in life sciences, and demonstrated its expertise in attaining practical quantum utility.
Quantum utility is when a quantum computer achieves dependable computations surpassing the brute force capabilities of classical computing, for precise problem-solving. Traditionally, classical approximation methods were often tailored for specific problems, were the sole was only to addressing the particular challenge.
Utilizing quantum utility, computational scientists and other researchers can use quantum computers to tackle real world large-scale problems as well.
The experiment utilized Algorithmiq’s proprietary error mitigation algorithms on the IBM Nazca, specifically the 127 qubit Eagle processor, incorporating 50 active qubits and 98 layers of CNOTS, totaling 2402 CNOTS gates.
This collaboration with IBM was initiated in 2022, and aims to pave the way for the first practical quantum advantage in chemistry. Quantum computers despite their potential, grapple with high error rates, hindering large-scale calculations.
Algorithmiq’s proprietary Tensor Network Error Mitigation (TEM) techniques, employed in collaboration with IBM Zurich’s Ivano Tavernelli and Trinity College Dublin’s John Goold, achieved groundbreaking results. These techniques effectively mitigated noise even with increased circuit depth, surpassing traditional error mitigation methods.
“Today’s quantum computers are prone to errors. This means that when a simulation is run for a molecule, the properties we would infer would be biased. Error mitigation aims at correcting for those biases. TEM gives the best performance in terms of QC usage time because the error mitigation takes place in post processing,” Professor Sabrina Maniscalco, co-founder and CEO of Algorithmiq, told Metaverse Post. “In practice this means that we can correct for the errors in a fraction of the time that other reliable methods would require.”
Moreover, the TEM approach demonstrated a capability to recover quantum signals in challenging regimes, presenting substantial improvements in measurement overhead. This efficiency translates to significantly faster computations, reducing timescales from years to mere hours.
In June 2023, Algorithmiq raised €13.7 million in Series A round is led by Inventure VC, a Nordic venture fund. The funds were utilized to pursue its proof-of-concept work with pharmaceutical companies globally, aiming to reduce the time and cost of drug discovery and development.
The company’s recent experiments have now laid a groundwork for scalable quantum computation, marking a crucial step toward the fault-tolerance era for drug discovery.
“With the help of quantum chemistry experiments on a quantum computer going for the first time hand-in-hand with on-site clinical laboratory studies, this opens up the possibility to engineer precise chemical modifications of light-activated drug compounds that enhance their efficacies while minimizing (unwanted) side effects, ultimately maximizing patient treatment success rates,” Maniscalco explained.
Leveraging Quantum Computing for Chemistry Applications
Algorithmiq’s achievements extended beyond error mitigation, with CEO Sabrina Maniscalco presenting key results from a collaboration with AstraZeneca, IBM, and the Hartree Centre.
The team explored a novel approach to study proton transfer reactions, employing hardware-adapted fermion-to-qubit mapping and compilation algorithms. This approach significantly reduced quantum hardware requirements, offering up to a 54% reduction in the number of noisy operations.
“While the community is aimed at improving the size of the circuit that are executed on hardware (meaning number on CNOTs) it is important to be as efficient as possible in the construction of circuit because the fewer operations one needs to solve the problem the easier it gets to run them,” Algorithmiq’s Maniscalco told Metaverse Post. “Thus, at Algorithmiq, one of the main research lines is devoted to simplifying the circuit for Q chemistry simulation as much as possible.”
Moreover, Algorithmiq become the new owner of Qiskit Nature code, IBM’s highly curated quantum community for chemistry.
The development aligns with IBM’s broader changes to the Qiskit Ecosystem, encouraging external partners to take on maintenance responsibilities. Algorithmiq aims to develop software that empowers researchers and companies to tackle quantum simulation challenges in the natural sciences.
“We are very focused on the simulation of quantum chemistry (and later for drug discovery). Simulating quantum systems was the main motivation for quantum computing in first place, thus it is a very sensible use case for the devices even in the near term,” said Algorithmiq’s Maniscalco. “While many industries can benefit from enhance Q chemistry computational capabilities, our main target industry is drug discovery.”
Algorithmiq’s innovative measurement approach and advanced chemistry methods also recently secured a $4.25 million investment from Wellcome Leap. The funding will support the design of new photon-drug interactions for cancer prevention and treatment in collaboration with partners IBM and Cleveland Clinic.
“The IBM Summit experiment was only the first step to prove that TEM works for complex circuits. Now, we are thrilled to think about what we can do moving forward, especially with the news of the expected improvement in IBM devices,” Algorithmiq’s Maniscalco told Metaverse Post.
IBM recently unveiled a new quantum computing chip and machine with the aim of laying the groundwork for much larger systems by 2033. The company’s new Quantum System Two, incorporates three “Heron” chips — providing more than 1,000 qubits.
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Victor is a Managing Tech Editor/Writer at Metaverse Post and covers artificial intelligence, crypto, data science, metaverse and cybersecurity within the enterprise realm. He boasts half a decade of media and AI experience working at well-known media outlets such as VentureBeat, DatatechVibe and Analytics India Magazine. Being a Media Mentor at prestigious universities including the Oxford and USC and with a Master's degree in data science and analytics, Victor is deeply committed to staying abreast of emerging trends. He offers readers the latest and most insightful narratives from the Tech and Web3 landscape.
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Victor is a Managing Tech Editor/Writer at Metaverse Post and covers artificial intelligence, crypto, data science, metaverse and cybersecurity within the enterprise realm. He boasts half a decade of media and AI experience working at well-known media outlets such as VentureBeat, DatatechVibe and Analytics India Magazine. Being a Media Mentor at prestigious universities including the Oxford and USC and with a Master's degree in data science and analytics, Victor is deeply committed to staying abreast of emerging trends. He offers readers the latest and most insightful narratives from the Tech and Web3 landscape.
