Technical Paper Tracks

QCE20 VirtualVirtual IEEE Quantum Week aims to be a leading venue for presenting high-quality original research, groundbreaking innovations, and compelling insights in quantum computing and engineering. Technical papers are peer-reviewed and can be on any topic related to quantum computing and its technologies. Papers accepted by IEEE International Conference on Quantum Computing & Engineering (QCE20) will be submitted to IEEE Xplore Digital Library. The best papers will be invited to the journals IEEE Transactions on Quantum Engineering (TQE) and ACM Transactions on Quantum Computing (TQC).

QCE20 Format and Schedule

QCE20 will be held in a digital-only or virtual-only format during the week of Oct 12-16, 2020. QCE20 was originally scheduled to be held in Broomfield, Colorado which is in the Mountain Time Zone (MDT) or UTC-6. The QCE20 daily program will be broadcast from 8:30 to 19:45 Mountain Time. Recorded sessions will be available for registered QCE20 attendees a few hours after the live sessions until November 30, 2020. Every day of this week, QCE20 hosts 9-10 parallel tracks of quantum computing and engineering programming including Workshops, Tutorials, Technical Paper Tracks intermixed with Keynotes, Panels, Exhibits, Posters, Birds of a Feather, and Networking sessions featuring a total of over 270 hours of programming.

Technical Papers — Final Digital Presentation Guidelines

Please visit the Advance Conference Program for the time and date of the QCE20 Technical Paper presentations. Final Digital Recording and Uploading Guidelines have now been posted. As a Technical Paper Presenter, please visit this Presentation Guideline page regularly for updates on how to prepare your QCE20 Technical Paper presentation for the week of Oct 12-16, 2020.

Technical Paper Inquiries

For any inquiries or questions about Technical Papers, please contact Program Board Chair Greg Byrd.

Technical Papers Program

The following Technical Papers have been accepted for presentation at the virtual QCE20 and inclusion in the Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE20). QCE20 Technical Papers have been peer-reviewed by at least three experts who are members of the QCE20 technical program committee.

QCE20 Technical Papers will be presented during the week of Oct 12-16, 2020 every day in the Technical Paper Tracks between 10:45 and 16:45 Mountain Time (MDT) — UTC-6.

Mon, Oct 12 — 10:45 — Quantum Information & Algorithms QIA1

  • 10:45 — Ewout van den Berg, IBM T.J. Watson Research Center. Quantum phase estimation with optimized sample complexity
    Abstract — In this work we consider Kitaev’s algorithm for quantum phase estimation. We analyze the use of phase shifts that simplify the estimation of successive bits in the estimation of unknown phase phi. By using increasingly accurate shifts we reduce the number of measurements to the point where only a single measurement is needed for each additional bit. This results in an algorithm that can estimate phi to (m+2) bits of accuracy with probability at least 1−epsilon using N(epsilon)+m measurements, where N(epsilon) is a constant that depends only on epsilon and the particular sampling algorithm. We present different sampling algorithms and study the exact number of measurements needed through careful numerical evaluation, and provide theoretical bounds and numerical values for N(epsilon).
  • 11:15 — Hiroshi Yano, Yudai Suzuki, Rudy Raymond and Naoki Yamamoto Keio University and IBM Research Tokyo. Efficient discrete feature encoding for variational quantum classifier — QIA Track Best Paper Award
    Abstract — Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving machine learning tasks in, e.g., classification, regression, and recommender systems. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational Quantum Classification (VQC) is one of such variational methods with possible quantum advantage in using quantum-enhanced features that are hard to compute by classical methods. Its performance depends on the mapping of classical features into quantum-enhanced feature space. Although there have been many quantum-mapping functions proposed so far, there is little discussion on efficient mapping of discrete features, such as, race, gender, marriage status and others that are often significant for classifying datasets of interest. We first introduce the use of Quantum Random Access Coding (QRAC) to map such discrete features efficiently into limited number of qubits for VQC. We numerically show that QRAC can help speeding up the training of VQC by reducing its parameters via reduction on the number of qubits for the mapping. We confirm the effectiveness of the QRAC in VQC by experimenting on classification of healthcare datasets with both simulators and real quantum devices.
  • 11:45 — William Cappelletti, Rebecca Erbanni and Joaquín Keller, Entropica Labs, Singapore. Polyadic quantum classifier
    Abstract — We introduce here a supervised quantum machine learning algorithm for n-ary classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an ibmq 5-qubit quantum computer and the algorithm shows good results –compared to a classical machine learning model– for ternary classification of the iris dataset. Furthermore, through simulations we evaluate how it fares on known binary datasets and, with a higher number of classes, on synthetic datasets.

Mon, Oct 12 — 13:00 — Quantum Information & Algorithms QIA2

  • 13:00 — Julien Gacon, Christa Zoufal and Stefan Woerner, IBM Research Zürich and ETH Zürich. Quantum-enhanced simulation-based optimization
    Abstract — In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective function that is computationally expensive to evaluate exactly, and thus, is approximated via simulation. Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up over classical Monte Carlo simulation.
    Hence, in many cases, it can achieve a speed-up for simulation-based optimization as well. Combining QAE with ideas from quantum optimization, we show how this can be used not only for continuous but also for discrete optimization problems. Furthermore, the algorithm is demonstrated on illustrative problems such as portfolio optimization with a Value at Risk constraint and inventory management.
  • 13:30 — Zsolt Tabi, Ericsson Hungary and Eötvös Loránd University; Kareem H. El-Safty, Wigner Research Centre for Physics; Zsófia Kallus, Ericsson Research Budapest; Péter Hága, Ericsson Research Budapest; Tamás Kozsik, Eötvös Loránd University; Adam Glos, Polish Academy of Sciences and Zoltán Zimborás, Wigner Research Centre for Physics and Budapest University of Technology. Quantum optimization for the graph coloring problem with space-efficient embedding
    Abstract — Current quantum computing devices have different strengths and weaknesses depending on their architectures. This means that flexible approaches to circuit design is necessary. We address this task by introducing a novel space-efficient quantum optimization algorithm for the graph coloring problem. Our circuits are deeper than the ones of the standard approach. However, the number of required qubits is exponentially reduced in the number of colors. We present extensive numerical simulations demonstrating that our approach lowers the quantum volume required for solving the same coloring problem. Furthermore, to explore alternative current possibilities, we also perform a study of random graph coloring on a quantum annealer to test the limiting factors of that approach, too.
  • 14:00 — Nathan Thompson, James Steck and Elizabeth Behrman, Wichita State University. A non-algorithmic approach to “programming” quantum computers via machine learning
    Abstract — Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many of these genuine quantum algorithms, like Shor’s prime factoring or phase estimation, require extremely long circuit depth for any practical application, which necessitates error correction. In contrast, we show that machine learning can be used as a systematic method to construct algorithms, that is, to non-algorithmically “program” quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate “building blocks”, eliminating that difficult step and potentially increasing efficiency by simplifying and reducing unnecessary complexity. In addition, our non-algorithmic machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrate this using a fundamentally non-classical calculation: experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware, using reinforcement learning.

Mon, Oct 12 — 15:15 — Quantum Information & Algorithms QIA3

  • 15:15 — Andreas Bärtschi and Stephan Eidenbenz. Grover mixers for QAOA, Los Alamos National Laboratory. Shifting complexity from mixer design to state preparation
    Abstract — We propose GM-QAOA, a variation of the Quantum Alternating Operator Ansatz (QAOA) that uses Grover-like selective phase shift mixing operators. GM-QAOA works on any NP optimization problem for which it is possible to efficiently prepare an equal superposition of all feasible solutions; it is designed to perform particularly well for constraint optimization problems, where not all possible variable assignments are feasible solutions. GM-QAOA has the following features: (i) It is not susceptible to Hamiltonian Simulation error (such as Trotterization errors) as its operators can be implemented exactly using standard gate sets and (ii) Solutions with the same objective value are always sampled with the same amplitude.
    We illustrate the potential of GM-QAOA on several optimization problem classes: for permutation-based optimization problems such as the Traveling Salesperson Problem, we present an efficient algorithm to prepare a superposition of all possible permutations of n numbers, defined on O(n^2) qubits; for the hard constraint k-Vertex-Cover problem, and for an application to Discrete Portfolio Rebalancing, we show that GM-QAOA outperforms existing QAOA approaches.
  • 15:45 — Jeremy Cook, Stephan Eidenbenz and Andreas Bärtschi, Los Alamos National Laboratory. The quantum alternating operator Ansatz on Max-k Vertex Cover
    Abstract — The Quantum Alternating Operator Ansatz is a generalization of the Quantum Approximate Optimization Algorithm (QAOA) designed for finding approximate solutions to combinatorial optimization problems with hard constraints. In this paper, we study Max-k Vertex Cover under this ansatz due to its modest complexity, while still being more complex than the well studied problems of Max-Cut and Max E3-LIN2. Our approach includes (i) a performance comparison between easy-to-prepare classical states and Dicke states, (ii) a performance comparison between two XY-Hamiltonian mixing operators: the ring mixer and the complete graph mixer, (iii) an analysis of the distribution of solutions via Monte Carlo sampling, and (iv) the exploration of efficient angle selection strategies. Our results are: (i) Dicke states improve performance compared to easy-to-prepare classical states, (ii) an upper bound on the simulation of the complete graph mixer, (iii) the complete graph mixer improves performance relative to the ring mixer, (iv) numerical results indicating the standard deviation of the distribution of solutions decreases exponentially in p (the number of rounds in the algorithm), requiring an exponential number of random samples find a better solution in the next round, and (iv) a correlation of angle parameters which exhibit high quality solutions that behave similarly to a discretized version of the Quantum Adiabatic Algorithm.

Mon, Oct 12 — 15:15 — Quantum Engineering QENG

  • 15:15 — Sahar Daraeizadeh, Shavindra Premaratne and Anne Matsuura, Intel Labs. Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning — QENG Track Best Paper Award
    Abstract — In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility.
  • 15:45 — Andrew Lanham and Brian R. La Cour, The University of Texas at Austin. Detection-based measurement for quantum emulation devices
    Abstract — Quantum emulation devices use analog electronic signals to mimic precisely the behavior of a gate-based quantum computer. These devices also introduce the flexibility to choose different detection rules to generate measurement outcomes. Such rules may depend on deterministic decisions rather than the randomized decisions occurring in Born rule-based measurements of true quantum systems. In this paper, we leverage this flexibility in an emulation device to consider implementations of measurement detection rules with better performance than the Born rule. We visit physically-inspired detection strategies such as maximum-energy and threshold detectors, as well the optimal maximum-likelihood detector. We gauge the detectors by how often they successfully produce the correct outcome of a quantum algorithm in the presence of noise, deriving computationally efficient implementations and comparing their probability of error performance to the Born rule detector’s. We find that efficient detectors may be defined with significantly better performance than the Born rule detector at comparable signal-to-noise ratios. We also introduce a theoretical performance comparison to a true quantum computing system operating in a generalized depolarizing channel.

Tue, Oct 13 — 10:45 — Quantum Communications, Sensing & Cryptography QCSC1

  • 10:45 — Patricio Fuentes, Josu Etxezarreta Martinez, Pedro M. Crespo, Tecnun – University of Navarra and Javier Garcia-Frías, University of Delaware. Performance of non-CSS LDGM-based quantum codes over the Misidentified Depolarizing Channel
    Abstract — Quantum Low Density Generator Matrix (QLDGM) codes based on Calderbank-Steane-Shor (CSS) constructions have shown unprecedented error correction capabilities in the paradigm of quantum communication. Recently, a strategy based on non-CSS quantum codes derived from QLDGM CSS codes has been shown to surpass other Quantum Low Density Parity Check (QLDPC) schemes proposed in the literature over the depolarizing channel. Given the importance of quantum channel estimation and the impact it has on the performance of QLDPC codes, in this article, we study the behaviour of non-CSS QLDGM codes under the umbrella of channel mismatch. We begin by showing how a relatively accurate estimate of the quantum channel is pivotal for these codes to perform appropriately. We follow this by analyzing an off-line and an on-line quantum channel parameter estimation technique, as well as discussing how these methods affect the Quantum Error Correction (QEC) codes under consideration. Finally, we show how the on-line methodology yields similar performance to the perfect channel knowledge scenario despite its relative simplicity.
  • 11:15 — Josu Etxezarreta Martinez, Patricio Fuentes, Pedro M. Crespo, Tecnun – University of Navarra and Javier Garcia-Frías, University of Delaware. Pauli channel online estimation protocol for quantum turbo codes
    Abstract — In this paper, we tackle the channel estimation problem for Pauli channels. Online estimation methods for the depolarizing channel have been proposed in previous literature. However, realistic quantum devices often exhibit an asymmetric behaviour not captured by the symmetric depolarizing model, implying that the estimation method used by the Quantum Turbo Codes (QTC) should exploit such asymmetry for the error correcting operations to be successful. Consequently, we propose an online iterative method that aids in successfully estimating each of the individual error probabilities associated with the Pauli channel, increasing the probability of correct decoding. The benefits this method provides come at the expense of an increase in the decoding complexity.
  • 11:45 — Muyuan Li, Georgia Institute of Technology and Theodore Yoder, IBM T.J. Watson Research Center. A numerical study of Bravyi-Bacon-Shor and subsystem hypergraph product codes — QCSC Track Best Paper Award
    Abstract—We provide a numerical investigation of two families of subsystem quantum codes that are related to hypergraph product codes by gauge-fixing. The first family consists of the Bravyi-Bacon-Shor (BBS) codes which have optimal code parameters for subsystem quantum codes local in 2-dimensions. The second family consists of the constant rate “generalized Shor” codes of Bacon and Cassicino [1], which we re-brand as subsystem hypergraph product (SHP) codes. We show that any hypergraph product code can be obtained by entangling the gauge qubits of two SHP codes. To evaluate the performance of these codes, we simulate both small and large examples. For circuit noise, a J21, 4, 3K BBS code and a J49, 16, 3K SHP code have pseudo thresholds of 2 × 10−3 and 8 × 10−4, respectively. Simulations for phenomenological noise show that large BBS and SHP codes start to outperform surface codes with similar encoding rate at physical error rates 1 × 10−6 and 4 × 10−4, respectively.

Tue, Oct 13 — 10:45 — Quantum Applications and Simulating Nature QASN1

  • 10:45 — Khaled Kelany, Nikitas Dimopoulos, Clemens Adolphs, Bardia Barabadi and Amirali Baniasadi, University of Victoria. Quantum annealing approaches to the phase-unwrapping problem in synthetic-aperture radar imaging — QASN Track Best Paper Award
    Abstract — The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger-sized images. Our approach involves formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved on a quantum annealer. Given that present embodiments of quantum annealers remain limited in the number of qubits they possess, we decompose the problem into a set of subproblems that can be solved individually. These individual solutions are close to optimal up to an integer constant, with one constant per sub-image. In a second phase, these integer constants are determined as a solution to yet another QUBO problem. We test our approach with a variety of software-based QUBO solvers and on a variety of images, both synthetic and real. Additionally, we experiment using D-Wave Systems’ quantum annealer, the D-Wave 2000Q. The software-based solvers obtain high-quality solutions comparable to state-of-the-art phase-unwrapping solvers. We are currently working on optimally mapping the problem onto the restricted topology of the quantum annealer to improve the quality of the solution.
  • 11:15 — Francesco Tacchino, Panagiotis Barkoutsos, Chiara Macchiavello, Dario Gerace, Ivano Tavernelli and Daniele Bajoni, IBM Research Europe, Zürich and University of Pavia. Variational learning for quantum artificial neural networks
    Abstract — In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The rapidly growing field of Quantum Machine Learning aims at bringing together these two ongoing revolutions. Here we first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. Such framework is explicitly designed to achieve exponential advantage in storage resources with respect to classical counterparts. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. While keeping full compatibility with the overall memory-efficient feed-forward architecture, such a construction effectively reduces the quantum circuit depth required to determine the activation probability of single neurons upon input of the relevant data-encoding quantum states. This suggests a viable approach towards the use of quantum neural networks for pattern classification on near-term quantum hardware.

Tue, Oct 13 — 13:00 — Quantum Communications, Sensing & Cryptography QCSC2

  • 13:00 — Omar Amer, Walter O. Krawec and Bing Wang, University of Connecticut. Efficient routing for quantum key distribution Networks
    Abstract — As quantum key distribution becomes increasingly practical, questions of how to effectively employ it in large-scale networks and over large distances becomes increasingly important. To that end, in this work, we model the performance of the E91 entanglement based QKD protocol when operating in a network consisting of both quantum repeaters and trusted nodes. We propose a number of routing protocols for this network and compare their performance under different usage scenarios. Through our modeling, we investigate optimal placement and number of trusted nodes versus repeaters depending on device performance (e.g., quality of the repeater’s measurement devices). Along the way we discover interesting lessons determining what are the important physical aspects to improve for upcoming quantum networks in order to improve secure communication rates.
  • 13:30 — Boxi Li, ETH Zürich; Tim Coopmans and David Elkouss, Delft University of Technology. Efficient optimization of cut-offs in quantum repeater chains
    Abstract — Quantum communication enables the implementation of tasks that are unachievable with classical resources. However, losses on the communication channel preclude the direct long-distance transmission of quantum information in many relevant scenarios. In principle quantum repeaters allow one to overcome losses. However, realistic hardware parameters make long-distance quantum communication a challenge in practice. For instance, in many protocols an entangled pair is generated that needs to wait in quantum memory until the generation of an additional pair. During this waiting time the first pair decoheres, impacting the quality of the final entanglement produced. At the cost of a lower rate, this effect can be mitigated by imposing a cut-off condition. For instance, a maximum storage time for entanglement after which it is discarded. In this work, we optimize the cut-offs for quantum repeater chains. First, we develop an algorithm for computing the probability distribution of the waiting time and fidelity of entanglement produced by repeater chain protocols which include a cut-off. Then, we use the algorithm to optimize cut-offs in order to maximize secret-key rate between the end nodes of the repeater chain. We find that the use of the optimal cut-off extends the parameter regime for which secret key can be generated and moreover significantly increases the secret-key rate for a large range of parameters.

Tue, Oct 13 — 13:00 — Quantum Applications and Simulating Nature QASN2

  • 13:00 — Adam Holmes and Anne Matsuura, Intel Labs. Efficient quantum circuits for accurate preparation of smooth, differentiable quantum states
    Abstract — Effective quantum computation relies upon making good use of the exponential information capacity of a quantum machine. A large barrier to designing quantum algorithms for execution on real quantum machines is that, in general, it is intractably difficult to construct an arbitrary quantum state to high precision. Many quantum algorithms rely instead upon initializing the machine in a simple state, and evolving the state through an efficient (i.e. at most polynomial-depth) quantum algorithm. In this work, we show that there exist families of quantum states that can be prepared to high precision with circuits of linear size and depth. We focus on real-valued, smooth, differentiable functions with bounded derivatives on a domain of interest, exemplified by commonly used probability distributions. We further develop an algorithm that requires only linear classical computation time to generate accurate linear-depth circuits to prepare these states, and apply this to well-known and heavily-utilized functions including Gaussian and lognormal distributions. Our procedure rests upon the quantum state representation tool known as the \textit{matrix product state} (MPS). By efficiently and scalably encoding an explicit amplitude function into an MPS, a high fidelity, linear-depth circuit can directly be generated. These results enable the execution of many quantum algorithms that, aside from initialization, are otherwise depth-efficient.
  • 13:30 — Nicolas Sawaya, Gian Giacomo Guerreschi and Adam Holmes, Intel Labs. On connectivity-dependent resource requirements for digital quantum simulation of d-level particles
    Abstract — A primary objective of quantum computation is to efficiently simulate quantum physics. Scientifically and technologically important quantum Hamiltonians include those with spin-$s$, vibrational, photonic, and other bosonic degrees of freedom, \textit{i.e.} problems composed of, or approximated by, $d$-level particles (qudits). Recently, several methods for encoding these systems into a set of qubits have been introduced, where each encoding’s efficiency was studied in terms of qubit and gate counts. Here, we build on previous results by including effects of hardware connectivity. To study the number of SWAP gates required to Trotterize commonly used quantum operators, we use both analytical arguments and automatic tools that optimize the schedule in multiple stages. We study the unary (or one-hot), Gray, standard binary, and block unary encodings, with three connectivities: linear array, ladder array, and square grid. Among other trends, we find that while the ladder array leads to substantial efficiencies over the linear array, the advantage of the square over the ladder array is less pronounced. Additionally, analytical and numerical results show that the Gray code is less advantageous when connectivity constraints are considered. These results are applicable in hardware co-design and in choosing efficient encodings for a given set of near-term quantum hardware when simulating Hamiltonians with $d$-level degrees of freedom.

Tue, Oct 13 — 15:15 — Quantum Communications, Sensing & Cryptography QCSC3

  • 15:15 — Randy Kuang and Nicolas Bettenburg, Quantropi Inc., Ottawa. Quantum public key distribution using randomized Glauber states
    Abstract — State-of-the-art Quantum Key Distribution (QKD) is based on the uncertainty principle of qubits on quantum measurements and is theoretically proven to be unconditionally secure. Over the past three decades, QKD has been explored with single photons as the information carrier. More recently, attention has shifted towards using weak coherent laser pulses as the information carrier. In this paper, we propose a novel quantum key distribution mechanism over a pure optical channel with the randomized Glauber states. The proposed mechanism closely resembles a quantum mechanical implementation of the public key envelope idea. Here is a brief summarization of our proposal:
    1. A user (Bob) generates a Glauber state as a quantum public key envelope (QPKE) by randomly modulating a secret phase φr, known only by him, and transmits it over an optical channel to the other user (Alice).
    2. Alice modulates a key phase φk into the QPKE Glauber state based on a random key and selected modulation scheme and returns it back to Bob.
    3. For the returning QPKE Glauber state, Bob derandomizes it with his private key or the phase -φr and then passes it to a coherent receiver to measure the key phase φk.
    4. For better security, differential phase-shift keying (DPSK) technique with a reference list is applied to extract keys.
    For the proposed solution, physical countermeasures are available to provide path authentication and to avoid man-in-the-middle attacks. Other attack vectors can be effectively mitigated by leveraging the QPKE, the uncertainty principle and the DPSK modulation technique.
  • 15:45 — Andrew Reinders, Santosh Ghosh, Rafael Misoczki and Manoj Sastry, Intel Labs. Efficient BIKE hardware design with constant-time decoder
    Abstract — BIKE (Bit-flipping Key Encapsulation) is a promising candidate under consideration in the NIST Post-Quantum Cryptography Standardization process. It is a code-based cryptosystem with a simple definition and well-understood underlying security. The most critical step in this cryptosystem consists of correcting errors with a QC-MDPC linear code. Published performance on 64-bit processors shows this decode procedure takes 20M cycles for the software implementation, and worse for embedded devices. In this paper, we propose a BIKE coprocessor that accelerates the operations least suitable to embedded software implementation, enabling power-constrained SoC devices to directly implement BIKE with good performance. We propose a simplification of the grey-black decoder in the BIKE spec, which is more friendly to hardware implementations, and demonstrate the performance of this decoder on an Intel Arria 10 FPGA platform. Our implementation has a cycle-area performance comparable to other MDPC decoders in the literature, while including design elements which may facilitate power side channel resilience. The main result is to enable BIKE to be run on small SoC platforms, and therefore for those devices to resist attacks by quantum computers.
  • 16:15 — Noel De la Cruz, Uttam Paudel, Ethan Tucker, Andrew Mollner, Joseph Betser, Pavel Ionov, Joseph Touch and Joshua Stoermer, The Aerospace Corporation El Segundo, California. Decoy-state quantum key distribution with direct modulated commercial off-the-shelf VCSEL lasers
    Abstract — We report on a BB84 decoy-state quantum key distribution (QKD) system constructed using commercial offthe-shelf (COTS) components. Four 794 nm vertical-cavity surface-emitting lasers (VCSELs) are current-modulated at 10 MHz rate with three power levels to form a decoy state transmitter. The COTS VCSELs exhibit long term stability with high polarization extinction ratio, narrow band operation (sub-nanometer bandwidth), and wavelength tunability and stability suitable for constructing four indistinguishable qubit channels. A 780 nm, 10 MHz time-transfer channel is used for transferring the timing information along with a start and end marker for the qubit transfer period. Internally-developed transmitter laser drivers and receiver detectors are controlled and read out with COTS system-on-chip (SoC) boards. We obtain a nominal bit-error-rate (BER) of ~4% for the system. We also report on the development of a synchronous (100 MHz) single photon detector for increasing the repetition rate of our QKD system. This work shows promise for building a COTS-based, small size, weight, and power hardware for space applications.

Wed, Oct 14 — 10:45 — Quantum Computing QC1

  • 10:45 — Jun Doi and Hiroshi Horii, IBM Research Tokyo. A cache blocking technique to large scale quantum computing simulation on supercomputers
    Abstract — Classical computers require large memory resources and computational power to simulate quantum circuits with a large number of qubits. Even supercomputers that can store huge amounts of data face a scalability issue in regard to parallel quantum computing simulations because of the latency of data movements between distributed memory spaces. Here, we apply a cache blocking technique by inserting swap gates in quantum circuits to decrease data movements. We implemented this technique in the open source simulation framework Qiskit Aer. We evaluated our simulator on GPU clusters and observed good scalability.
  • 11:15 — Marc Grau Davis, Ethan Smith, Ana Tudor, Koushik Sen, Irfan Siddiqi, University of California Berkeley and Costin Iancu, Lawrence Berkeley National Laboratory. Towards depth optimal, topology aware quantum circuit synthesis — QC Track Best Paper Award
    Abstract — We present an algorithm for compiling arbitrary unitaries into a sequence of gates native to a quantum processor. As accurate CNOT gates are hard for the foreseeable Noisy-Intermediate-Scale Quantum devices era, our A* inspired algorithm attempts to minimize their count, while accounting for connectivity. We discuss the search strategy together with metrics to expand the solution frontier. For a workload of circuits with complexity appropriate for the NISQ era, we produce solutions well within the best upper bounds published in literature and match or exceed hand tuned implementations, as well as other existing synthesis alternatives. In particular, when comparing against state-of-the-art available synthesis packages we show 2.4x average (up to 5.3x) reduction in CNOT count. We also show how to re-target the algorithm for a different chip topology and native gate set, while obtaining similar quality results. We believe that empirical tools like ours can facilitate algorithmic exploration, gate set discovery for quantum processor designers, as well as providing useful optimization blocks within the quantum compilation tool-chain.
  • 11:45 — Michel Barbeau, Carleton University, Joaquin Garcia-Alfaro, SAMOVAR, Telecom SudParis and Evangelos Kranakis, Carleton University. Capacity requirements of quantum repeaters
    Abstract — We consider the problem of path congestion avoidance in quantum communication networks. We assume networks of quantum repeaters and terminals in which the sets of complete paths between terminals may affect the capacity of repeaters in the network. We compare the reduction of congestion avoidance of two representative path establishment algorithms: shortest-path establishment vs. layer-peeling path establishment. We observe that both strategies provide an equivalent entanglement rate, while the layer-peeling establishment algorithm considerably reduces the congestion in the network of repeaters. Repeaters in the inner layers get less congested and require a lower number of qubits, while providing a similar entanglement rate.

Wed, Oct 14 — 10:45 — Quantum Communications, Sensing & Cryptography QCSC4

  • 10:45 — Dov Fields, City University of New York; Arpád Varga, University of Pécs, Hungary and Janos Bergou, City University of New York. Sequential measurements on qubits by multiple observers: Joint best guess strategy
    Abstract — We study sequential state discrimination measurements performed on the same qubit by subsequent observers. Specifically, we focus on the case when the observers perform a kind of a minimum-error type state discriminating measurement where the goal of the observers is to maximize their joint probability of successfully guessing the state that the qubit was initially prepared in. We call this the joint best guess strategy. In this scheme Alice prepares a qubit in one of two possible states. The qubit is first sent to Bob, who measures it, and then on to Charlie, and so on to altogether N consecutive receivers who all perform measurements on it. The goal for all observers is to determine which state Alice sent. In the joint best guess strategy, every time a system is received the observer is required to make a guess, aided by the measurement, about its state. The price to pay for this requirement is that errors must be permitted, the guess can be correct or in error. There is a nonzero probability for all the receivers to successfully identify the initially prepared state, and we maximize this joint probability of success. This work is a step toward developing a theory of nondestructive sequential quantum measurements and could be useful in multiparty quantum communication schemes based on communicating with single qubits, particularly in schemes employing continuous variable states. It also represents a case where subsequent observers can probabilistically and optimally get around both the collapse postulate and the no-broadcasting theorem.
  • 11:15 — Janis Nötzel and Stephen DiAdamo, Technische Universität München. Entanglement-enhanced communication networks
    Abstract — Building quantum networks ultimately requires a strong use case. As today’s design and use of the Internet solely rests on the interconnection of classical computing devices, the development of hardware should take this dependence on an existing market into account. One might think quantum secure communication would be such a use case, but the entire design of the current Internet is built on the end-to-end argument and may reject the idea of implementing security as a physical layer protocol. On the other hand, higher data rates and reduced latency have been successfully used as key arguments for the conception of new communication standards. We thus argue that exactly these two figures of merit should be used again. We define two new initial stages of development of the quantum Internet, where in the first phase entanglement is only generated and used between network nodes, and in second phase entanglement swapping and thus distribution of entanglement over increasing distances becomes possible. In both phases, we show by simulation how the available new protocols increase the network capacity. Interestingly, following this envisioned approach can serve the needs of current market participants while paving the road for fully quantum applications in the future.
  • 11:145 — Randy Kuang and Nicolas Bettenburg, Quantropi Inc., Ottawa. Shannon perfect secrecy in a discrete Hilbert space
    Abstract — The one-time-pad (OTP) was mathematically proven to be perfectly secure by Shannon in 1949. We propose to extend the classical OTP from an n-bit finite field to the entire symmetric group over the finite field. Within this context the symmetric group can be represented by a discrete Hilbert sphere DHS with an n-bit computational basis. Unlike the continuous Hilbert space defined over a complex field in quantum computing, a DHS is defined over the finite field GF(2). Within this DHS, the entire symmetric group can be completely described by the complete set of n-bit binary permutation matrices. Encoding of a plaintext can be done by randomly selecting a permutation matrix from the symmetric group to multiply with the computational basis vector associated with the state corresponding to the data to be encoded. Then, the resulting vector is converted to an output state as the ciphertext. The decoding is the same procedure but with the transpose of the permutation matrix. We demonstrate that under this extension, the 1-to-1 mapping in the classical OTP is equally likely decoupled in Discrete Hilbert Space. The uncertainty relationship between permutation matrices protects the selected pad, consisting of M (quantum) permutation matrices (also called Quantum Pad, or QTP). QTP not only maintains the perfect secrecy property of the classical formulation but is also reusable without invalidating the perfect secrecy property. The extended Shannon perfect secrecy is then stated such that the ciphertext C gives absolutely no information about the plaintext P and the pad.

Wed, Oct 14 — 13:00 — Quantum Computing QC2

  • 13:00 — Elijah Pelofske, Los Alamos National Laboratory; Georg Hahn, Harvard University and Hristo Djidjev, Los Alamos National Laboratory. Advanced anneal paths for improved quantum annealing
    Abstract — Advances in quantum annealing technology make it possible to obtain high quality approximate solutions of important NP-hard problems. With the newer generations of the D-Wave annealer, more advanced features are available which allow the user to have greater control of the anneal process. In this contribution, we study how such features can help in improving the quality of the solutions returned by the annealer. Specifically, we focus on two of these features: reverse annealing and h-gain. Reverse annealing (RA) was designed to allow refining a known solution by backward annealing from a classical state representing the solution to a mid-anneal point where a transverse field is present, followed by an ordinary forward anneal, which is hoped to improve on the previous solution. The h-gain (HG) feature stands for time-dependent gain in Hamiltonian linear (h) biases and was originally developed to help study freezeout times and phase transitions in spin glasses. Here we apply HG to bias the quantum state in the beginning of the annealing process towards the known solution as in the RA case, but using a different apparatus. We also investigate a hybrid reverse annealing/h-gain schedule, which has a backward phase resembling an RA step and whose forward phase uses the HG idea. To optimize the parameters of the schedules, we employ a Bayesian optimization framework. We test all techniques on a variety of input problems including the weighted Maximum Cut problem and the weighted Maximum Clique problem.
  • 13:30 — Wim Lavrijsen, Lawrence Berkeley National Laboratory; Ana Tudor, University of California Berkeley; Juliane Mueller, Costin Iancu and Wibe De Jong, Lawrence Berkeley National Laboratory. Classical optimizers for noisy intermediate-scale quantum devices
    Abstract — We present a collection of optimizers tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization (QAOA) algorithms. They are also used for calibration tasks, hyperparameter tuning, in machine learning, etc. We analyze the efficiency and effectiveness of different optimizers in a VQE case study. VQE is a hybrid algorithm, with a classical minimizer step driving the next evaluation on the quantum processor. While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results. We explore state-of-the-art gradient-free optimizers capable of handling noisy, black-box, cost functions and stress-test them using a quantum circuit simulation environment with noise injection capabilities on individual gates. Our results indicate that specifically tuned optimizers are crucial to obtaining valid science results on NISQ hardware, and will likely remain necessary even for future fault tolerant circuits.
  • 14:00 — Tudor Giurgica-Tiron, Yousef Hindy, Stanford University; Ryan LaRose, Michigan State University; Andrea Mari, Xanadu and William Zeng, Goldman, Sachs & Co, Unitary Fund. Portable and efficient zero noise extrapolation for quantum error mitigation
    Abstract — Zero-noise extrapolation (ZNE) is an increasingly popular technique for mitigating errors in noisy quantum computations without using additional quantum resources. We review the fundamentals of ZNE and introduce several improvements to noise scaling and extrapolation, the two key components in the technique. We introduce unitary folding and parameterized noise scaling. These are digital noise scaling frameworks, i.e. one can apply them using only gate-level access common to most quantum instruction sets.
    We also study different extrapolation methods, including a new adaptive protocol, using a statistical inference framework. Benchmarks of our techniques show their improvement over existing ZNE methods and show ZNE’s effectiveness at larger qubit numbers than have been tested previously.
    In addition to presenting new results, this work is a self-contained introduction to the practical use of ZNE by quantum programmers.

Wed, Oct 14 — 15:15 — Quantum Computing QC3

  • 15:15 — Natalie Brown, Georgia Institute of Technology; Andrew Cross, IBM T.J. Watson Research Center and Kenneth Brown, Duke University. Critical faults of leakage errors on the surface code
    Abstract — Leakage is a particularly damaging error that occurs when a qubit leaves the defined computational subspace. Leakage errors limit the effectiveness of quantum error correcting codes by spreading additional errors to other qubits and corrupting syndrome measurements. The effects of leakage errors on the surface code has been studied in various contexts. However, the effects of a leaked data qubit versus a leaked ancilla qubit can be quite different.
    Here, we study the effects of data leakage and ancilla leakage separately. We show that data leakage is much less damaging.
    We show that the surface code maintains its distance in the presence of leakage by either confining leakage to data qubits or eliminating aniclla qubit leakage at the critical fault location.
    We also introduce a new technique for handling leakage by using gates with one-sided leakage or mixing two types of leakage reducing circuits: one to handle data leakage and one to handle ancilla leakage.
  • 15:45 — Shavindra Premaratne and Anne Matsuura, Intel Labs. Engineering the cost function of a variational quantum algorithm for implementation on near-term devices
    Abstract — Variational hybrid quantum-classical algorithms are some of the most promising workloads for near-term quantum computers without error correction. The aim of these variational algorithms is to guide the quantum system to a target state that minimizes a cost function, by varying certain parameters in a quantum circuit. This paper proposes a new approach for engineering cost functions to improve the performance of a certain class of these variational algorithms on today’s small qubit systems. We apply this approach to a variational algorithm that generates thermofield double states of the transverse field Ising model, which are relevant when studying phase transitions in condensed matter systems. We discuss the benefits and drawbacks of various cost functions, apply our new engineering approach, and show that it yields good agreement across the full temperature range.

Thu, Oct 15 — 10:45 — Quantum Education QEDU

  • 10:45 — Parham Pashaei, Haris Amiri, Rafael Haenel, Pedro Lopes, and Lukas Chrostowski, The University of British Columbia. Education resources for promoting talent in quantum computing — QEDU Track Best Paper Award
    Abstract — Quantum physics has a deep impact in today’s economy, with a historical role in industry sectors ranging from chemicals to electronics. Today, Quantum Computing advents to industrial relevance, signaling a renewed demand for a quantum-enabled workforce. Despite this clear interest, novelty and complexity frequently limit the exposure of young students to this topic, threatening the engagement of potential future practitioners and leaders. We advocate that quantum computing concepts can, effectively, be introduced at the primary and secondary school level. We substantiate this by compiling in this paper a number of publicly available resources, of our creation and from third parties, tested in real classes, for integrating Quantum Computing to the education for K-12 age groups.
  • 11:15 — Prashanti Angara, Ulrike Stege, and Andrew MacLean, University of Victoria. Quantum computing for high school students: An experience report
    Abstract —Quantum computing is an emerging field that can revolutionize our ability to solve problems and enable breakthroughs in the areas including optimization, machine learning, chemistry, and drug design. With improvements in the accuracy and power of quantum computers, the demand for a skilled workforce in quantum computing increases significantly. The theory of quantum computing lies at the crossroads of quantum physics, mathematics, and computer science. The field of quantum computing has matured and can be explored by students in either of these fields. Today, quantum computers are accessible and programmable over the internet. However, quantum computing education is still scarce. We describe our experiences in organizing and delivering a one-day workshop on quantum computing for high-school students with little to no experience in the above fields. The workshop introduces students to the world of quantum computing in innovative ways, such as newly designed “unplugged” activities for teaching basic quantum computing concepts. Overall, we take a programmatic approach and introduce students to the IBM Q Experience including Qiskit and Jupyter notebooks.
  • 11:45 — Thomas Plunkett, Terrill Frantz, Hamida Khatri, Praveen Ragendran and Sunny Midha, Harrisburg University of Science and Technology. A Survey of Quantum Computing Workforce Education
    Abstract — This survey paper identifies current education efforts to enable the broader, mainstream population to enter the quantum computing workforce. The paper identifies skill expectations, pedagogical techniques, and strategies to educate mainstream students from several different learning groups, including existing professionals, college students at the undergraduate and master’s level, and high school students. While lacking graduate level research in quantum related areas, current professionals have the education and maturity to enable a refocusing of attention and vocabulary building for this new technology area. For high school, undergraduate and graduate students, there are different preparation strategies based on their academic level as well as for different academic backgrounds, i.e. students with a physics, computer science, or math background, and students with no technical background. This paper also surveys current quantum degree programs.

Thu, Oct 15 — 10:45 — Quantum Computing QC4

  • 10:45 —Toshinari Itoko and Takashi Imamichi, IBM Research Tokyo. Scheduling of operations in quantum compiler
    Abstract — When scheduling quantum operations, a shorter overall execution time of the resulting schedule yields a better throughput and higher fidelity output. In this paper, we demonstrate that quantum operation scheduling can be interpreted as a special type of job-shop problem. On this basis, we provide its formulation as Constraint Programming while taking into account commutation between quantum operations. We show that this formulation improves the overall execution time of the resulting schedules in practice through experiments with a real quantum compiler and quantum circuits from two common benchmark sets.
  • 11:15 — Ellis Wilson, Sudhakar Singh and Frank Mueller, North Carolina State University. Just-in-time quantum circuit transpilation reduces noise
    Abstract — Running quantum programs is fraught with challenges on on today’s noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the noisy characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not “created equal”, but their noise level also changes over time. IBM calibrates their quantum systems once per day and reports noise levels (errors) at the time of such calibration. This information is subsequently used to map circuits to higher quality qubits and connections up to the next calibration point. This work provides evidence that there is room for improvement over this daily calibration cycle. It contributes a technique to measure noise levels (errors) related to qubits immediately before executing one or more sensitive circuits and shows that just-in-time noise measurements benefit late physical qubit mappings. With this just-in-time recalibrated transpilation, the fidelity of results is improved over IBM’s default mappings, which only uses their daily calibrations. The framework assess two major sources of noise, namely readout errors (measurement errors) and two-qubit gate/connection errors. Experiments indicate that the accuracy of circuit results improves by 3-304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution.
  • 11:45 — Lukas Burgholzer, Johannes Kepler University Linz; Rudy Raymond, IBM Research Tokyo and Robert Wille, Johannes Kepler University Linz. Verifying results of the IBM Qiskit quantum circuit compilation flow
    Abstract — Realizing a conceptual quantum algorithm on an actual physical device necessitates the algorithm’s quantum circuit description to undergo certain transformations in order to adhere to all constraints imposed by the hardware. In this regard, the individual high-level circuit components are first synthesized to the supported low-level gate-set of the quantum computer, before being mapped to the target’s architecture—utilizing several optimizations in order to improve the compilation result. Specialized tools for this complex task exist, e.g., IBM’s Qiskit, Google’s Cirq, Microsoft’s QDK, or Rigetti’s Forest. However, to date, the circuits resulting from these tools are hardly verified, which is mainly due to the immense complexity of checking if two quantum circuits indeed realize the same functionality. In this paper, we propose an efficient scheme for quantum circuit equivalence checking—specialized for verifying results of the IBM Qiskit quantum circuit compilation flow. To this end, we combine characteristics unique to quantum computing, e.g., its inherent reversibility, and certain knowledge about the compilation flow into a dedicated equivalence checking strategy. Experimental evaluations confirm that the proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less, whereas state-of-the-art techniques frequently time-out or require substantially more runtime. A corresponding open source implementation of the proposed method is publicly available.

Thu, Oct 15 — 13:00 — Quantum Computing QC5

  • 13:00 — Mathias Soeken and Martin Roetteler, Microsoft Quantum. Quantum circuits for functionally controlled NOT gates
    Abstract — We generalize quantum circuits for the Toffoli gate presented by Selinger and Jones for functionally controlled NOT gates, i.e., an X gate controlled by an arbitrary n-variable Boolean function instead of just the 2-input AND function. Our constructions target the Clifford+R1 gate set and make use of techniques proposed by Schuch and Siewert as well as Welch et al. We show constructions for the general case in which the target qubit is in an arbitrary state, as well for the special case in which the target qubit is known to be |0> before or after applying the gate. In addition, we show constructions that require no auxiliary qubits and constructions that have a rotation depth of 1.
  • 13:30 — Sima Esfandiarpour Borujeni, Wichita State University; Nam Nguyen, Boeing Research & Technology; Saideep Nannapaneni, Elizabeth Behrman and James Steck, Wichita State University. Experimental evaluation of quantum Bayesian networks on IBM QX hardware
    Abstract — Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.
  • 14:00 — Pranav Gokhale, University of Chicago; Olivia Angiuli, University of California, Berkeley; Yongshan Ding, Kaiwen Gui, University of Chicago; Teague Tomesh, Princeton University & Argonne National Laboratory; Martin Suchara, University of Chicago & Argonne National Laboratory; Margaret Martonosi, Princeton University and Frederic T. Chong, University of Chicago. Optimization of simultaneous measurement for variational quantum eigensolver applications — QC Track Best Paper Award
    Abstract — Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum computers. VQE aims to approximate solutions to exponentially-sized optimization problems by executing a polynomial number of quantum subproblems. However, the number of subproblems scales as $N^4$ for typical problems of interest—a daunting growth rate that poses a serious limitation for emerging applications such as quantum computational chemistry.We mitigate this issue by exploiting the simultaneous measurability of subproblems corresponding to commuting terms. Our technique transpiles VQE instances into a format optimized for simultaneous measurement, ultimately yielding 8-30x lower cost. Our work also encompasses a synthesis tool for compiling simultaneous measurement circuits with minimal overhead. We demonstrate experimental validation of our techniques by estimating the ground state energy of deuteron with a quantum computer. We also investigate the underlying statistics of simultaneous measurement and devise an adaptive strategy for mitigating harmful covariance terms.

Thu, Oct 15 — 15:15 — Quantum Computing QC6

  • 15:15 —Thien Nguyen, Anthony Santana and Alexander McCaskey, Oak Ridge National Laboratory. Extending XACC for quantum optimal control
    Abstract — Quantum computing vendors are beginning to open up application programming interfaces for direct pulse-level quantum control. With this, programmers can begin to describe quantum kernels of execution via sequences of arbitrary pulse shapes. This opens new avenues of research and development with regards to smart quantum compilation routines that enable direct translation of higher-level digital assembly representations to these native pulse instructions. In this work, we present an extension to the XACC system-level quantum-classical software framework that directly enables this compilation lowering phase via user-specified quantum optimal control techniques. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences that are optimal with respect to the backend system dynamics. Our work is modular and extensible, enabling third party optimal control techniques and strategies in both C\texttt{++} and Python. We demonstrate this extension with familiar gradient-based methods like gradient optimization based on analytic controls (GOAT), gradient ascent pulse engineering (GRAPE), and Krotov’s method. Our work serves as a foundational component of future quantum-classical compiler designs that lower high-level programmatic representations to low-level machine instructions.
  • 15:45 — B. C. A. Morrison, A. J. Landahl, D. S. Lobser, K. M. Rudinger, A. E. Russo, J. W. Van Der Wall and Peter Maunz, Sandia National Laboratories and University of New Mexico. Just another quantum assembly language (Jaqal)
    Abstract — QSCOUT is the Quantum Scientific Computing Open User Testbed, a trapped-ion quantum computer testbed realized at Sandia National Laboratories on behalf of the Department of Energy’s Office of Science and its Advanced Scientific Computing (ASCR) program. Jaqal, for Just Another Quantum Assembly Language, is the programming language used to specify programs executed on QSCOUT. We describe the capabilities of the Jaqal language, our approach in designing it, and the reasons for its creation. To learn more about QSCOUT and the Jaqal language developed for it, please visit or send an e-mail to
  • 16:15 — Jack Raymond, D-Wave Systems Burnaby, Guatum Rayaprolu, Ndiame Ndiaye, McGill University and Andrew King, D-Wave Systems Burnaby. Improving performance of logical qubits by parameter tuning and topology compensation
    Abstract — Optimization or sampling of arbitrary pairwise Ising models, in a quantum annealing protocol of constrained interaction topology, can be enabled by a minor-embedding procedure. The logical problem of interest is transformed to a physical (device programmable) problem, where one binary variable is represented by a logical qubit consisting of multiple physical qubits. In this paper we discuss tuning of this transformation for the cases of clique, biclique, and cubic lattice problems on the D-Wave 2000Q quantum computer. We demonstrate parameter tuning protocols in a variety of problems, focusing on anneal duration, chain strength, and post-processing. Inhomogeneities in coupling strength between logical qubits arising from minor embedding are shown to be mitigated by efficient strategies accounting for logical qubit topology.

Fri, Oct 16 — 10:45 — Quantum Benchmarks & Measurements QBM1

  • 10:45 — Kathleen Hamilton, Tyler Kharazi, Titus Morris, Alex McCaskey, Ryan Bennink and Raphael Pooser, Oak Ridge National Laboratory. Scalable quantum processor noise characterization
    Abstract — Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and sub- stantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansion. Our method can also be used to characterize various types of correlation error.
  • 11:15 — Sam Tomkins and Rogério de Sousa, University of Victoria. Noise mitigation with delay pulses in the IBM Quantum Experience
    Abstract — One of the greatest challenges for current quantum computing hardware is how to obtain reliable results from noisy
    devices. A recent paper [A. Kandala et al., Nature 567, 491 (2019)] described a method for injecting noise by stretching gate times, enabling the calculation of quantum expectation values as a function of the amount of noise in the IBM Q devices. Extrapolating to zero noise led to excellent agreement with exact results. Here an alternative scheme is described that employs the intentional addition of identity pulses, pausing the device periodically in order to gradually subject the quantum computation to increased levels of noise. The scheme is implemented in a one qubit circuit on an IBM-Q device. It is determined that this is an effective method for controlled addition of noise, and further, that using noisy results to perform extrapolation can lead to improvements in the final output, provided careful attention is paid to how the extrapolation is carried out.

Fri, Oct 16 — 13:00 — Quantum Benchmarks & Measurements QBM2

  • 13:00 — Tristan Zaborniak and Rogério de Sousa, University of Victoria. In situ noise characterization of the D-Wave quantum annealer
    Abstract — Magnetic flux noise limits the performance of quantum computers based on superconducting flux qubits by altering their states in an uncontrolled manner throughout computations and reducing their coherence time. In quantum annealers, this noise introduces fluctuations to the linear constants of the original problem Hamiltonian, such that they find the ground states of problems perturbed from those programmed. Here we describe how to turn this drawback into a method to probe the flux noise frequency dependence \it{in situ} of the D-Wave 2000Q quantum annealer. The method relates the autocorrelation of the readout-state of a qubit repeatedly collapsed from uniform superposition to that of the flux noise impingent on the qubit. We show that this leads to an estimate for the noise spectral density affecting D-Wave qubits under normal operating conditions. The method is general and can be used to characterize noise in all architectures for quantum annealing.
  • 13:30 — Samudra Dasgupta and Travis Humble, Oak Ridge National Laboratory. Characterizing the Stability of NISQ Devices — QBM Track Best Paper Award
    Abstract — In this study, we focus on the question of stability of NISQ devices. The parameters that define the device stability profile are motivated by the work of DiVincenzo where the requirements for physical implementation of quantum computing are discussed. We develop the metrics and theoretical framework to quantify the DiVincenzo requirements and study the stability of those key metrics. The basis of our assessment is histogram similarity (in time and space). For identical experiments, devices which produce reproducible histograms in time, and similar histograms in space, are considered more reliable. To investigate such reliability concerns robustly, we propose an intuitive moment-based distance (MBD) metric. We illustrate our methodology using data collected from IBM’s Yorktown device. Two types of assessments are discussed: spatial stability and temporal stability.