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DTSTART;TZID=America/New_York:20241004T130000
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DTSTAMP:20260504T204300
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SUMMARY:Progress and Challenges in Quantum Algorithms for Quantum Chemistry: Hard Lessons from the Field
DESCRIPTION:Abstract\nOne of the standard arguments for building a quantum computer is that we might profitably use the entanglements between qubits to simulate the correlations between electrons and thus solve a myriad of important chemical and material design problems. While this is surely an elegant and effective one-line funding justification\, the devil is very much in the details. This talk will provide an overview of some significant progress made over the last five years in this area by both the field and in a few examples by the team I work with at QC Ware\, and will also discuss some serious challenges that remain for us to achieve practical quantum advantage for quantum chemistry. On the positive side\, some specific topics to be discussed are quantum number preserving gate fabrics that finally provide a clean mapping of electrons to qubits\, quantum symmetry adapted perturbation theory as a desperate riposte to the problem of subtractive cancellation in NISQ algorithms\, and quantum Krylov methods that might provide a compelling alternative to the variational quantum eigensolver. On the negative side\, we will look at the relentless progress of classical quantum chemistry methods for solving the average-case chemical problem\, the obvious difficulty of parameter optimization in variational quantum algorithms\, and the current state of performance of real hardware experiments. The resultant picture is that quantum chemistry remains a highly compelling target for practical quantum advantage\, but that urgent attention is needed to remove some dangerous remaining uncertainties in the field. \nSpeaker Bio\nRob Parrish leads the quantum chemistry technology mission at QC Ware. Rob has spent the bulk of his early career learning how to use many types of hardware to accelerate quantum chemistry codes\, including CPUs\, GPUs\, and even some forays into forthcoming QPUs. He has key interests in getting either more detailed or more complete information out of quantum chemistry codes\, as exemplified by his efforts to robustly decompose interaction energies with “F-SAPT” methodology or to provide complete workflows for experimental observables like spectroscopies and conformer distributions. Rob operates under the strong hypothesis that quantum chemistry is imminently due to provide the same digital transformation to chemistry that computational fluid dynamics did for aeronautical engineering\, and works daily to be a small part of that transformation.
URL:https://quantum.ncsu.edu/event/triangle-quantum-computing-seminar-series-talk-3/
LOCATION:Virtual
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DTSTART;TZID=America/New_York:20241011T130000
DTEND;TZID=America/New_York:20241011T140000
DTSTAMP:20260504T204300
CREATED:20241004T073730Z
LAST-MODIFIED:20241011T141459Z
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SUMMARY:Low-overhead fault tolerance for transversal quantum algorithms
DESCRIPTION:Abstract\nWe will discuss experimental and theoretical progress towards large-scale error-corrected quantum computation. First\, we report recent advances in quantum information processing using dynamically reconfigurable arrays of neutral atoms.  Using this logical processor with various types of error-correcting codes\, we demonstrate that we can improve logical two-qubit gates by increasing code distance\, create logical GHZ states\, and perform computationally complex quantum simulation of information scrambling. In performing such circuits\, we observe that the performance can be substantially improved by accounting for error propagation during transversal logical entangling gates and decoding the logical qubits jointly. We find that by using this correlated decoding technique and correctly handling feedforward operations\, the number of noisy syndrome extraction rounds in universal quantum computation can be reduced from O(d) to O(1)\, where d is the code distance. These techniques result in new theories of fault-tolerance and in practical reductions to the cost of large-scale computation by over an order of magnitude. \nSpeaker Bio\n\nMaddie Cain is a 6th year PhD student in theoretical physics working in Professor Mikhail Lukin’s group at Harvard University. Her research explores the theory of quantum information processing\, including topics spanning quantum algorithms and quantum error correction. She is interested in developing resource-efficient\, fault-tolerant compilations of useful quantum algorithms\, with a focus on reducing the overhead of error correction in hardware. She also closely collaborates with experimentalists in the Lukin group developing neutral atom arrays. \n 
URL:https://quantum.ncsu.edu/event/triangle-quantum-computing-seminar-series-talk-4/
LOCATION:Virtual
ORGANIZER;CN="IBM Quantum Innovation Center at NC State":MAILTO:quantumhelp@ncsu.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241018T130000
DTEND;TZID=America/New_York:20241018T140000
DTSTAMP:20260504T204300
CREATED:20241011T142837Z
LAST-MODIFIED:20241015T160049Z
UID:10000310-1729256400-1729260000@quantum.ncsu.edu
SUMMARY:Searching for Symmetry Violation with Molecular Ions
DESCRIPTION:Abstract\nMolecules have emerged as powerful instruments for conducting precise tests of fundamental symmetries\, such as the search for the electron electric dipole moment (eEDM). However\, molecules are generally difficult to trap and cool\, making it challenging to perform narrow linewidth measurements with long interrogation times without a complicated laser-cooling setup capable of repumping multiple rovibrational levels. Our approach harnesses molecular ions\, which offer distinct advantages. They can be readily stored in ion traps for prolonged durations and sympathetically cooled by laser-cooled atomic ions. High-fidelity coherent manipulations and state readout can be performed quantum logic control via co-trapped atomic ions. Notably\, atomic lutetium ions are amenable to direct laser cooling\, making them ideal candidates to serve as sympathetic coolants and to form pre-cooled molecular ions. Furthermore\, Lu-176 boasts one of the largest nuclear electric quadrupole moments of any long-lived isotope\, rendering it exceptionally sensitive to the CP-violating nuclear magnetic quadrupole moment (nMQM). I will describe how we can harness these properties in molecular ions containing lutetium\, such as LuOH⁺\, to probe new physics through the simultaneous investigation of nMQM and eEDM. \nSpeaker Bio\n\nDr. Matt Grau is an experimental AMO physicist at Old Dominion University. His research focused on using trapped atomic and molecular ions for quantum computing and tests of fundamental symmetries in the search for new physics beyond the Standard Model. Before joining ODU in Spring 2022\, he was a postdoctoral researcher and senior scientist with the trapped ion quantum information group at ETH Zürich. He received his B.S. in Physics from Caltech and his Ph.D. in Physics from the University of Colorado Boulder\, where his thesis focused on precision measurements of the electron’s electric dipole moment using trapped molecular ions. \n 
URL:https://quantum.ncsu.edu/event/triangle-quantum-computing-seminar-series-talk-5/
LOCATION:Hybrid
ORGANIZER;CN="IBM Quantum Innovation Center at NC State":MAILTO:quantumhelp@ncsu.edu
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DTSTART;TZID=America/New_York:20241025T130000
DTEND;TZID=America/New_York:20241025T140000
DTSTAMP:20260504T204300
CREATED:20241021T175702Z
LAST-MODIFIED:20241023T021622Z
UID:10000311-1729861200-1729864800@quantum.ncsu.edu
SUMMARY:Quantum Generative Models of Financial Time  Series
DESCRIPTION:Abstract\nOur research program investigates quantum models for the simulation\, classification\, and prediction of financial time series. Financial processes\, such as asset prices\, interest rates\, and forex rates\, exhibit complex dynamics on different time scales. These processes are partially observable\, and we identify the underlying internal dynamics through stochastic observations. \nA promising research direction involves using quantum Completely Positive Trace Preserving (CPTP) maps\, or quantum channels\, as generative models of partially observable stochastic systems. This approach is motivated by the dual nature of the channel’s operator-sum representation. The representation can be interpreted both as a description of unobservable quantum dynamics and as a Positive Operator-Valued Measure (POVM) measurement\, where the outcomes correspond to specific operators defining the quantum process. \nTo quantize classical linear stochastic models\, such as Hidden Markov Models (HMMs) or\, more broadly\, Observable Operators Models (OOMs)\, as quantum channels\, we map the stochastic vector space and observable operators of the classical model to the space of density matrices and the corresponding operators of the quantum channel. We refer to these models as “quantum stochastic generators.” \nWe demonstrate that quantum stochastic generators provide a more efficient description of discrete stochastic processes in terms of state-space complexity compared to classical models. Specifically\, it is shown that for every classical OOM of order \( n \)\, there exists a quantum stochastic generator of order \( N = \sqrt{n} \) which generates the same stochastic process. \nWe propose an efficient parameterization of quantum stochastic generators as unitary circuits with mid-circuit measurements. By utilizing the space of unitary circuits as a hypothesis space\, we establish a formal learning model for the quantum generators. It is demonstrated that the landscape of the proposed learning model is smooth and auto-correlated. Smooth landscapes enable the implementation of efficient heuristic and gradient-based learning algorithms. For instance\, we discuss a learning algorithm developed through hyperparameter-adaptive evolutionary search\, which is used for learning generative models of synthetic and real-life financial time series. \nAs a future research opportunity\, we present several quantum generator architectures that implicitly and explicitly model processes with long-term dependencies. Finally\, quantum hardware implementations of the discussed models are demonstrated. \nSpeaker Bios\n\n\n\n\n\n\n\n\nVanio Markov\nDistinguished Engineer and Managing Director\nWells Fargo – Advanced Technology \nVanio Markov has 25 years of experience engineering and building sophisticated technology products in the areas of Financial Data Management\, Data Science\, and Intelligent Decision Making. \nHe has successfully led complex\, multimillion-dollar engineering organizations\, programs\, and projects\, delivering critical business solutions by leveraging global distributed computing and machine intelligence. \nVanio possesses deep expertise in financial risk management\, asset management\, and related stochastic modeling and computational intelligence. \nHis professional background includes roles at prominent companies such as Oracle Corp\, the global hedge fund administrator Citco\, Citigroup\, and Wells Fargo NA. \nHe holds advanced degrees in Systems Engineering and Control\, Applied Mathematics\, and Computer Science. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr. Vladimir Rastunkov\nComputational Scientist\nIBM Quantum \nDr. Vladimir Rastunkov is a computational scientist at IBM Quantum\, currently focusing on practical applications of quantum machine learning in financial services. \nWith over 15 years of experience in advanced analytics and machine learning\, Vladimir leads consulting and joint development research engagements. \n\n\n\n\n\n\n 
URL:https://quantum.ncsu.edu/event/triangle-quantum-computing-seminar-series-talk-6/
LOCATION:Virtual
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