Technical Papers Program

Technical Papers Scope and Goals

IEEE Quantum Week aims to be a leading venue for presenting high-quality original research, ground-breaking innovations, and compelling insights in quantum computing and engineering. Technical papers are peer-reviewed, can be on any topic related to quantum computing and engineering, and will be considered by one of the following technical paper tracks. 

Technical Paper Tracks

  • Quantum Computing & Systems (QCS)
  • Quantum Algorithms & Applications (QAA)
  • Quantum Networking & Communications (QNC)
  • Quantum Engineering, Devices & Sensing (QEDS)
  • Quantum Workforce & Society (QWS)

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Accepted Technical Papers

  • Each technical paper presentation is 30 minutes long and technical papers session are 90 minutes long.

QCE21 Technical Papers Overview

Mon, Oct 18 @ 10:45-12:15 — QAA-1: Quantum Algorithms & Applications 1

Normalized Gradient Descent for Variational Quantum Algorithms
Yudai Suzuki (Keio Univ.), Hiroshi Yano (Keio Univ.), Rudy Raymond (IBM Quantum), Naoki Yamamoto (Keio Univ.)
Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm
Xinwei Lee (Univ. of Tsukuba), Yoshiyuki Saito (Univ. of Aizu), Dongsheng Cai (Univ. of Tsukuba), Nobuyoshi Asai (Univ. of Aizu)
Quantum-inspired Algorithm for Vehicle Sharing Problem
Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau (Singapore Mgmt. Univ.)

Mon, Oct 18 @ 13:00-14:30 — QAA-2: Quantum Algorithms & Applications 2

Decision Diagrams for Quantum Measurements with Shallow Circuits
Stefan Hillmich (Johannes Kepler Univ. Linz), Charles Hadfield (IBM Quantum), Rudy Raymond (IBM Quantum), Antonia Mezzacapo (IBM Quantum), Robert Wille (Johannes Kepler Univ. Linz)
Multi-car Paint Shop Optimization with Quantum Annealing
Sheir Yarkoni, Alex Alekseyenko, Michael Streif, David Von Dollen, Florian Neukart (Volkswagen), Thomas Bäck (Leiden Univ.)
The Effect of Noise on the Performance of Variational Algorithms for Quantum Chemistry
Waheeda Saib (IBM Quantum), Petros Wallden (Univ. of Edinburgh), Ismail Akhalwaya (IBM Research)

Mon, Oct 18 @ 15:15-16:45 — QAA-3: Quantum Algorithms & Applications 3

A Simple Method for Sampling Random Clifford Operators
Ewout van den Berg (IBM Quantum)
PT-Enhanced Bayesian Parameter Estimation
Yaroslav Balytskyi, Manohar Raavi, Sang-Yoon Chang (Univ. Colorado, Colorado Springs)
QuGAN: A Quantum State Fidelity based Generative Adversarial Network
Samuel A. Stein (Pacific NW Natl. Lab), Betis Baheri (Kent State Univ.), Daniel Chen (Case Western Reserve Univ.), Ying Mao (Fordham Univ.), Qiang Guan (Kent State Univ.), Ang Li, Bo Feng (Pacific NW Natl. Lab), Shuai Xu (Case Western Reserve Univ.)

Tue, Oct 19 @ 10:45-12:15 — QCS-1: Quantum Computing & Systems 1

Sampling Strategy Optimization for Randomized Benchmarking
Toshinari Itoko, Rudy Raymond (IBM Quantum)
Hybrid Schrödinger-Feynman Simulation of Quantum Circuits With Decision Diagrams
Lukas Burgholzer, Hartwig Bauer, Robert Wille (Johannes Kepler Univ. Linz)
Sampling on NISQ Devices: "Who's the Fairest One of All?"
Elijah Pelofski, John Golden, Andreas Bärtschi, Daniel O'Malley, Stephan Eidenbenz (Los Alamos Natl. Lab)

Tue, Oct 19 @ 13:00-14:30 — QCS-2: Quantum Computing & Systems 2

A Simple Heuristic for Expressing a Truth Table as a Quadratic Pseudo-Boolean Function
Scott Pakin (Los Alamos Natl. Lab)
Resource Optimal Executable Quantum Circuit Generation Using Approximate Computing
Smaran Adarsh, Matthias Möller (Delft Univ. of Tech.)
QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis
Ed Younis (Lawrence Berkeley Natl. Lab), Koushik Sen (UC-Berkeley), Katherine Yelick (UC-Berkeley), Costin Iancu (LBNL)

Tue, Oct 19 @ 15:15-16:45 — QCS-3: Quantum Computing & Systems 3

A Quantum Computing Programming Language for Transparent Experiment Descriptions
Virginia Frey, Rich Rademacher, Elijah Durso-Sabina, Matthew Day, Noah Greenberg, Nikolay Videnov, Rajibul Islam, Crystal Senko (Univ. of Waterloo)
A MLIR Dialect for Quantum Assembly Languages
Alexander McCaskey, Thien Nguyen (Oak Ridge Natl. Lab)
Quantum Annealing Stencils with Applications to Fuel Loading of a Nuclear Reactor
Joseph Fustero, Scott Palmtag, Frank Mueller (NC State Univ.)

Tue, Oct 19 @ 10:45-12:15 — QWS-1: Quantum Workforce & Society 1

GraphStateVis: Interactive Visual Analysis of Qubit Graph States and their Stabilizer Groups
Matthias Miller (Univ. Konstanz), Daniel Miller (Heinrich-Heine-Univ. Düsseldorf)
Teaching Quantum Computing with an Interactive Textbook
James Wootton, Francis Harkins, Nick Bronn, Almudena Carrera Vazquez, Anna Phan, Abraham Asfaw (IBM Quantum)

Tue, Oct 19 @ 13:00-14:30 — QWS-2: Quantum Workforce & Society 2

IBM-HBCU Quantum Center: A Model for Industry Academic Partnerships for Advancing a Diverse, Quantum Aware Workforces
Kayla Lee (IBM), Thomas Searles (Howard Univ.)
Quantum Computing for Undergraduate Engineering Students: Report of an Experience
Rafael Sotelo, Laura Gatti (Univ. de Montevideo)

Wed, Oct 20 @ 10:45-12:15 — QAA-4: Quantum Algorithms & Applications 4

Modified Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics Event Classification
Eraraya Ricardo Muten (Inst. Teknologi Bandung), Togan Tlimakhov Yusuf (Ankara Univ.), Andrei Voicu Tomut (Babeş-Bolyai Univ.)
Quantum Image Representation on Clusters
Arijit Mandal (Visvesvaraya Natl. Instl of Technology), Shreya Banerjee , Prasanta K. Panigrahi (Indian Inst. of Science Ed. and Res.)
Optimizing Parameterized Quantum Circuits with Free-Axis Selection
Hiroshi Watanabe (Keio Univ.), Rudy Raymond (IBM Quantum), Yu-ya Ohnishi (JSR Corp.), Eriko Kaminishi (Keio Univ.), Michihiko Sugawara (Keio Univ.)

Wed, Oct 20 @ 13:00-14:30 — QAA-5: Quantum Algorithms & Applications 5

Simpler (Classical) and Faster (Quantum) Algorithms for Gibbs Partition Functions
Srinivasan Arunachalam, Vojtech Havlicek, Giacommo Nannicini, Kristan Temme, Pawel Wocjan (IBM Quantum)
Photonic Quantum Policy Learning in OpenAI Gym
Dániel Nagy (Wigner Res. Ctr for Physics), Zsolt Tabi (Ericsson), Péter Hága, Zsófia Kallus (Ericsson Res.) Zoltán Zimborás (Wigner Res. Ctr for Physics)
Towards a Quantum Modeling Approach to Reactive Agents
Abder Koukam, Abdeljalil Abbas-Turki, Vincent Hilaire, Yassine Ruichek (Univ. Bourgogne Franche-Comté, UTBM)

Wed, Oct 20 @ 15:15-16:45 — QAA-6: Quantum Algorithms & Applications 6

Threshold-Based Quantum Optimization
John Golden, Andreas Bärtschi, Dan O'Malley, Stephan Eidenbenz (Los Alamos Natl. Lab)
A Quantum-Inspired Classical Solver for k-Satisfiability Problems
S. Andrew Lanham, Brian La Cour (ARL, UT Austin)
Numerical Simulations of Noisy Variational Quantum Eigensolver Ansatz Circuits
Meenambika Gowrishankar (Univ. of Tennessee/ORNL), Daniel Claudino, Jerimiah Wright, Alex McCaskey, Thien Nguyen, Travis Humble (Oak Ridge Natl. Lab)

Wed, Oct 20 @ 13:00-14:30 — QEDS-1: Quantum Engineering, Devices, and Sensing 1

Practical Implications of SFQ-based Two-qubit Gates
Mohammad Reza Jokar, Richard Rines, Fred Chong (Univ. of Chicago)
Efficient Quantum Gate Discovery with Optimal Control
Paul Kairys (Univ. of Tennessee), Travis Humble (Oak Ridge Natl. Lab)
Adaptive Circuit Learning for Quantum Metrology
Ziqi Ma, Pranav Gokhale, Tian-Xing Zheng, Sisi Zhou, Xiaofei Yu, Liang Jiang, Peter Maurer, Fred Chong (Univ. of Chicago)

Wed, Oct 20 @ 15:15-16:45 — QEDS-2: Quantum Engineering, Devices, and Sensing 2

Quantum Engineering of Nitrogen Vacancy Diamond Magnetometers (Invited Presentation)
Danielle Braje (MIT Lincoln Laboratory)
Perspectives and Progress on Scaling Subsystems for Trapped-Ion Quantum Computing (Invited Presentation)
Steve Sanders (Honeywell Quantum Solutions)

Thu, Oct 21 @ 10:45-12:15 — QNC-1: Quantum Networking & Communications 1

Optically Active Nanophotonic Spin Systems in Diamond as Resource for Quantum Communication (Invited Presentation)
Tim Schröder (Humbold-Univ. zu Berlin)
Distance-Independent Entanglement Generation in a Quantum Network using Space-Time Multiplexed Greenberger–Horne–Zeilinger (GHZ) Measurements
Ashlesha Patil, Joshua I. Jacobson, Emily Van Milligen (Univ. of Arizona), Don Towsley (Univ. of Massachusetts, Amherst), Saikat Guha (Univ. of Arizona)

Thu, Oct 21 @ 13:00-14:30 — QNC-2: Quantum Networking & Communications 2

A Quantum-Walk Control Plane for Distributed Quantum Computing in Quantum Networks
Matheus Guedes de Andrade (Univ. of Massachusetts, Amherst), Saikat Guha (Univ. of Arizona), Don Towsley, Wenhan Dai (Univ. of Massachusetts, Amherst)
Distributing Graph States Across Quantum Networks
Alex Fischer, Don Towsley (Univ. of Massachusetts, Amherst)
Towards Optical Quantum Networks with Rare Earth Ions (Invited Presentation)
Andrei Faraon, Andrei Ruskuc, Chun Ju Wu, Jake Rochman, Tian Xie, Mi Lei, Rikuto Fukumori, Daniel Riedel (California Inst. of Technology)

Thu, Oct 21 @ 15:15-16:45 — QNC-3: Quantum Networking & Communications 3

Password Authentication Schemes on a Quantum Computer
Sherry Wang, Carlisle Adams, Anne Broadbent (Univ. of Ottawa)
Performance Analysis of the Quantum Safe Multivariate Polynomial Public Key Algorithm
Randy Kuang, Michel Barbeau (Quantropi)
Pseudo Quantum Random Number Generator with Quantum Permutation Pad
Randy Kuang, Dafu Lou, Alex He, Chris McKenzie, Michael Redding (Quantropi)

Fri, Oct 22 @ 10:45-12:15 — QNC-4: Quantum Networking & Communications 4

The Quantum Internet: A Communication Engineering Perspective (Invited Presentation)
Marcello Caleffi, Jessica Illiano, Seid Koudia, Angela Sara Cacciapuoti (Univ. of Naples Federico II)
Networking Trapped-Ion Quantum Computers (Invited Presentation)
Chris Ballance (Univ. of Oxford)
A Complete Quantum Circuit to Solve the Information Set Decoding Problem
Simone Perriello, Alessandro Barenghi, Gerardo Pelosi (Polytecnico di Milano)

Fri, Oct 22 @ 13:00-14:30 — QAA-7: Quantum Algorithms & Applications 7

Quantum Optimization Heuristics with an Application to Knapsack Problems
Wim van Dam (UC-Santa Barbara), Karim Eldefrawy, Nicholas Genise (SRI Intl.), Natalie Parham (Univ. of Waterloo)
Transferability of Optimal QAOA Parameters between Random Graphs
Alexey Galda (Univ. of Chicago), Danylo Lykov (Argonne Natl. Lab), Xiaoyuan Liu, Ilya Safro (Univ. of Delaware), Yuri Alexeev (Argonne Natl. Lab)
Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment
Samantha Koretsky (Univ. of Chicago), Pranav Gokhale (Super.tech), Jonathan Baker, Joshua Viszlai (Univ. of Chicago), Hanhao Zheng, Niroj Gurung, Ruan Burg, Aleksi Paaso (Commonwealth Edison), Amin Khodaei (Univ. of Denver), Rozhin Eskandarpour (Resilient Entanglement), Fred Chong (Univ. of Chicago)

Fri, Oct 22 @ 15:15-16:45 — QCS-4: Quantum Computing & Systems 4

Quantum Fan-out: Circuit Optimizations and Technology Modeling
Pranav Gokhale (Super.tech), Samantha Koretsky (Univ. of Chicago), Shilin Huang, Swarnadeep Majumder (Duke Univ.), Andrew Drucer (Univ. of Chicago), Kenneth Brown (Duke Univ.), Fred Chong (Univ. of Chicago)
Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries
Ruslan Shaydulin (Argonne Natl. Lab), Alexey Galda (Univ. of Chicago)
Adaptive Job and Resource Management for the Growing Quantum Cloud
Gokul Subramanian, Kaitlin Smith (Univ. of Chicago), Prakash Murali (Princeton Univ.), Fred Chong (Univ. of Chicago)


QCE21 Technical Paper Abstracts


Mon, Oct 18 @ 10:45-12:15 — QAA-1: Quantum Algorithms & Applications 1

Normalized Gradient Descent for Variational Quantum Algorithms
Yudai Suzuki (Keio Univ.), Hiroshi Yano (Keio Univ.), Rudy Raymond (IBM Quantum), Naoki Yamamoto (Keio Univ.)

Time: 10:45-11:15 Mountain Time (MDT) — UTC-6
Abstract: Variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classical optimizers such as gradient-based optimizers are utilized to adjust the parameters of the quantum circuit so that the objective function is minimized. However, they often suffer from the so-called vanishing gradient or barren plateau issue. On the other hand, the normalized gradient descent (NGD) method, which employs the normalized gradient vector to update the parameters, has been successfully utilized in several optimization problems. Here, we study the performance of the NGD methods in the optimization of VQAs for the first time. Our goal is two-fold. The first is to examine the effectiveness of NGD and its variants for overcoming the vanishing gradient problems. The second is to propose a new NGD that can attain the faster convergence than the ordinary NGD. We performed numerical simulations of these gradient-based optimizers in the context of quantum chemistry where VQAs are used to find the ground state of a given Hamiltonian. The results show the effective convergence property of the NGD methods in VQAs, compared to the relevant optimizers without normalization. Moreover, we make use of some normalized gradient vectors at the past iteration steps to propose the novel historical NGD that has a theoretical guarantee to accelerate the convergence speed, which is observed in the numerical experiments as well.

Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm
Xinwei Lee (Univ. of Tsukuba), Yoshiyuki Saito (Univ. of Aizu), Dongsheng Cai (Univ. of Tsukuba), Nobuyoshi Asai (Univ. of Aizu)

Time: 11:15-11:45 Mountain Time (MDT) — UTC-6
Abstract: The quantum approximate optimization algorithm (QAOA) has numerous promising applications in solving the combinatorial optimization problems on near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure. Its quantum part consists of a parameterized alternating operator ansatz, and its classical part consists of an optimization algorithm, which optimizes the parameters to maximize the expectation value of the problem Hamiltonian. This expectation value depends highly on the parameters. This implies that a set of good parameters leads to an accurate solution. However, at large circuit depth of QAOA, it is difficult to achieve global optimization due to the multiple occurrences of local minima or maxima. In this paper, we propose a parameters fixing strategy which gives high approximation ratio on average, even at large circuit depths, by initializing QAOA with the optimal parameters obtained from the previous depths. We test our strategy on the Max-cut problem of a certain classes
of graphs such as the 3-regular graphs and the Erdös-Rényi graphs.

Quantum-inspired Algorithm for Vehicle Sharing Problem
Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau (Singapore Mgmt. Univ.)

Time: 11:45-12:15 Mountain Time (MDT) — UTC-6
Abstract: Recent developments in quantum technologies have inspired a myriad of special-purpose hardwares tasked to solve optimization problems. In this paper, we explore the application of Fujitsu’s quantum-inspired CMOS-based Digital Annealer (DA) in solving constrained routing problems arising in transportation and logistics. More precisely, we study the vehicle sharing problem and show that DA as a QUBO solver can potentially fill the gap between two common methods: exact solvers like Cplex and heuristics. We benchmark the scalability and quality of solutions obtained by DA with Cplex and with a greedy heuristic. Our results show that DA is a general QUBO solver that is more robust than heuristics, and more scalable than Cplex. Our methodology and framework is also directly applicable to other quantum-inspired and fully quantum devices that are undergoing development.

Mon, Oct 18 @ 13:00-14:30 — QAA-2: Quantum Algorithms & Applications 2

Decision Diagrams for Quantum Measurements with Shallow Circuits
Stefan Hillmich (Johannes Kepler Univ. Linz), Charles Hadfield (IBM Quantum), Rudy Raymond (IBM Quantum), Antonia Mezzacapo (IBM Quantum), Robert Wille (Johannes Kepler Univ. Linz)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: We consider the problem of estimating quantum observables on a collection of qubits, given as a linear combination of Pauli operators, with shallow quantum circuits consisting of single-qubit rotations. We introduce estimators based on randomised measurements, which use decision diagrams to sample from probability distributions on measurement bases. This approach generalises previously known uniform and locally-biased randomised estimators. The decision diagrams are constructed given target quantum operators and can be optimised considering different strategies. We show numerically that the estimators introduced here can produce more precise estimates on some quantum chemistry Hamiltonians, compared to previously known randomised protocols and Pauli grouping methods.

Multi-car Paint Shop Optimization with Quantum Annealing
Sheir Yarkoni, Alex Alekseyenko, Michael Streif, David Von Dollen, Florian Neukart (Volkswagen), Thomas Bäck (Leiden Univ.)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the optimization is to minimize the number of color switches between cars in a paint shop queue during manufacturing, a known NP-hard problem. We distinguish between different sub-classes of paint shop problems, and show how to formulate the basic MCPS problem as an Ising model. The problem instances used in this study are generated using real-world data from a factory in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and Advantage quantum processors to other classical solvers and a hybrid quantum-classical algorithm offered by D-Wave Systems. We observe that the quantum processors are well-suited for smaller problems, and the hybrid algorithm for intermediate sizes. However, we find that the performance of these algorithms quickly approaches that of a simple greedy algorithm in the large size limit.

The Effect of Noise on the Performance of Variational Algorithms for Quantum Chemistry
Waheeda Saib (IBM Quantum), Petros Wallden (Univ. of Edinburgh), Ismail Akhalwaya (IBM Research)

Time: 14:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Variational quantum algorithms are suitable for use on Noisy Intermediate Scale Quantum (NISQ) devices. One of the most important use-cases is the quantum simulation of materials, using the variational quantum eigensolver (VQE). To optimize VQE performance, a suitable parameterized quantum circuit (ansatz) must be selected. We investigate a class of ansatz that incorporates knowledge of the quantum hardware, namely the hardware efficient ansatz. Since the performance of the hardware efficient ansatz is affected differently by noise, our goal is to see how noise changes which ansatz is the best for a chemistry problem. First, we study the effect of noise on the different hardware efficient ansatz by benchmarking and ranking the performance of each ansatz family on a chemistry application using VQE and by a recently established metric called “expressibility”. The results demonstrate the ranking of optimal circuits does not remain constant in the presence of noise. Second, we evaluate the validity of the expressibility measure, by performing a correlation study between expressibility and the performance of the circuits on a chemistry application using VQE. Our simulations reveal a weak correlation and therefore demonstrate that expressibility is not an adequate measure to quantify the effectiveness of parameterized quantum circuits for quantum chemistry. Third, we evaluate the effect of different quantum device noise models on the ordering of which ansatz family is best. Interestingly, we see that to decide which ansatz is optimal for use, one needs to consider the specific hardware used even within the same family of quantum hardware.

Mon, Oct 18 @ 15:15-16:45 — QAA-3: Quantum Algorithms & Applications 3

A Simple Method for Sampling Random Clifford Operators
Ewout van den Berg (IBM Quantum)

Time: 15:15-15:45 Mountain Time (MDT) — UTC-6
Abstract: We describe a simple algorithm for sampling n-qubit Clifford operators uniformly at random. The algorithm outputs the Clifford operators in the form of quantum circuits with at most 5n+2n^2 elementary gates and a maximum depth of O(n log(n)) on fully connected topologies. The circuit can be output in a streaming fashion as the algorithm proceeds, and different parts of the circuit can be generated in parallel. The algorithm has an O(n^2) time complexity, which matches the current state of the art. The main advantage of the proposed algorithm, however, lies in its simplicity and elementary derivation.

PT-Enhanced Bayesian Parameter Estimation
Yaroslav Balytskyi, Manohar Raavi, Sang-Yoon Chang (Univ. Colorado, Colorado Springs)

Time: 15:45-16:15 Mountain Time (MDT) — UTC-6
Abstract: The frequentist (local) quantum parameter estimation based on the Cramer-Rao bound is an asymptotic theory and therefore requires an infinite number of samples for the measurements. On the other hand, by introducing the prior probability distribution, Bayesian parameter estimation approach relaxes this requirement and can be successfully applied under realistic assumption of possessing a limited prior information about the parameter value under investigation. Meanwhile, recent advances in PT-symmetric quantum mechanics, both theoretically and experimentally, provide an opportunity to significantly enhance the parameter estimation precision by manipulating the Hilbert space of the system. Taking advantage of these, we develop the Bayesian parameter estimation approach for the two-level system possessing PT symmetry. We consider both options available in PT-symmetric quantum mechanics, namely the CPT and Hermitian measurements, for encoding the probe state and manipulation of the quantum Fisher information of the system and take into account possible decoherence effects inherent for the practical implementations. Our approach should be particularly relevant for applications where the accuracy of quantum parameter estimation is crucial, such as quantum randomness sources and the quantum key distribution. Finally, to bridge the gap with the practical implementations, we consider the relation of our scheme with the current implementation of PT symmetry on the IBM quantum processor.

QuGAN: A Quantum State Fidelity based Generative Adversarial Network
Samuel A. Stein (Pacific NW Natl. Lab), Betis Baheri (Kent State Univ.), Daniel Chen (Case Western Reserve Univ.), Ying Mao (Fordham Univ.), Qiang Guan (Kent State Univ.), Ang Li, Bo Feng (Pacific NW Natl. Lab), Shuai Xu (Case Western Reserve Univ.)

Time: 16:15-16:45 Mountain Time (MDT) — UTC-6
Abstract: Tremendous progress has been witnessed in artificial intelligence, where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-states based gradients and significantly reduced parameter sets. The QuGANarchitecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, addition-ally, QuGAN outperforms state-of-the-art quantum based GANsin the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets.

Tue, Oct 19 @ 10:45-12:15 — QCS-1: Quantum Computing & Systems 1​

Sampling Strategy Optimization for Randomized Benchmarking
Toshinari Itoko, Rudy Raymond (IBM Quantum)

Time: 10:45-11:15 Mountain Time (MDT) — UTC-6
Abstract: Randomized benchmarking (RB) is a widely used method for estimating the average fidelity of gates implemented on a quantum computing device. The stochastic error of the average gate fidelity estimated by RB depends on the sampling strategy (i.e., how to sample sequences to be run in the protocol). The sampling strategy is determined by a set of configurable parameters (an RB configuration) that includes Clifford lengths (a list of the number of independent Clifford gates in a sequence) and the number of sequences for each Clifford length. The RB configuration is often chosen heuristically and there has been little research on its best configuration. Therefore, we propose a method for fully optimizing an RB configuration so that the confidence interval of the estimated fidelity is minimized while not increasing the total execution time of sequences. By experiments on real devices, we demonstrate the efficacy of the optimization method against heuristic selection in reducing the variance of the estimated fidelity.

Hybrid Schrödinger-Feynman Simulation of Quantum Circuits With Decision DiagramsLukas Burgholzer, Hartwig Bauer, Robert Wille (Johannes Kepler Univ. Linz)

Time: 11:15-11:45 Mountain Time (MDT) — UTC-6
Abstract: Classical simulations of quantum computations are vital for the future development of this emerging technology. To this end, decision diagrams have been proposed as a complementary technique which frequently allows to tackle the inherent exponential complexity of these simulations. In the worst case, however, they still cannot escape this complexity. Additionally, while other techniques make use of all the available processing power, decision diagram-based simulation to date cannot exploit the many processing units of today’s systems. In this work, we show that both problems can be tackled together by employing a hybrid Schrödinger-Feynman scheme for the simulation. More precisely, we show that realizing such a scheme with decision diagrams is indeed possible, we discuss the resulting problems in its realization, and propose solutions how they can be handled. Experimental evaluations confirm that this significantly advances the state of the art in decision diagram-based simulation—allowing to simulate certain hard circuits within minutes that could not be simulated in a whole day thus far.

Sampling on NISQ Devices: "Who's the Fairest One of All?"
Elijah Pelofski, John Golden, Andreas Bärtschi, Daniel O'Malley, Stephan Eidenbenz (Los Alamos Natl. Lab)

Time: 11:45-12:15 Mountain Time (MDT) — UTC-6
Abstract: Modern NISQ devices are subject to a variety of biases and sources of noise that degrade the solution quality of computations carried out on these devices. A natural question that arises in the NISQ era, is how fairly do these devices sample ground state solutions. To this end, we run five fair sampling problems (each with at least three ground state solutions) that are based both on quantum annealing and on the Grover Mixer-QAOA algorithm for gate-based NISQ hardware. In particular, we use seven IBMQ devices, the Aspen-9 Rigetti device, the IonQ device, and three D-Wave quantum annealers. For each of the fair sampling problems, we measure the ground state probability, the relative fairness of the frequency of each ground state solution with respect to the other ground state solutions, and the aggregate error as given by each hardware provider. Overall, our results show that NISQ devices do not achieve fair sampling yet. We also observe differences in the software stack with a particular focus on compilation techniques that illustrate what work will still need to be done to achieve a seamless integration of frontend (i.e. quantum circuit description) and backend compilation.

Tue, Oct 19 @ 13:00-14:30 — QCS-2: Quantum Computing & Systems 2

A Simple Heuristic for Expressing a Truth Table as a Quadratic Pseudo-Boolean Function
Scott Pakin (Los Alamos Natl. Lab)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: Quadratic pseudo-Boolean functions (i.e., quadratic functions with real-valued coefficients and two-valued variables) are the native input to quantum annealers and also a common problem format to optimize with the QAOA algorithm on a circuit-model quantum computer. In both cases, large, complex optimization problems can be expressed in terms of combinations of simpler problems. A key challenge in problem set-up lies in finding the coefficients for the constituent sub-problems. These need to be chosen such that the function is minimized on valid inputs and is higher elsewhere. One difficulty is that it is not possible in general to solve for a sub-problem’s coefficients without introducing ancillary variables, the number of which and their corresponding per-row values being unknown a priori. These unknowns lead to solving for a sub-problem’s coefficients being an NP-complete problem. In this paper we present a simple heuristic that considers a prioritized subset of the exponential number of possibilities for each per-row ancillary variable. Using this heuristic, coefficients can be found substantially faster than would be possible using a brute-force search and with less software complexity than is required by prior approaches.

Resource Optimal Executable Quantum Circuit Generation Using Approximate Computing
Smaran Adarsh, Matthias Möller (Delft Univ. of Tech.)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: Quantum Computing is an emerging technology that combines the principles of computer science and quantum mechanics to solve computationally challenging problems significantly faster than classical computers. In this paper, we present a proof-of-principle procedure for generating hardware-executable quantum circuits for Noisy Intermediate-Scale Quantum (NISQ) devices that follows the paradigm of approximate computing. Our approach starts from the reference circuit and transforms it into an executable circuit with tuneable parameters by replacing the high-level quantum operations by approximate decompositions into hardware-native gates. An inner optimization loop over the gates’ rotation angles ensures that the so-created circuit behaves in the same way as the reference one in terms of its expectation-value landscape. This technique is complemented by compiler-based optimizations to further reduce or aggregate gate groups of the optimized circuit. This three-step procedure is embedded into an outer genetic algorithm framework that inspects many different circuit designs with placements of single- and multi-qubit gates according to the hardware’s lattice structure, and returns a set of approximate quantum circuits that can be executed on NISQ devices directly. We have validated our approach for superconducting quantum systems from IBM and Rigetti for various benchmark algorithms. In nearly all cases, our approach outperforms the vendors’ quantum-compiler frameworks and produces significantly smaller circuits with up to 50% reduction in the number of gates.

QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis
Ed Younis (Lawrence Berkeley Natl. Lab), Koushik Sen (UC-Berkeley), Katherine Yelick (UC-Berkeley), Costin Iancu (LBNL)

Time: 14:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: We present a topology aware quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encode placement and topology using generic “gates”, which allows the QFAST algorithm to replace expensive searches over circuit structures with few steps of numerical optimization. When compared against optimal depth, search based state-of-the-art techniques, QFAST produces comparable results: 1.19× longer circuits up to four qubits, with an increase in compilation speed of 3.6×. In addition, QFAST scales up to seven qubits. When compared with the state-of-the-art “rule” based decomposition techniques in Qiskit, QFAST produces circuits shorter by up to two orders of magnitude (331×), albeit 5.6× slower. We also demonstrate the composability with other techniques and the tunability of our formulation in terms of circuit depth and running time.

Tue, Oct 19 @ 15:15-16:45 — QCS-3: Quantum Computing & Systems 3

A Quantum Computing Programming Language for Transparent Experiment Descriptions
Virginia Frey, Rich Rademacher, Elijah Durso-Sabina, Matthew Day, Noah Greenberg, Nikolay Videnov, Rajibul Islam, Crystal Senko (Univ. of Waterloo)

Time: 15:15-15:45 Mountain Time (MDT) — UTC-6
Abstract: We present a new quantum programming language that enables true full-stack programming of quantum hardware. Our language allows seamless integration of abstraction layers such as the digital circuit layer and the analog control pulse waveform layer. Additionally, our language supports user-issued low-level hardware instructions like FPGA actions. Mid-circuit measurements and branching decision logic support real-time, adaptive programs. This flexibility allows users to write code for everything from quantum error correction all the way to analog quantum simulation. The combination of a user-facing calibration database and a powerful symbolic algebra framework provides users with an unprecedented level of expressiveness and transparency. We display the salient characteristics of the language structure and describe how the accompanying compiler can translate programs written in any abstraction layer into precisely timed hardware commands. We intend for this language to bridge the gap between circuit-level programming and physical operations on real hardware while maintaining full transparency in each level of the stack. This eliminates the need for “behind-the-scenes” compilation and provides users with insights into the day-to-day calibration routines.

A MLIR Dialect for Quantum Assembly Languages
Alexander McCaskey, Thien Nguyen (Oak Ridge Natl. Lab)

Time: 15:45-16:15 Mountain Time (MDT) — UTC-6
Abstract: We demonstrate the utility of the Multi-Level Intermediate Representation (MLIR) for quantum computing. Specifically, we extend MLIR with a new quantum dialect that enables the expression and compilation of common quantum assembly languages. The true utility of this dialect is in its ability to be lowered to the LLVM intermediate representation (IR) in a manner that is adherent to the quantum intermediate representation (QIR) specification recently proposed by Microsoft. We leverage a qcor-enabled implementation of the QIR quantum runtime API to enable a retargetable (quantum hardware agnostic) compiler workflow mapping quantum languages to hybrid quantum-classical binary executables and object code. We evaluate and demonstrate this novel compiler workflow with quantum programs written in OpenQASM 2.0. We provide concrete examples detailing the generation of MLIR from OpenQASM source files, the lowering process from MLIR to LLVM IR, and ultimately the generation of executable binaries targeting available quantum processors.

Quantum Annealing Stencils with Applications to Fuel Loading of a Nuclear Reactor
Joseph Fustero, Scott Palmtag, Frank Mueller (NC State Univ.)

Time: 16:15-16:45 Mountain Time (MDT) — UTC-6
Abstract: A method for mapping quadratic unconstrained binary optimizations expressed as nearest neighbor stencils onto contemporary quantum annealing machines is developed. The method is shown to provide higher utilization of annealing hardware resources than prior work. Applying the technique to the problem of determining an effective fuel loading pattern for nuclear reactors shows that densely mapped quantum stencils result in higher fidelity solutions of optimization problems then the sparser default solutions. These results are likely to generalize to quadratic unconstrained binary optimizations that can be expressed as dense quantum stencils, thereby improving optimization results obtained from noisy quantum devices.

Tue, Oct 19 @ 10:45-12:15 — QWS-1: Quantum Workforce & Society 1​

GraphStateVis: Interactive Visual Analysis of Qubit Graph States and their Stabilizer Groups
Matthias Miller (Univ. Konstanz), Daniel Miller (Heinrich-Heine-Univ. Düsseldorf)

Time: 10:45-11:15 Mountain Time (MDT) — UTC-6
Abstract: Fathoming out quantum state space is a challenging endeavor due to its exponentially growing dimensionality. At the expense of being bound in its expressiveness, the discrete and finite subspace of graph states is easier to investigate via a pictorial framework accompanied with a theoretical toolkit from the stabilizer formalism. Analyzing hand-drawn graphs is a tedious and time-consuming task and imposes limitations to the problem sizes that can be addressed. Similarly, algorithmic studies using adjacency matrices alone lack the benefit of a visual representation of the states. We argue that applying visual analytics to investigate graph states can be advantageous. To this end, we introduce GraphStateVis, a web-based application for the visual analysis of qubit graph states and their stabilizer groups. Our tool facilitates the interactive construction of a graph through multiple components supported by linking and brushing. The user can explore graph-state-specific properties, including the Pauli-weight distribution of its stabilizer operators and noise thresholds for entanglement criteria. We propose a use case in the context of near-term quantum algorithms to illustrate the capabilities of our prototype. We provide access to GraphStateVis as an open-source project and invite the broader quantum computing and engineering communities to take advantage of this tool and further boost its development.

Teaching Quantum Computing with an Interactive Textbook
James Wootton, Francis Harkins, Nick Bronn, Almudena Carrera Vazquez, Anna Phan, Abraham Asfaw (IBM Quantum)

Time: 11:15-11:45 Mountain Time (MDT) — UTC-6
Abstract: Quantum computing is a technology that promises to offer significant advantages during the coming decades. Though the technology is still in a prototype stage, the last few years have seen many of these prototype devices become accessible to the public. This has been accompanied by the open-source development of the software required to use and test quantum hardware in increasingly sophisticated ways. Such tools provide new education opportunities, not just for quantum computing specifically, but also more broadly for quantum information science and even quantum physics as a whole. In this paper we present a case study of one education resource which aims to take advantage of the opportunities: the open-source online textbook `Learn Quantum Computation using Qiskit’. An overview of the topics covered is given, as well as an explanation of the approach taken for each.

Tue, Oct 19 @ 13:00-14:30 — QWS-2: Quantum Workforce & Society 2​

IBM-HBCU Quantum Center: A Model for Industry Academic Partnerships for Advancing a Diverse, Quantum Aware Workforces
Kayla Lee (IBM), Thomas Searles (Howard Univ.)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: The IBM-HBCU Quantum Center is a first-of-a-kind collaboration between IBM and a consortium of Historically Black Colleges and Universities (HBCUs) that seeks to address the lack of representation and build a diverse and aware workforce in quantum information science and engineering (QISE). Key pillars of the Center are focused on 1) building community and fostering a sense of belonging, 2) strengthening relationships internally and with the broader quantum community, and 3) providing funding to support undergraduate, graduate, and faculty research. As a part of the program, students and faculty are invited to participate in grant development workshops, a QISE invited speaker series, community hack-a-thons, and other opportunities to build competencies in the growing field of QISE. Since its launch, the IBM-HBCU Quantum Center has engaged a community of over 400 students, faculty, and researchers and will continue to establish a research presence in QISE and increase opportunities for research and workforce development.

Quantum Computing for Undergraduate Engineering Students: Report of an Experience
Rafael Sotelo, Laura Gatti (Univ. de Montevideo)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: This paper presents the experience of a Quantum Computer course addressed for the first time for undergraduate engineering students. Students’ levels varied from year two to five of their careers, and had strong previous knowledge on Calculus, Linear Algebra and Programming, but not on Quantum Mechanics or on Modern Physics. The main objective of the course was that students acquire programming skills in Quantum Computing, so it had a practical approach. Instead of beginning by introducing the physical phenomenology under the field, the course started directly with presenting the logic of quantum computing from an abstract point of view. The language used was Q#. The curricula was based on the one available for the Microsoft Quantum Network.

Wed, Oct 20 @ 10:45-12:15 — QAA-4: Quantum Algorithms & Applications 4​

Modified Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics Event Classification
Eraraya Ricardo Muten (Inst. Teknologi Bandung), Togan Tlimakhov Yusuf (Ankara Univ.), Andrei Voicu Tomut (Babeş-Bolyai Univ.)

Time: 10:45-11:15 Mountain Time (MDT) — UTC-6
Abstract: This paper aims to demonstrate the use of modified layerwise learning on a data-reuploading classifier, where the parameterized quantum circuit will be used as a quantum classifier to classify the SUSY dataset. We managed to produce a better result using this approach compared to the previous related research with fewer qubits. We obtained an AUC of 0.849 on a testing dataset with 5000 training and testing samples, trained and tested using a state-vector simulator. We also tested to run the circuit on Rigetti’s Aspen-9 quantum processing unit provided by AWS using the already optimized parameter to predict 2000 samples of the test dataset and obtained an AUC of 0.830.

Quantum Image Representation on Clusters
Arijit Mandal (Visvesvaraya Natl. Instl of Technology), Shreya Banerjee , Prasanta K. Panigrahi (Indian Inst. of Science Ed. and Res.)

Time: 11:15-11:45 Mountain Time (MDT) — UTC-6
Abstract: We propose a novel scheme for quantum image representation using the cluster states (QIRC). This scheme is capable to represent images of any size and color. We emphasize the use of cluster states in this scheme due to its high entanglement and various experimental realization across several hardware. We also propose a secure image encryption scheme embedded with QIRC. Further, we discuss the possibility of QIRC to be implemented successfully with a real quantum device in the NISQ era, by executing example representations of a 2×2 and a 3x2binary image on a real quantum processor provided by IBM Quantum.

Optimizing Parameterized Quantum Circuits with Free-Axis Selection
Hiroshi Watanabe (Keio Univ.), Rudy Raymond (IBM Quantum), Yu-ya Ohnishi (JSR Corp.), Eriko Kaminishi (Keio Univ.), Michihiko Sugawara (Keio Univ.)

Time: 11:45-12:15 Mountain Time (MDT) — UTC-6
Abstract: Parameterized quantum circuits (PQCs), which are essential for variational quantum algorithms, have conventionally been optimized by parameterized rotational angles of single-qubit gates around a predetermined set of axes. We propose a new method to optimize a PQC by continuous parameterization of both the angles and the axes of its single-qubit rotations. The method is based on the observation that when rotational angles are fixed, optimal axes of rotations can be computed by solving a system of linear equations whose coefficients can be determined from the PQC with a small computational overhead. The method can be further simplified to select axes freely from continuous parameters with rotational angles fixed to pi. We show the simplified free-axis selection method has better expressibility against other structural optimization methods when measured with Kullback-Leibler (KL) divergence. We also demonstrate PQCs with free-axis selection are more effective to search the ground states of Heisenberg models and molecular Hamiltonians. Because free-axis selection allows designing PQCs without specifying their single-qubit rotational axes, it may significantly improve the handiness of PQCs.

Wed, Oct 20 @ 13:00-14:30 — QAA-5: Quantum Algorithms & Applications 5​

Simpler (Classical) and Faster (Quantum) Algorithms for Gibbs Partition Functions
Srinivasan Arunachalam, Vojtech Havlicek, Giacommo Nannicini, Kritan Temme, Pawel Wocjan (IBM Quantum)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: We give classical and quantum algorithms for approximating partition functions of classical Hamiltonians at a given temperature. Specifically, we modify the classical algorithm of Stefankovic, Vempala and Vigoda (J. ACM, 56(3), 2009) to improve its sample complexity; and we quantize this new algorithm, improving upon the previously best quantum algorithm for computing Gibbs partition functions due to Harrow and Wei (SODA 2020). The conventional approach to estimate partition functions requires approximating the mean of Gibbs distributions at nearby inverse temperatures that satisfy certain properties; this set of temperatures is called a cooling schedule. The length of the cooling schedule directly affects the complexity of the algorithm. Combining our improved version of the algorithm of Stefankovic, Vempala and Vigoda with the paired-product estimator of Huber (Ann. Appl. Probab., 25(2), 2015), our new quantum algorithm uses a shorter cooling schedule than previously known. This length matches the optimal length conjectured by Stefankovic, Vempala and Vigoda. The quantum algorithm also achieves a quadratic advantage in the number of required quantum samples compared to the number of random samples drawn by the best classical algorithm, and its computational complexity has quadratically better dependence on the spectral gap of the Markov chains used to produce the quantum samples.

Photonic Quantum Policy Learning in OpenAI Gym
Dániel Nagy (Wigner Res. Ctr for Physics), Zsolt Tabi (Ericsson), Péter Hága, Zsófia Kallus (Ericsson Res.) Zoltán Zimborás (Wigner Res. Ctr for Physics)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: In recent years, near-term noisy intermediate scale quantum (NISQ) computing devices have become available. One of the most promising application areas to leverage such NISQ quantum computer prototypes is quantum machine learning. While quantum neural networks are widely studied for supervised learning, quantum reinforcement learning is still just an emerging field of this area. To solve a classical continuous control problem, we used continuous-variable quantum machine learning. We introduce proximal policy optimization for photonic variational quantum agent and also study the effect of the data re-uploading. We present performance assessment via empirical study using Strawberry Fields photonic simulator Fock backend and a hybrid training framework connected to an OpenAI Gym environment and TensorFlow. For the restricted CartPole problem, the two variations of the photonic policy learning achieve comparable performance levels and a faster convergence than the baseline classical neural network of same number of trainable parameters.
Time: 14:00-14:30 Mountain Time (MDT) — UTC-6

Towards a Quantum Modeling Approach to Reactive Agents
Abder Koukam, Abdeljalil Abbas-Turki, Vincent Hilaire, Yassine Ruichek (Univ. Bourgogne Franche-Comté, UTBM)

Abstract: Quantum computing offers a new approach to the problem modeling and solving. This paper deals with the quantum modeling of reactive agents. It also proposes a quantum algorithm to implement the subsumption architecture, widely used by reactive agents, particularly in robotics. This work shows the contribution of the formalism proposed by quantum mechanics to the modeling and the proof of certain properties of the agent behavior. After, the definition of the reactive agent state modeling, the paper suggests a behavior modeling approach based on two steps for subsumption architecture. The first one models the preset behavior that links each action to the perception states. The second one determines among several actuated actions, the one that the robot must achieve. The subsumption architecture raises the challenge of modeling hierarchical priority of actions. To this end, a multipartite entanglement is used in the second step. More precisely, the paper proposes and generalizes a W-state circuit in order to be used for modeling hierarchical priority actions and controlling the robot accordingly. The result of both steps provides a formal model that links the robot’s perception (input) to the actions (output), with respect to the subsumption architecture. The proposed model of agent is simulated using IBM quantum computer. The simulation shows that the model can either be served as a control unit of the robot (CU) to obtain the suitable action or to simulate the robot behavior.

Wed, Oct 20 @ 15:15-16:45 — QAA-6: Quantum Algorithms & Applications 6​

Threshold-Based Quantum Optimization
John Golden, Andreas Bärtschi, Dan O'Malley, Stephan Eidenbenz (Los Alamos Natl. Lab)

Time: 15:15-15:45 Mountain Time (MDT) — UTC-6
Abstract: We propose and study Th-QAOA (pronounced Threshold QAOA), a variation of the Quantum Alternating Operator Ansatz (QAOA) that replaces the standard phase separator operator, which encodes the objective function, with a threshold function that returns a value 1 for solutions with an objective value above the threshold and a 0 otherwise. We vary the threshold value to arrive at a quantum optimization algorithm. We focus on a combination with the Grover Mixer operator; the resulting GM-Th-QAOA can be viewed as a generalization of Grover’s quantum search algorithm and its minimum/maximum finding cousin to approximate optimization. Our main findings include: (i) we show semi-formally that the optimum parameter values of GM-Th-QAOA (angles and threshold value) can be found with O(log p * log M) iterations of the classical outer loop, where p is the number of QAOA rounds and M is an upper bound on the solution value (often the number of vertices or edges in an input graph), thus eliminating the notorious outer-loop parameter finding issue of other QAOA algorithms; (ii) GM-Th-QAOA can be simulated classically with little effort up to 90 qubits through a set of tricks that cut down memory requirements; (iii) somewhat surprisingly, GM-Th-QAOA outperforms its non-thresholded counterparts in terms of approximation ratios achieved. This third result holds across a range of optimization problems (MaxCut, Max k-VertexCover, Max k-DensestSubgraph, MaxBisection) and various experimental design parameters, such as different input edge densities and constraint sizes.

A Quantum-Inspired Classical Solver for k-Satisfiability Problems
S. Andrew Lanham, Brian La Cour (ARL, UT Austin)

Time: 15:45-16:15 Mountain Time (MDT) — UTC-6
Abstract: We detail a classical algorithmic approach to the k-satisfiability (k-SAT) problem that is inspired by the quantum amplitude amplification algorithm. This work falls under the emerging field of quantum-inspired classical algorithms. To propose our modification, we adopt an existing problem model for k-SAT known as Universal SAT (UniSAT), which casts the Boolean satisfiability problem as a non-convex global optimization over a real-valued space. The quantum-inspired modification to UniSAT is to apply a conditioning operation to the objective function that has the effect of “amplifying” the function value at points corresponding to optimal solutions. We describe the algorithm for achieving this amplification, termed “AmplifySAT,” which follows a familiar two-step process of applying an oracle-like operation followed by a reflection about the average. We then discuss opportunities for meaningfully leveraging this processing in a classical digital or analog computing setting, attempting to identify the strengths and limitations of AmplifySAT in the context of existing non-convex optimization strategies like simulated annealing and gradient descent.

Numerical Simulations of Noisy Variational Quantum Eigensolver Ansatz Circuits
Meenambika Gowrishankar (Univ. of Tennessee/ORNL), Daniel Claudino, Jerimiah Wright, Alex McCaskey, Thien Nguyen, Travis Humble (Oak Ridge Natl. Lab)

Time: 16:15-16:45 Mountain Time (MDT) — UTC-6
Abstract: This is a case study of the variational quantum eigensolver (VQE) method using numerical simulations to test the influence of noise on the accuracy of the underlying circuit ansatz. We investigate a computational chemistry application of VQE to calculate the electronic ground state and its energy for Sodium Hydride (NaH), a prototypical two-electron problem. Using a one-parameter ansatz derived from unitary coupled cluster (UCC) theory, we simulate the effects of noise on the energy expectation value and variance with respect to the ansatz parameter. These numerical simulations provide insights into the accuracy of the prepared quantum state and the efficiency of the classical optimizer that iteratively refines the ansatz. We conduct a comparative study between analytical results derived for the UCC ansatz in the absence of noise and the noisy numerical simulation results obtained using an isotropic depolarizing noise model for each gate. We also compare the relative increase in noise on logically equivalent UCC ansatz circuits generated by randomized compiling. Notably, we observe that the intrinsic variance in the energy due to the simplicity of the ansatz itself compares with the noise induced by the bare circuit.

Wed, Oct 20 @ 13:00-14:30 — QEDS-1: Quantum Engineering, Devices, and Sensing 1​

Practical Implications of SFQ-based Two-qubit Gates
Mohammad Reza Jokar, Richard Rines, Fred Chong (Univ. of Chicago)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: Scalability of today’s superconducting quantum computers is limited due to the huge costs of generating/routing microwave control pulses per qubit from room temperature. One active research area in both industry and academia is to push the classical controllers to the dilution refrigerator in order to increase the scalability of quantum computers. Superconducting Single Flux Quantum (SFQ) is a classical logic technology with low power consumption and ultra-high speed, and thus is a promising candidate for in-fridge classical controllers with maximized scalability. Prior work has demonstrated high-fidelity SFQ-based single-qubit gates. However, little research has been done on SFQ-based multi-qubit gates, which are necessary to realize SFQ-based universal quantum computing. In this paper, we present the first thorough analysis of SFQ-based two-qubit gates. Our observations show that SFQ-based two-qubit gates tend to have high leakage to qubit non-computational subspace, which presents severe design challenges. We show that despite these challenges, we can realize gates with high fidelity by carefully designing optimal control methods and qubit architectures. We develop optimal control methods that suppress leakage, and also investigate various qubit architectures that reduce the leakage. After carefully engineering our SFQ-friendly quantum system, we show that it can achieve similar gate fidelity and gate time to microwave-based quantum systems. The promising results of this paper show that (1) SFQ-based universal quantum computation is both feasible and effective; and (2) SFQ is a promising approach in designing classical controller for quantum machines because it can increase the scalability while preserving gate fidelity and performance.

Efficient Quantum Gate Discovery with Optimal Control
Paul Kairys (Univ. of Tennessee), Travis Humble (Oak Ridge Natl. Lab)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: Optimal control theory provides a framework for numerical discovery of device controls that implement quantum logic gates, but common objective functions used for optimization often assign arbitrarily high costs to otherwise useful controls. We propose a framework for designing objective functions that permit novel gate designs such as echo pulses or locally-equivalent gates. We use numerical simulations to demonstrate the efficacy of the new objective functions by designing microwave-only pulses that act as entangling gates for superconducting transmon architectures. We observe that the proposed objective functions lead to higher fidelity controls in fewer optimization iterations than obtainable by traditional objective functions.
Time: 14:00-14:30 Mountain Time (MDT) — UTC-6

Adaptive Circuit Learning for Quantum Metrology
Ziqi Ma, Pranav Gokhale, Tian-Xing Zheng, Sisi Zhou, Xiaofei Yu, Liang Jiang, Peter Maurer, Fred Chong (Univ. of Chicago)

Abstract: Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics. Our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the “parameter-shift” rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate up to 13.12x SNR improvement over existing fixed protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over the classical limit on 15 qubits using IBM quantum computer. More notably, our algorithm overcomes the decreasing performance of existing entanglement-based protocols with increased system sizes.

Wed, Oct 20 @ 15:15-16:45 — QEDS-2: Quantum Engineering, Devices, and Sensing 2​

Quantum Engineering of Nitrogen Vacancy Diamond Magnetometers (Invited Presentation)
Danielle Braje (MIT Lincoln Laboratory)

Time: 15:15-15:45 Mountain Time (MDT) — UTC-6
Abstract: For a well-defined laboratory proof-of-principle demonstration, quantum mechanics defines limits of sensor performance. A pivotal aspect of quantum engineering is rethinking the system for the goal. Sensitivity might be traded for power consumption; manufacturability and cost could supersede the need for miniaturization; or environmental tolerance could be the primary design driver. This talk will discuss the quantum engineering of solid-state magnetometers.

Perspectives and Progress on Scaling Subsystems for Trapped-Ion Quantum Computing (Invited Presentation)
Steve Sanders (Honeywell Quantum Solutions)

Time: 15:45-16:15 Mountain Time (MDT) — UTC-6
Abstract: Progress in quantum computing depends strongly on the ability to scale critical components and subsystems for every given computer architecture. As systems scale from dozens to thousands of qubits, the subsystems must scale by 100x or more in capability. Yet they cannot grow commensurately in size/power/cost/failure rate (SPCFR) if the technology is to be economical. This economy implies the need for new scalable subsystems which grow much faster in capability than in SPCFR. At the same time, to move out of the noisy “NISQ” era and into the stage of low gate error and quantum error correction, the subsystems require lower noise and better performance. Hence, subsystem development faces the challenges of becoming more capable and yet less costly, simultaneously. In this talk we will discuss such subsystem scaling challenges for one particular architecture, namely that of the trapped-ion quantum charge coupled-device (TIQCCD). We will present a broad survey of major subsystems used in TIQCCD, their critical functional requirements and performance metrics, and some of the most important challenges in scaling these subsystems. Examples from control systems, lasers, and laser beam delivery to ions will be discussed.

Thu, Oct 21 @ 10:45-12:15 — QNC-1: Quantum Networking & Communications 1

Optically Active Nanophotonic Spin Systems in Diamond as Resource for Quantum Communication (Invited Presentation)
Tim Schröder (Humbold-Univ. zu Berlin)

Time: 10:45-11:15 Mountain Time (MDT) — UTC-6
Abstract: Optically active spin defects in diamond have proven to be a promising resource for the implementation of quantum communication and have recently enabled the first demonstration of a 3-node network. In this presentation I bridge ongoing and anticipated work in the control, analysis and engineering of single spin systems for applications in quantum communication. I introduce a recent proposal in which we demonstrate theoretically that the realization of quantum communication over 1000 km becomes achievable with relatively few solid-state spin qubits that are efficiently coupled to an optical nanostructure. These coupled devices and their coherent control, however, have complex requirements that go beyond current state-of-the-art. In our work, we focus on understanding the devices’ nano- and microscopic noise environment, on device fabrication, and on system control schemes. To illustrate these efforts, I outline how to coherently control diamond spin qubits, how to enhance qubit-to-photon coupling with nanostructures, and how to mitigate noise in such nanostructures. In particular, I discuss how to achieve single ‘central’ spin qubits with long coherence times through decoupling from spin bath noise, and how several of such quantum memories can be controlled simultaneously in a sub-diffraction volume. For enhanced qubit-to-photon coupling I introduce photonic nanostructures and methods for their fabrication, and demonstrate how such qubit-nanostructure devices facilitate flux control of electromagnetic radiation. One important (yet undesired) ‘side-effect’ of nanostructures is spectral diffusion of the spin system’s optical transition frequency induced by time-varying electrostatic field noise. I introduce ways to investigate and mitigate the impact of this noise, and show that certain spin defect centres are immune to electric field noise to first order. Finally, I look into the near future and lay out how we plan to generate multi-qubit entangled states—an important step towards applying solid-state spin systems for the implementation of long-distance quantum communication and quantum networks.

Distance-Independent Entanglement Generation in a Quantum Network using Space-Time Multiplexed Greenberger–Horne–Zeilinger (GHZ) Measurements
Ashlesha Patil, Joshua I. Jacobson, Emily Van Milligen (Univ. of Arizona), Don Towsley (Univ. of Massachusetts, Amherst), Saikat Guha (Univ. of Arizona)

Time: 11:15-11:45 Mountain Time (MDT) — UTC-6
Abstract: In a quantum network that successfully creates links–shared Bell states between neighboring repeater nodes—with probability p, and performs Bell State Measurements at nodes with success probability q<1, the end-to-end entanglement generation rate drops exponentially with the distance between consumers, despite multi-path routing. If repeaters can perform multi-qubit projective measurements in the GHZ basis that succeed with probability q, the rate does not change with distance in a certain (p,q) region, but decays exponentially outside. This region where the distance-independent rate occurs is the super-critical region of a new percolation problem. We extend this GHZ protocol to incorporate a time-multiplexing blocklength k, the number of time slots over which a repeater can mix-and-match successful links to perform fusion on. As k increases, the super-critical region expands. For a given (p,q), the entanglement rate initially increases with k, and once inside the super-critical region for a high enough k, it decays as 1/k GHZ states per time slot. When memory coherence time exponentially distributed with mean mu is incorporated, it is seen that increasing k does not indefinitely increase the super-critical region; it has a hard mu-dependent limit. Finally, we find that incorporating space-division multiplexing, i.e., running the above protocol independently in up to d disconnected network regions, where d is the network’s node degree, one can go beyond the 1 GHZ state per time slot rate that the above randomized local-link-state protocol cannot surpass. As (p,q) increases, one can approach the ultimate min-cut entanglement-generation capacity of d GHZ states per slot.

Thu, Oct 21 @ 13:00-14:30 — QNC-2: Quantum Networking & Communications 2

A Quantum-Walk Control Plane for Distributed Quantum Computing in Quantum Networks
Matheus Guedes de Andrade (Univ. of Massachusetts, Amherst), Saikat Guha (Univ. of Arizona), Don Towsley, Wenhan Dai (Univ. of Massachusetts, Amherst)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: Quantum networks are complex systems formed by the interaction among quantum processors through quantum channels. Analogous to classical computer networks, quantum networks allow for the distribution of quantum computation among quantum computers. In this work, we describe a quantum walk protocol to perform distributed quantum computing in a quantum network. The protocol uses a quantum walk as a quantum control signal to perform distributed quantum operations. We consider a generalization of the discrete-time coined quantum walk model that accounts for the interaction between a quantum walker system in the network graph with quantum registers inside the network nodes. The protocol logically captures distributed quantum computing, abstracting hardware implementation and the transmission of quantum information through channels. Control signal transmission is mapped to the propagation of the walker system across the network, while interactions between the control layer and the quantum registers are embedded into the application of coin operators. We demonstrate how to use the quantum walker system to perform a distributed CNOT operation, which shows the universality of the protocol for distributed quantum computing. Furthermore, we apply the protocol to the task of entanglement distribution in a quantum network.

Distributing Graph States Across Quantum Networks
Alex Fischer, Don Towsley (Univ. of Massachusetts, Amherst)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: Graph states are an important class of multipartite entangled quantum states. We propose a new approach for distributing graph states across a quantum network. We consider a quantum network consisting of nodes-quantum computers within which local operations are free-and EPR pairs shared between nodes that can continually be generated. We prove upper bounds for our approach on the number of EPR pairs consumed, number of timesteps taken, and amount of classical communication required, all of which are equal to or better than that of prior work. We also reduce the problem of minimizing the number of timesteps taken to distribute a graph state using our approach to a network flow problem having polynomial time complexity.

Towards Optical Quantum Networks with Rare Earth Ions (Invited Presentation)
Andrei Faraon, Andrei Ruskuc, Chun Ju Wu, Jake Rochman, Tian Xie, Mi Lei, Rikuto Fukumori, Daniel Riedel (California Inst. of Technology)

Time: 14:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Quantum optical networks will enable distribution of quantum entanglement at long distances, with applications including interconnects between future quantum computers and secure quantum communications. I will present our recent work on developing quantum networking components based on rare-earth ions such as single optically addressable quantum bits based on ytterbium 171 in yttrium orthovanadate, microwave to optical transducers based on erbium doped crystals coupled to microwave and optical resonators, and on-chip telecom optical quantum memories.

Thu, Oct 21 @ 15:15-16:45 — QNC-3: Quantum Networking & Communications 3

Password Authentication Schemes on a Quantum Computer
Sherry Wang, Carlisle Adams, Anne Broadbent (Univ. of Ottawa)

Time: 15:15-15:45 Mountain Time (MDT) — UTC-6
Abstract: In a post-quantum world, where attackers may have access to full-scale quantum computers, all classical password-based authentication schemes will be compromised. Quantum copy-protection prevents adversaries from making copies of existing quantum software; we suggest this as a possible approach for designing post-quantum-secure password authentication systems. In this paper, we show an implementation of quantum copy-protection for password verification on IBM quantum computers. We also share our quantum computation results and analyses, as well as lessons learned.

Performance Analysis of the Quantum Safe Multivariate Polynomial Public Key Algorithm
Randy Kuang, Michel Barbeau (Quantropi)

Time: 15:45-16:15 Mountain Time (MDT) — UTC-6
Abstract: The Multivariate Polynomial Public Key (MPPK) algorithm, over a prime Galois field, takes a multiplier multivariate polynomial and two multiplicand univariate linear polynomials to create two product multivariate polynomials. The public key consists of all coefficients of the product multivariate polynomials, except the two constant terms. The private key is made of both constant terms. Encryption takes a list of random numbers, over the prime Galois field. The first number is the secret to exchange. The other random numbers generate noise automatically cancelled by decryption. The secret is easily extracted from the evaluation of a linear equation. The level of security provided by Multivariate Polynomial Public Key (MPPK) is adaptable. The algorithm can be used in several different ways. In this paper, we review the performance achieved by MPPK for several combinations of polynomial configurations and Galois field sizes. For every combination, we calculated the public key size, key generation time, encryption time and decryption time. We also compare the effectiveness of MPPK with the performance of all four NIST PQC finalists. For MPPK, the data has been collected from the execution of an implementation in Java. In comparison to the NIST PQC finalists, MPPK key generation, encryption and decryption performance is excellent.

Pseudo Quantum Random Number Generator with Quantum Permutation Pad
Randy Kuang, Dafu Lou, Alex He, Chris McKenzie, Michael Redding (Quantropi)

Time: 16:15-16:45 Mountain Time (MDT) — UTC-6
Abstract: Cryptographic random number generation is critical for any quantum safe encryption. Based on the natural uncertainty of some quantum processes, variety of quantum random number generators or QRNGs have been created with physical quantum processes. They generally generate random numbers with good unpredictable randomness. Of course, physical QRNGs are costic and require physical integrations with computing systems. This paper proposes a pseudo quantum random number generator with a quantum algorithm called quantum permutation pad or QPP, leveraging the high entropy of quantum permutation space. Unlike the Boolean algebra where the size of information space is 2^n for a n-bit system, a n-bit quantum permutation space consists of 2n! quantum permutation matrices, representing all quantum permutation gates over a n-bit computational basis. This permutation space holds an equivalent Shannon information entropy log_2(2^n!). A QPP can be used to create a pseudo QRNG or pQRNG capable integrated with any classical computing system or directly with any application for good quality deterministic random number generation. Using a QPP pad with 64 8-bit permuation matrices, pQRNG holds 107,776 bits of entropy for the pseudo random number generation, comparing with 4096 bits of entropy in Linux /dev/random. It can be used as a deterministic PRNG or entropy booster of other PRNGs. It can also be used as a whitening algorithm for any hardware random number generator including QRNG without discarding physical bias bits.

Fri, Oct 22 @ 10:45-12:15 — QNC-4: Quantum Networking & Communications 4

The Quantum Internet: A Communication Engineering Perspective (Invited Presentation)
Marcello Caleffi, Jessica Illiano, Seid Koudia, Angela Sara Cacciapuoti (Univ. of Naples Federico II)

Time: 10:45-11:15 Mountain Time (MDT) — UTC-6
Abstract: Internet just turned 50: five decades that shaped the world we live in. Indeed, Internet itself evolved astonishingly since the beginning, from a network prototype consisting of a few static nodes in the early days to a leviathan interconnecting with billions of devices half of the world’s population. Yet the fundamental assumption underlying Internet’s design – i.e., transmitting messages that can be encoded in a sequence of classical bits – remained unchanged during these five decades. But the dawn of the engineering phase of quantum technologies is challenging Internet’s fundamental assumption. Quantum devices demand for communication primitives – namely, the ability to distribute entangled states and to transmit quantum information – governed by the laws of quantum mechanics. Hence, principles and phenomena with no counterpart in classical networks require a major network-paradigm shift to harness the quantum mechanics specificities. This presentation aims at shedding light on the challenges and the open problems arising with the design of a protocol stack for the Quantum Internet.

Networking Trapped-Ion Quantum Computers (Invited Presentation)
Chris Ballance (Univ. of Oxford)

Time: 11:15-11:45 Mountain Time (MDT) — UTC-6
Abstract: Quantum networks of quantum computers offer a promising way to build quantum supercomputing clusters, as well as enabling many new applications. In this talk, I will present our work on networking trapped-ion quantum computers. There are many different physical systems used for quantum computing and for quantum networking; it is challenging to find systems that offer both high performance qubits and high performance networking. I will describe a scalable approach to networking trapped-ion quantum processors, and present results showing a network-link fidelity and rate comparable to the intra-processor operations, allowing the networking operation to be used as a practical computing primitive. I will then present two applications of these networking primitives: (1) demonstrating Device-Independent Quantum Key Distribution (DIQKD), taking advantage of the processor’s local operations and quantum memory to securely distribute a secret key across the network; (2) performing entanglement enhancement spectroscopy between two networked optical atomic clocks.

A Complete Quantum Circuit to Solve the Information Set Decoding Problem
Simone Perriello, Alessandro Barenghi, Gerardo Pelosi (Polytecnico di Milano)

Time: 11:45-12:15 Mountain Time (MDT) — UTC-6
Abstract: Providing strong security margins against cryptanalytic attackers equipped with quantum computers is a major research direction fostered by the USA National Institute of Standards and Technology (NIST) Post-quantum Cryptography Standardization process. Among the viable candidates, code-based asymmetric cryptosystems are one of the prominent approaches. In this work, we propose the first fully detailed quantum circuit to accelerate the solution of the Information Set Decoding problem, the main cryptanalytic tool against such cryptosystems. Our circuit design employs only the Clifford+T gate set, one of the most promising candidates for fault-tolerant quantum computation and does not require quantum RAM. We evaluate the cryptanalytic effort with our circuit design on actual parameters from cryptosystems admitted to the final stage of the NIST standardization process and compare it with the previous conservative asymptotic estimates. We show that the actual computational effort of our solution is smaller than the one estimated via asymptotics by a factor ranging from 4 to 16; while reporting that the conservative margins taken in the design parameters of code-based cryptosystems allow them to benefit of a computational security margin greater than the one needed to quantum-accelerate the brute-forcing of the secret key of an AES cipher, as per the NIST requirements, for various security levels.

Fri, Oct 22 @ 13:00-14:30 — QAA-7: Quantum Algorithms & Applications 7

Quantum Optimization Heuristics with an Application to Knapsack Problems
Wim van Dam (UC-Santa Barbara), Karim Eldefrawy, Nicholas Genise (SRI Intl.), Natalie Parham (Univ. of Waterloo)

Time: 13:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: This paper introduces two techniques that make the standard Quantum Approximate Optimization Algorithm (QAOA) more suitable for constrained optimization problems. The first technique describes how to use the outcome of a prior greedy classical algorithm to define an initial quantum state and mixing operation to adjust the quantum optimization algorithm to explore the possible answers around this initial greedy solution. The second technique is used to nudge the quantum exploration to avoid the local minima around the greedy solutions. To analyze the benefits of these two techniques we run the quantum algorithm on known hard instances of the Knapsack Problem using unit depth quantum circuits. The results show that the adjusted quantum optimization heuristic typically perform better than various classical heuristics. The paper concludes by describing how solutions to Knapsack-like problems have applications in the cybersecurity setting of defending oneself against Distributed Denial of Service attacks.

Transferability of Optimal QAOA Parameters between Random Graphs
Alexey Galda (Univ. of Chicago), Danylo Lykov (Argonne Natl. Lab), Xiaoyuan Liu, Ilya Safro (Univ. of Delaware), Yuri Alexeev (Argonne Natl. Lab)

Time: 13:30-14:00 Mountain Time (MDT) — UTC-6
Abstract: The Quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. In a typical QAOA setup, a set of quantum circuit parameters is optimized to prepare a quantum state used to find the optimal solution of a combinatorial optimization problem. Several empirical observations about optimal parameter concentration effects for special QAOA MaxCut problem instances have been made in recent literature, however, a rigorous study of the subject is still lacking. We show that convergence of the optimal QAOA parameters around specific values and, consequently, successful transferability of parameters between different QAOA instances can be explained and predicted based on the local properties of the graphs, specifically the types of subgraphs (lightcones) from which the graphs are composed. We apply this approach to random regular and general random graphs. For example, we demonstrate how optimized parameters calculated for a 6-node random graph can be successfully used without modification as nearly optimal parameters for a 64-node random graph, with less than 1% reduction in approximation ratio as a result. This work presents a pathway to identifying classes of combinatorial optimization instances for which such variational quantum algorithms as QAOA can be substantially accelerated.
Time: 14:00-14:30 Mountain Time (MDT) — UTC-6

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit CommitmentSamantha Koretsky (Univ. of Chicago), Pranav Gokhale (Super.tech), Jonathan Baker , Joshua Viszlai (Univ. of Chicago), Hanhao Zheng, Niroj Gurung, Ruan Burg, Aleksi Paaso (Commonwealth Edison), Amin Khodaei (Univ. of Denver), Rozhin Eskandarpour (Resilient Entanglement), Fred Chong (Univ. of Chicago)

Abstract: In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. We also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.

Fri, Oct 22 @ 15:15-16:45 — QCS-4: Quantum Computing & Systems 4

Quantum Fan-out: Circuit Optimizations and Technology Modeling
Pranav Gokhale (Super.tech), Samantha Koretsky (Univ. of Chicago), Shilin Huang, Swarnadeep Majumder (Duke Univ.), Andrew Drucer (Univ. of Chicago), Kenneth Brown (Duke Univ.), Fred Chong (Univ. of Chicago))

Time: 15:15-15:45 Mountain Time (MDT) — UTC-6
Abstract: Instruction scheduling is a key compiler optimization in quantum computing, just as it is for classical computing. Current schedulers optimize for data parallelism by allowing simultaneous execution of instructions, as long as their qubits do not overlap. However, on many quantum hardware platforms, instructions on overlapping qubits can be executed simultaneously through global interactions. For example, while fan-out in traditional quantum circuits can only be implemented sequentially when viewed at the logical level, global interactions at the physical level allow fan-out to be achieved in one step. We leverage this simultaneous fan-out primitive to optimize circuit synthesis for NISQ (Noisy Intermediate-Scale Quantum) workloads. In addition, we introduce novel quantum memory architectures based on fan-out. Our work also addresses hardware implementation of the fan-out primitive. We perform realistic simulations for trapped ion quantum computers. We also demonstrate experimental proof-of-concept of fan-out with superconducting qubits. We perform depth (runtime) and fidelity estimation for NISQ application circuits and quantum memory architectures under realistic noise models. Our simulations indicate promising results with an asymptotic advantage in runtime, as well as 7-24% reduction in error.

Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries
Ruslan Shaydulin (Argonne Natl. Lab), Alexey Galda (Univ. of Chicago)

Time: 15:45-16:15 Mountain Time (MDT) — UTC-6
Abstract: High error rates and limited fidelity of quantum gates in near-term quantum devices are the central obstacles to successful execution of the Quantum Approximate Optimization Algorithm (QAOA). In this paper we introduce an application-specific approach for mitigating the errors in QAOA evolution by leveraging the symmetries present in the classical objective function to be optimized. Specifically, the QAOA state is projected into the symmetry-restricted subspace, with projection being performed either at the end of the circuit or throughout the evolution. Our approach improves the fidelity of the QAOA state, thereby increasing both the accuracy of the sample estimate of the QAOA objective and the probability of sampling the binary string corresponding to that objective value. We demonstrate the efficacy of the proposed methods on QAOA applied to the MaxCut problem, although our methods are general and apply to any objective function with symmetries, as well as to the generalization of QAOA with alternative mixers. We experimentally verify the proposed methods on an IBM Quantum processor, utilizing up to 5 qubits. When leveraging a global bit-flip symmetry, our approach leads to a 23% average improvement in quantum state fidelity.

Adaptive Job and Resource Management for the Growing Quantum Cloud
Gokul Subramanian, Kaitlin Smith (Univ. of Chicago), Prakash Murali (Princeton Univ.), Fred Chong (Univ. of Chicago)

Time: 16:15-16:45 Mountain Time (MDT) — UTC-6
Abstract: As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing. This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations.