7 research outputs found
Software Techniques to Mitigate Errors on Noisy Quantum Computers
Quantum computers are domain-specific accelerators that can provide a large speedup
for important problems. Quantum computers with few tens of qubits have already been
demonstrated, and machines with 100+ qubits are expected soon. These machines face
significant reliability and scalability challenges. The high hardware error rates limit quantum
computers. To enable quantum speedup, it is essential to mitigate hardware errors.
Our first work exploits the variability in the error rates of qubits to steer more operations
towards qubits with lower error rates and avoid error-prone qubits. Our second work looks at
executing different versions of the programs tuned to cause diverse mistakes so that the
machine is less vulnerable to correlated errors, thereby making it easier to infer the correct
answer. Our third work looks at exploiting the state-dependent bias in measurement errors
(state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to measure
the stronger state. We perform our evaluations on real quantum machines from IBM and
demonstrate significant improvement in the overall system reliability.Ph.D
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 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 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
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