Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs

In support of the growing interest in quantum computing experimentation, programmers need new tools to write quantum algorithms as program code. Compared to debugging classical programs, debugging quantum programs is difficult because programmers have limited ability to probe the internal states of quantum programs; those states are difficult to interpret even when observations exist; and programmers do not yet have guidelines for what to check for when building quantum programs. In this work, we present quantum program assertions based on statistical tests on classical observations. These allow programmers to decide if a quantum program state matches its expected value in one of classical, superposition, or entangled types of states. We extend an existing quantum programming language with the ability to specify quantum assertions, which our tool then checks in a quantum program simulator. We use these assertions to debug three benchmark quantum programs in factoring, search, and chemistry. We share what types of bugs are possible, and lay out a strategy for using quantum programming patterns to place assertions and prevent bugs.Comment: In The 46th Annual International Symposium on Computer Architecture (ISCA '19). arXiv admin note: text overlap with arXiv:1811.0544

The Dirty Secret of SSDs: Embodied Carbon

Scalable Solid-State Drives (SSDs) have revolutionized the way we store and access our data across datacenters and handheld devices. Unfortunately, scaling technology can have a significant environmental impact. Across the globe, most semiconductor manufacturing use electricity that is generated from coal and natural gas. For instance, manufacturing a Gigabyte of Flash emits 0.16 Kg CO$_2$ and is a significant fraction of the total carbon emission in the system. We estimate that manufacturing storage devices has resulted in 20 million metric tonnes of CO$_2$ emissions in 2021 alone. To better understand this concern, this paper compares the sustainability trade-offs between Hard Disk Drives (HDDs) and SSDs and recommends methodologies to estimate the embodied carbon costs of the storage system. In this paper, we outline four possible strategies to make storage systems sustainable. First, this paper recommends directions that help select the right medium of storage (SSD vs HDD). Second, this paper proposes lifetime extension techniques for SSDs. Third, this paper advocates for effective and efficient recycling and reuse of high-density multi-level cell-based SSDs. Fourth, specifically for hand-held devices, this paper recommends leveraging elasticity in cloud storage.Comment: In the proceedings of the 1st Workshop on Sustainable Computer Systems Design and Implementation (HotCarbon 2022

Understanding Side-Channel Vulnerabilities in Superconducting Qubit Readout Architectures

Frequency-multiplexing is an effective method to achieve resource-efficient superconducting qubit readout. Allowing multiple resonators to share a common feedline, the number of cables and passive components involved in the readout of a qubit can be drastically reduced. However, this improvement in scalability comes at the price of a crucial non-ideality -- an increased readout crosstalk. Prior works have targeted building better devices and discriminators to reduce its effects, as readout-crosstalk-induced qubit measurement errors are detrimental to the reliability of a quantum computer. However, in this work, we show that beyond the reliability of a system, readout crosstalk can introduce vulnerabilities in a system being shared among multiple users. These vulnerabilities are directly related to correlated errors due to readout crosstalk. These correlated errors can be exploited by nefarious attackers to predict the state of the victim qubits, resulting in information leakage

Enabling Leakage Reduction via Fast and High-Fidelity Qutrit Readout

Quantum Error Correction (QEC) is key to operating quantum processors effectively at practical scales. QECs are designed for systems comprising two-level systems, such as qubits, as their fundamental building block. Unfortunately, qubits can leak to third and higher energy levels, making these leaks challenging to detect and mitigate. If not addressed promptly, these leakage errors can proliferate and undermine QEC, leading to significant computational inaccuracies. Here, we present a high-fidelity three-level qubit readout protocol that is simple to implement on dedicated hardware such as FPGAs. Our design enables faster and higher-fidelity leakage detection over approaches using conventional qubit-state discriminators

Exploiting many-body localization for scalable variational quantum simulation

Variational quantum algorithms have emerged as a promising approach to achieving practical quantum advantages using near-term quantum devices. Despite their potential, the scalability of these algorithms poses a significant challenge. This is largely attributed to the "barren plateau" phenomenon, which persists even in the absence of noise. In this work, we explore the many-body localization (MBL)-thermalization phase transitions within a framework of Floquet-initialized variational quantum circuits and investigate how MBL could be used to avoid barren plateaus. The phase transitions are observed through calculations of the inverse participation ratio, the entanglement entropy, and a metric termed low-weight stabilizer R\'enyi entropy. By initializing the circuit in the MBL phase and employing an easily preparable initial state, we find it is possible to prevent the formation of a unitary 2-design, resulting in an output state with entanglement that follows an area- rather than a volume-law, and which circumvents barren plateaus throughout the optimization. Utilizing this methodology, we successfully determine the ground states of various model Hamiltonians across different phases and show that the resources required for the optimization are significantly reduced. We have further validated the MBL approach through experiments carried out on the 127-qubit $ibm\_brisbane$ quantum processor. These experiments confirm that the gradients needed to carry out variational calculations are restored in the MBL phase of a Heisenberg model subject to random unitary "kicks". These results provide new insights into the interplay between MBL and quantum computing, and suggest that the role of MBL states should be considered in the design of quantum algorithms.Comment: 18 pages, 10 figure

Synthesizing Quantum-Circuit Optimizers

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing. (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.Comment: Full version of PLDI 2023 pape

Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures

Reading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classification to assign the qubit states `0' or `1'. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1-10%. High readout accuracy is essential for enabling high fidelity for near-term noisy quantum computers and error-corrected quantum computers of the future. Prior works have used machine-learning-assisted single-shot qubit-state classification, where a deep neural network was used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit the scalability of systems, especially if fast hardware discrimination is required. This state-of-the-art baseline design cannot be implemented on off-the-shelf FPGAs used for the control and readout of superconducting qubits in most systems, which increases the overall readout latency as discrimination has to be performed in software. In this work, we propose HERQULES, a scalable approach to improve qubit-state discrimination by using a hierarchy of matched filters in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. We achieve substantially higher readout accuracies (16.4% relative improvement) than the baseline with a scalable design that can be readily implemented on off-the-shelf FPGAs. We also show that HERQULES is more versatile and can support shorter readout durations than the baseline design without additional training overheads