Casey Duckering

Casey Duckering

Casey Duckering

Casey Duckering

Casey Duckering

Computer Science PhD Candidate
University of Chicago
Quantum Computing

Casey Duckering

I am a PhD candidate in quantum computing advised by Prof. Fred Chong at the University of Chicago. My research aims to bring together quantum algorithms and quantum error correction with their physical implementations on NISQ quantum computers and beyond. Some of my work focuses on analyzing the benefits of accessing higher energy states of qubits for compiling quantum circuits to hardware and, most recently, exploring efficiently implementable architectures for quantum error correction.

Previously, I received my Bachelor degrees in Electrical Engineering and Computer Science (EECS) and Mechanical Engineering from the University of California Berkeley. At UC Berkeley, I studied robotics and embedded systems.

In my spare time, I enjoy getting outdoors, hiking, and playing soccer. I often contribute to open source projects and create computer-generated art.

Education

  • PhD Candidate in Computer Science, 2018–present
    University of Chicago
  • Master of Science in Computer Science, 2020
    University of Chicago
  • Bachelor of Science in Mechanical Engineering, 2016
    University of California, Berkeley
  • Bachelor of Science in Electrical Engineering and Computer Science (EECS), 2016
    University of California, Berkeley

Publications & Talks

  • [Paper+Talk] Virtualized Logical Qubits: A 2.5D Architecture for Error-Corrected Quantum Computing
    Casey Duckering, Jonathan M. Baker, David I. Schuster, Frederic T. Chong
    MICRO '20 • Proceedings of the 53rd IEEE/ACM International Symposium on Microarchitecture • October 2020
    Best Paper Runner-Up at MICRO 2020
    Abstract: Current, near-term quantum devices have shown great progress in recent years culminating with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to...
    Abstract: Current, near-term quantum devices have shown great progress in recent years culminating with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to transition to greater reliability through error correction, likely through promising techniques such as surface codes which are well suited for near-term devices with limited qubit connectivity. We discover quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5D architecture, can efficiently implement surface codes with substantial hardware savings and performance/fidelity gains. Specifically, we virtualize logical qubits by storing them in layers distributed across qubit memories connected to each transmon.
    Surprisingly, distributing each logical qubit across many memories has a minimal impact on fault tolerance and results in substantially more efficient operations. Our design permits fast transversal CNOT operations between logical qubits sharing the same physical address which are 6x faster than lattice surgery CNOTs. We develop a novel embedding which saves ~10x in transmons with another 2x from an additional optimization for compactness.
    Although Virtualized Logical Qubits (VLQ) pays a 10x penalty in serialization, advantages in the transversal CNOT and area efficiency result in performance comparable to 2D transmon-only architectures. Our simulations show fault tolerance comparable to 2D architectures while saving substantial hardware. Furthermore, VLQ can produce magic states 1.22x faster for a fixed number of transmon qubits. This is a critical benchmark for future fault-tolerant quantum computers as magic states are essential and machines will spend the majority of their resources continuously producing them. VLQ substantially reduces the hardware requirements for fault tolerance and puts within reach a proof-of-concept experimental demonstration of around 10 logical qubits, requiring only 11 transmons and 9 attached cavities in total.
  • [Talk] Quantum Circuit Optimization
    Casey Duckering, Frederic T. Chong
    STAQ Quantum Ideas Summer School • June 2020
    Abstract: An introduction to quantum circuit optimization with a hands-on demonstration and exercises in the online circuit simulator, Quirk.
    Abstract: An introduction to quantum circuit optimization with a hands-on demonstration and exercises in the online circuit simulator, Quirk.
  • [Upcoming] Improved Quantum Circuits via Intermediate Qutrits
    Jonathan M. Baker, Casey Duckering, Pranav Gokhale, Natalie C. Brown, Kenneth R. Brown, Frederic T. Chong
    ACM Transactions on Quantum Computing • 2020
    Abstract: Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant...
    Abstract: Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant factor improvements, owing to the log2(3) binary-to-ternary compression factor. We present a novel technique, intermediate qutrits, to achieve sublinear depth decompositions of the Generalized Toffoli and other arithmetic circuits using no additional ancilla—a significant improvement over linear depth for the best qubit-only equivalents. For example, our Generalized Toffoli construction features a 70x improvement in two-qudit gate count over a qubit-only decomposition. This results in circuit cost reductions for important algorithms like quantum neurons, Grover search, and even Shor's algorithm. Using a previously developed simulator with near-term noise models we demonstrate for these models over 90% mean reliability (fidelity) for the Toffoli construction, versus under 30% for the qubit-only baseline. For our other constructions, such as the Incrementer, the A + B adder and the +K adder we demonstrate the power of intermediate qutrits in producing asymptotic depth improvements with no additional ancilla. Together, these results suggest qutrits offer a promising path towards scaling quantum computation.
  • Resource-efficient quantum computing by breaking abstractions
    Yunong Shi, Pranav Gokhale, Prakash Murali, Jonathan M. Baker, Casey Duckering, Frederic T. Chong
    Proceedings of the IEEE • June 2020
    Abstract: Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to...
    Abstract: Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.
  • Time-Sliced Quantum Circuit Partitioning for Modular Architectures
    Jonathan M. Baker, Casey Duckering, Alexander Hoover, Frederic T. Chong
    CF '20 • Proceedings of the 17th ACM International Conference on Computing Frontiers • May 2020
    Abstract: Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and...
    Abstract: Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical machines one time slice at a time. Specifically, we create optimized mappings for each time slice, accounting for the cost to move data from the previous time slice and using a tunable lookahead scheme to reduce the cost to move to future time slices. We compare our approach to a traditional statically-mapped, owner-computes model. Our results show strict improvement over the static mapping baseline. We reduce the non-local communication overhead by 89.8% in the best case and by 60.9% on average. Our techniques, unlike many exact solver methods, are computationally tractable.
  • [Upcoming] Efficient Quantum Circuit Decompositions via Intermediate Qudits
    Jonathan M. Baker, Casey Duckering, Frederic T. Chong
    ISMVL '20 • Proceedings of the 50th International Symposium on Multiple-Valued Logic • May 2020
    Abstract: Many quantum algorithms make use of ancilla, additional qubits used to store temporary information during computation, to reduce the total execution time. Quantum computers will be...
    Abstract: Many quantum algorithms make use of ancilla, additional qubits used to store temporary information during computation, to reduce the total execution time. Quantum computers will be resource-constrained for years to come so reducing ancilla requirements is crucial. In this work, we give a method to generate ancilla out of idle qubits by placing some in higher-value states, called qudits. We show how to take a circuit with many O(n) ancilla and design an ancilla-free circuit with the same asymptotic depth. Using this, we give a circuit construction for an in-place adder and a constant adder both with O(log n) depth using temporary qudits and no ancilla.
  • Extending the Frontier of Quantum Computers with Qutrits
    Pranav Gokhale, Jonathan M. Baker, Casey Duckering, Natalie C. Brown, Kenneth R. Brown, Frederic T. Chong
    IEEE Micro Top Picks • April 2020
    Abstract: Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and...
    Abstract: Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical machines one time slice at a time. Specifically, we create optimized mappings for each time slice, accounting for the cost to move data from the previous time slice and using a tunable lookahead scheme to reduce the cost to move to future time slices. We compare our approach to a traditional statically-mapped, owner-computes model. Our results show strict improvement over the static mapping baseline. We reduce the non-local communication overhead by 89.8% in the best case and by 60.9% on average. Our techniques, unlike many exact solver methods, are computationally tractable.
  • [Talk] Virtualized Logical Qubits
    Casey Duckering, Jonathan M. Baker, David I. Schuster, Frederic T. Chong
    MS Presentation at the University of Chicago • April 2020
    Abstract: Current, near-term quantum devices have shown great progress in the last several years culminating recently with a demonstration of quantum supremacy. These devices, however, are extremely limited...
    Abstract: Current, near-term quantum devices have shown great progress in the last several years culminating recently with a demonstration of quantum supremacy. These devices, however, are extremely limited with prohibitively large error rates and therefore they have relatively few applications. Many of the most anticipated quantum algorithms such as Shor's and Grover's require fault tolerant logical qubits which are built from large numbers of noisy, physical qubits and errors are corrected via quantum error correction codes such as the surface code. While current work on NISQ-era devices is important, there is simultaneously a need to develop architectures for larger scale use of systems composed of error corrected logical qubits.
    In this work, we introduce an architecture matching a recent qubit memory technology with established error correction designed without memory in mind. We provide a new method for the virtualization of error-corrected, logical qubits implemented with surface code patches. Surface codes are promising error correction codes which only require physical qubits with local, nearest-neighbor connectivity which is a common feature among current leading superconducting quantum hardware. Traditionally, surface code patches were arranged on this two-dimensional grid. Recent hardware advances have demonstrated the ability to store qubits in the resonant modes of superconducting cavities attached to transmons and interactions between qubits in the cavity are mediated via the transmon. This memory-like technology enables a new 2.5D architecture which we demonstrate allows logical qubits to be stored and can be paged in and out of memory as needed, essentially virtualizing the logical qubits.
    We demonstrate how traditional representations of surface code patches can be implemented on our new system and show how operations in the lattice-surgery-based surface code translate to our system. Specifically, our system allows for transversal application of CNOT operations between logical qubits sharing the same set of transmons (same physical address) and can use either transversal or standard lattice surgery CNOTs between logical qubits of different physical addresses. These transversal CNOTs are 6x faster than standard lattice surgery CNOTs. Our system can achieve fault tolerance comparable to conventional two-dimensional grids while saving substantial hardware. Furthermore, our architecture can produce magic states at 1.22x the baseline rate given a fixed number of transmon qubits. This is a critical benchmark for future fault-tolerant quantum computers, as magic states are essential and machines will spend the majority of their resources continuously producing them. This architecture will reduce the hardware requirements for fault tolerant quantum computing and experimentalists should consider it for early experimental demonstrations.
  • Decomposing Quantum Generalized Toffoli with an Arbitrary Number of Ancilla
    Jonathan M. Baker, Casey Duckering, Alexander Hoover, Frederic T. Chong
    arXiv • April 2019
    Abstract: We present a general decomposition of the Generalized Toffoli, and for completeness, the multi-target gate using an arbitrary number of clean or dirty ancilla. While prior work has shown how to...
    Abstract: We present a general decomposition of the Generalized Toffoli, and for completeness, the multi-target gate using an arbitrary number of clean or dirty ancilla. While prior work has shown how to decompose the Generalized Toffoli using 0, 1, or O(n) many clean ancilla and 0, 1, and n − 2 dirty ancilla, we provide a generalized algorithm to bridge the gap, i.e. this work gives an algorithm to generate a decomposition for any number of clean or dirty ancilla. While it is hard to guarantee optimality, our decompositions guarantee a decrease in circuit depth as the number of ancilla increases.
  • Asymptotic Improvements to Quantum Circuits via Qutrits
    Pranav Gokhale, Jonathan M. Baker, Casey Duckering, Frederic T. Chong
    ISCA '19 • Proceedings of the 46th International Symposium on Computer Architecture • June 2019
    IEEE Micro Top Pick Award
    Abstract: Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant...
    Abstract: Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant factor improvements, owing to the log2(3) binary-to-ternary compression factor. We present a novel technique using qutrits to achieve a logarithmic depth (runtime) decomposition of the Generalized Toffoli gate using no ancilla—a significant improvement over linear depth for the best qubit-only equivalent. Our circuit construction also features a 70x improvement in two-qudit gate count over the qubit-only equivalent decomposition. This results in circuit cost reductions for important algorithms like quantum neurons and Grover search. We develop an open-source circuit simulator for qutrits, along with realistic near-term noise models which account for the cost of operating qutrits. Simulation results for these noise models indicate over 90% mean reliability (fidelity) for our circuit construction, versus under 30% for the qubit-only baseline. These results suggest that qutrits offer a promising path towards scaling quantum computation.
  • [Poster] Improved Quantum Circuits via Qutrits
    Pranav Gokhale, Casey Duckering, Jonathan M. Baker, Frederic T. Chong
    QIP '19 • 22nd Annual Conference on Quantum Information Processing • January 2019
    Best Poster Award
    Abstract: Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant factor...
    Abstract: Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant factor improvements, owing to the lg(3) binary-to-ternary compression factor. We present a novel technique using qutrits to achieve a logarithmic depth (runtime) decomposition of the Generalized Toffoli gate using no ancilla–a significant improvement over linear depth for the best qubit-only equivalent. Our circuit construction also features a 70x improvement in two-qudit gate count over the qubit-only equivalent decomposition. This results in circuit cost reductions for important algorithms like quantum neurons and Grover search. We develop an open-source circuit simulator for qutrits, along with realistic near-term noise models which account for the cost of operating qutrits. Simulation results for these noise models indicate over 90% mean reliability (fidelity) for our circuit construction, versus under 30% for the qubit-only baseline. These results suggest that qutrits offer a promising path towards scaling quantum computation.
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Miscellaneous

Open source projects
  • Cirq — Quantum computing library (contributor)
  • drawSvg — Vector drawing library (author and maintainer)
  • hyperbolic — Geometric construction and drawing library (author and maintainer)
  • bloch_sphere — Bloch sphere visualizations for teaching (author)

Contact

(Click to send mail) scrambled: < .@Eacccdghiiklmouuu>