Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session A72: Quantum Application BenchmarkingFocus
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Sponsoring Units: DQI Chair: Seth Merkel, IBM TJ Watson Research Center Room: Room 406 |
Monday, March 6, 2023 8:00AM - 8:12AM |
A72.00001: Benchmarking Quantum Computers: Best Practices Mirko Amico, Dave McKay, Andrew Wack, Petar Jurcevic, Luke C Govia As quantum computers grow in size and scope the question of how best to benchmark performances has become one of great importance. Here we discuss some of the questions around benchmarking devices at scale, some of the issues that arise and best practices. In particular, we focus on defining benchmarking and diagnostic methods, the use of error-mitigation, possible weaknesses to gaming strategies and the role of applications-oriented benchmarking in the current landscape of quantum computing. Furthermore, we consider one of the available applications-oriented benchmarking suite and show the effects of error-suppression and mitigation strategies, along with gaming strategies, on the benchmark. |
Monday, March 6, 2023 8:12AM - 8:24AM |
A72.00002: Validating models of quantum computer performance Megan L Dahlhauser, Timothy J Proctor, Robin J Blume-Kohout, Kevin Young Modeling low-level components of quantum computers is critical to understand quantum systems, identify errors, and pursue opportunities for engineering improvements. Tantamount to these tasks is the expectation that an effective and useful model should provide accurate predictions of circuit outcomes. Successfully predicting circuit outcomes validates our model and understanding of our quantum system, whereas failing to predict circuit outcomes indicates either a poor, inappropriate, or obsolete characterization. While evaluating the performance of a model is vital, it is not a binary metric and determining when a model is performing well or at least satisfactorily can be difficult. |
Monday, March 6, 2023 8:24AM - 8:36AM |
A72.00003: Quantum Volume for Measurement-based Quantum Processors Yuxuan Zhang, Hassan Shapourian, Daoheng Niu, Alireza Shabani Defining metrics for near-term quantum computing processors has been an integral part of quantum hardware research and development efforts. Such quantitative characteristics are not only useful for reporting the progress and comparing different quantum platforms but also essential for identifying the bottlenecks and designing a technology roadmap. Most metrics, such as the quantum volume (QV), were originally introduced for circuit-based quantum computers and were not immediately applicable to measurement-based quantum computing (MBQC) units. In this talk, I introduce a framework to map physical noises and imperfections in MBQC processes to logical errors in equivalent quantum circuits, thereby enabling the well-known metrics to characterize MBQC. I further explain our framework in the case of a continuous-variable cluster state based on the Gottesman-Kitaev-Preskill (GKP) encoding as a near-term candidate for photonic quantum computing, derive the effective logical gate error channels, and calculate the QV in terms of the GKP squeezing and photon loss rate. |
Monday, March 6, 2023 8:36AM - 8:48AM |
A72.00004: Elliptic curve cryptographic challenges for benchmarking early fault-tolerant quantum computers Pierre-Luc Dallaire-Demers, William Doyle, Timothy Foo As we are transitioning from the NISQ era to the early fault-tolerant quantum computing era, it becomes imperative to benchmark the progress of the technology at performing tasks that are otherwise classically intractable. Despite the fact that there will be important societal consequences of enabling the ability to factor large numbers and compute discrete logarithms, the exact timeline of the development of those capabilities remains approximate and speculative. In this work, we take a first step at quantitatively characterizing the cryptanalytic capabilities of quantum computers in the next decade by developing sets of elliptic curve cryptographic challenges with a fine gradation in difficulty in order to provide accurate statistical signal of the progress of the quantum hardware. We define two approaches to constructing these challenges. In the first one, we modulate the difficulty by giving sets of hint bits of the solution for challenges on a 256-bit elliptic curve. In the second approach, we use the same curve equation over smaller primes to get a smaller group such that the Shor algorithm is easy to compile. The advantage of using elliptic curves for our challenges is that we can deploy our benchmarking platform on blockchains for a decentralized access to the results. |
Monday, March 6, 2023 8:48AM - 9:00AM |
A72.00005: Characteristics of Optimization Applications as Quantum Performance Benchmarks Pratik Sathe, Thomas Lubinski, Carleton Coffrin, Joshua Apanavicius, Catherine McGeoch, David E Bernal Neira Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) are expected to demonstrate significant run-time performance benefits over classical solutions. We review existing methods for characterizing the performance of classical optimization algorithms and use these to compare solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum processors and simulators. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the quality of solution for iterative hybrid quantum-classical applications. Of particular relevance is how the nature of the problem input configuration impacts the solution quality, the length of time required to achieve a solution, and the algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing open-source QED-C Application-Oriented Benchmark suite. The suite can be executed on various quantum simulators, quantum hardware backends, and a quantum annealing device. |
Monday, March 6, 2023 9:00AM - 9:12AM |
A72.00006: Hardware-efficient learning of quantum many-body states Katherine Van Kirk, Jordan Cotler, Hsin-Yuan Huang, Mikhail D Lukin Recent developments in quantum learning theory have established that a modest number of randomized measurements suffices to learn exponentially many properties of a quantum many-body system. However, implementing the appropriate randomized measurements requires universal local control over individual qubits, which is unavailable in many experimental platforms. Here, we generalize the framework of classical shadow tomography to systems where control over individual qubits and measurement capabilities are limited. Employing techniques from learning theory, we provide a general framework and specific algorithms for hardware-efficient learning with rigorous guarantees. We numerically demonstrate the effectiveness of our algorithms through the example of estimating energy densities in a U(1) lattice gauge theory with limited control. We also combine our framework with techniques from unsupervised learning to establish that certain topological states of matter can be distinguished even with very limited measurement capabilities. |
Monday, March 6, 2023 9:12AM - 9:24AM |
A72.00007: A scalable classical verification reveals the gap of the state-of-the-art Gaussian Boson Sampling experiments Yufan Zheng, Yingkang Cao, Xiaodi Wu Gaussian Boson Sampling (GBS) is one major candidate for establishing quantum computational advantage in the near term. Despite promising experimental progress, most existing validation schemes of GBS experiments merely rule out a few experimentally-motivated alternative physical hypotheses and fail to certify the device's output distribution directly, which is critical for establishing the computational hardness. Inspired by the symmetry property of Hafnian functions, we propose a scalable classical verification scheme of GBS, based on symmetry testing via a coarse-grained statistical heuristic, with strong theoretical and empirical evidence for its validity. Theoretically, when symmetry properties are approximately established, we prove that the device's output distribution is close to GBS under a mild assumption of the device. Empirically, based on classical simulation of up to 20 modes, we observe that our protocol can successfully distinguish GBS devices from all known alternative physical hypotheses and is further resilient to a small photon loss in the experiment. We perform part of our verification scheme on Xanadu's Borealis machine, which reveals a large inhomogeneity among alleged 216 squeezed modes that fail our symmetry test, and hence a gap for the claimed computational advantage based on GBS. |
Monday, March 6, 2023 9:24AM - 9:36AM |
A72.00008: An entanglement-based volumetric benchmark for near-term quantum hardware Kathleen Hamilton, Nouamane Laanait, Akhil Francis, Sophia Economou, George Barron, Kubra Yeter-Aydeniz, Titus Morris, Harrison Cooley, Muhun Kang, Raphael Pooser We introduce a volumetric benchmark for near-term quantum platforms based on the generation and verification of genuine entanglement across n-qubits using graph states and direct stabilizer measurements. Our benchmark evaluates the robustness of multipartite and bipartite n-qubit entanglement with respect to many sources of hardware noise: qubit decoherence, CNOT and swap gate noise, and readout error. We demonstrate our benchmark on multiple superconducting qubit platforms available from IBM (ibmq_belem, ibmq_toronto, ibmq_guadalupe and ibmq_jakarta). Subsets of n < 10 qubits are used for graph state preparation and stabilizer measurement. Evaluation of genuine and biseparable entanglement witnesses we report observations of 5 qubit genuine entanglement, but robust multipartite entanglement is difficult to generate for n > 4 qubits and identify two-qubit gate noise as strongly correlated with the quality of genuine multipartite entanglement. |
Monday, March 6, 2023 9:36AM - 9:48AM |
A72.00009: Tensor Network Tomography using Classical Shadows Jonathan Kunjummen, Jacob M Taylor Classical shadow tomography is a powerful tool for extracting large amounts of physically relevant information from a quantum state using a polynomial number of measurements. However, classical shadows are not quantum states — they are not completely positive, and thus are limited to the estimation of expectation values. Intermediate representations of states derived from shadows that are completely positive but retain the computational benefits of polynomial scaling in estimation are a valuable tool, if they can be constructed with only polynomial (in qubit number) classical computation. Here we present theoretical and numerical progress toward constructing tensor network state ansatzes from classical shadows data. Our starting point is an existing protocol by Baumgratz, Gross, Cramer, and Plenio for reconstructing Matrix Product States (MPSs) from local correlation functions. We implement this protocol with data from classical shadows. We also go beyond the protocol of Plenio et al. by investigating the use of shadow data to reconstruct the locality structure of system, after which the MPS tomography algorithm can be applied. We discuss prospects for extending this technique to different tensor network ansatzes, such as PEPS or MERA, and to higher dimensions. |
Monday, March 6, 2023 9:48AM - 10:24AM |
A72.00010: Characterizing errors on multi-qubit superconducting qubit processors for improved circuit compilation and gate decomposition Invited Speaker: Ravi K Naik In the current era of noisy quantum processors, the performance of quantum algorithms greatly depends on the nature of errors the processors interact with. Here, we explore the interplay of errors and the structure of gate decomposition in algorithms with experiments on multi-qubit superconducting qubit processors. We describe how we characterize the types of errors afflicting quantum processors, including investigating the context of error with respect to the prior state and multi-qubit environment. We demonstrate how randomization, a key ingredient in characterization, is utilized in circuit compilation to produce predictable algorithm performance with manageable error scaling. Finally, we show how decomposition of gate cycles in algorithms can be optimized to account for the multi-qubit error environment inherently present and characterized in current processors. |
Monday, March 6, 2023 10:24AM - 10:36AM |
A72.00011: Variational Approach to Quantum State Tomography based on Maximal Entropy Formalism Rishabh Gupta Quantum state tomography is an integral part of quantum computation and offers the starting point for the validation of various quantum devices. One of the central tasks in the field of state tomography is to reconstruct with high fidelity, the quantum states of a quantum system. From an experiment on a real quantum device, one can obtain the mean measurement values of different operators. With such a data as input, we employ the maximal entropy formalism to construct the least biased mixed quantum state that is consistent with the given set of expectation values. Even though in principle, the reported formalism is quite general and should work for an arbitrary set of observables, in practice we shall demonstrate the efficacy of the algorithm on an informationally complete (IC) set of Hermitian operators. Such a set possesses the advantage of uniquely specifying a single quantum state from which the experimental measurements have been sampled and hence renders the rare opportunity to not only construct a least-biased quantum state but even replicate the exact state prepared experimentally within a preset tolerance. The primary workhorse of the algorithm is re-constructing an energy function which we designate as the effective Hamiltonian of the system, and parameterizing it with Lagrange multipliers, according to the formalism of maximal entropy. These parameters are thereafter optimized variationally so that the reconstructed quantum state of the system converges to the true quantum state within an error threshold. To this end, we employ a parameterized quantum circuit and a hybrid quantum-classical variational algorithm to obtain such a target state making our recipe easily implementable on a near-term quantum device. |
Monday, March 6, 2023 10:36AM - 10:48AM |
A72.00012: Detecting quantum complexity using transformer based neural network (I) Kaarthik Varma, Hyejin Kim, Chao Wan, Yiqing Zhou, Yuri Lensky, Kilian Q Weinberger, Eun-Ah Kim The minimum depth required for a quantum circuit to perform a given computation is the quantum complexity of the circuit. This quantity is relevant for establishing a regime of quantum supremacy for the task. We want to address whether it can be identified from the sampled output of a quantum circuit. The quantum circuit is simulated classically using google's Cirq software, and the output wavefunction is sampled to generate a set of measured bit-strings. The circuit we choose is a pseudo-random quantum circuit consisting of random one-qubit and local two-qubit gates implemented on 20 qubits, following the reference [1]. Noise is then introduced in the circuit using a depolarizing channel, similar to the noise model for the Sycamore processor. Using the simulated data for noisy circuits, it will also be possible to address the effect of increasing noise levels on the signal for quantum complexity. We use the simulated data to train a transformer-based neural network model. |
Monday, March 6, 2023 10:48AM - 11:00AM |
A72.00013: Detecting quantum complexity using transformer-based neural network (II) Hyejin Kim, Kaarthik Varma, Chao Wan, Yiqing Zhou, Yuri D Lensky, Kilian Q Weinberger, Eun-Ah Kim Various data-driven attempts with classical agents have been made to analyze and capture the properties of quantum circuit, primarily to understand the region of quantum supremacy. Here, we also introduce a data-driven supervised-learning approach for our question of the quantum complexity of the given quantum circuit outputs. In particular, we focus on the transformer-based neural network model, consisting of several attention modules. With the conjecture that single circuit output will not solely demonstrate system property, a set of measured bit-strings from the wave function is fed into the neural network. As the experimental circuit data has a high level of noise, we first focus on the noiseless data to see whether there is a significant signal in the circuit data. We present a model performance on depth classification with varying noise levels, further discussing the possibility of quantum complexity. |
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