4,158 research outputs found
Comparing the Overhead of Topological and Concatenated Quantum Error Correction
This work compares the overhead of quantum error correction with concatenated
and topological quantum error-correcting codes. To perform a numerical
analysis, we use the Quantum Resource Estimator Toolbox (QuRE) that we recently
developed. We use QuRE to estimate the number of qubits, quantum gates, and
amount of time needed to factor a 1024-bit number on several candidate quantum
technologies that differ in their clock speed and reliability. We make several
interesting observations. First, topological quantum error correction requires
fewer resources when physical gate error rates are high, white concatenated
codes have smaller overhead for physical gate error rates below approximately
10E-7. Consequently, we show that different error-correcting codes should be
chosen for two of the studied physical quantum technologies - ion traps and
superconducting qubits. Second, we observe that the composition of the
elementary gate types occurring in a typical logical circuit, a fault-tolerant
circuit protected by the surface code, and a fault-tolerant circuit protected
by a concatenated code all differ. This also suggests that choosing the most
appropriate error correction technique depends on the ability of the future
technology to perform specific gates efficiently
Quantum resource estimates for computing elliptic curve discrete logarithms
We give precise quantum resource estimates for Shor's algorithm to compute
discrete logarithms on elliptic curves over prime fields. The estimates are
derived from a simulation of a Toffoli gate network for controlled elliptic
curve point addition, implemented within the framework of the quantum computing
software tool suite LIQ. We determine circuit implementations for
reversible modular arithmetic, including modular addition, multiplication and
inversion, as well as reversible elliptic curve point addition. We conclude
that elliptic curve discrete logarithms on an elliptic curve defined over an
-bit prime field can be computed on a quantum computer with at most qubits using a quantum circuit of at most Toffoli gates. We are able to classically simulate the
Toffoli networks corresponding to the controlled elliptic curve point addition
as the core piece of Shor's algorithm for the NIST standard curves P-192,
P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to
recent resource estimates for Shor's factoring algorithm. The results also
support estimates given earlier by Proos and Zalka and indicate that, for
current parameters at comparable classical security levels, the number of
qubits required to tackle elliptic curves is less than for attacking RSA,
suggesting that indeed ECC is an easier target than RSA.Comment: 24 pages, 2 tables, 11 figures. v2: typos fixed and reference added.
ASIACRYPT 201
Layered architecture for quantum computing
We develop a layered quantum computer architecture, which is a systematic
framework for tackling the individual challenges of developing a quantum
computer while constructing a cohesive device design. We discuss many of the
prominent techniques for implementing circuit-model quantum computing and
introduce several new methods, with an emphasis on employing surface code
quantum error correction. In doing so, we propose a new quantum computer
architecture based on optical control of quantum dots. The timescales of
physical hardware operations and logical, error-corrected quantum gates differ
by several orders of magnitude. By dividing functionality into layers, we can
design and analyze subsystems independently, demonstrating the value of our
layered architectural approach. Using this concrete hardware platform, we
provide resource analysis for executing fault-tolerant quantum algorithms for
integer factoring and quantum simulation, finding that the quantum dot
architecture we study could solve such problems on the timescale of days.Comment: 27 pages, 20 figure
Improving Quantum Circuit Synthesis with Machine Learning
In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations
of quantum algorithms that minimize the number of expensive and error prone
multi-qubit gates is vital to ensure computations produce meaningful outputs.
Unitary synthesis, the process of finding a quantum circuit that implements
some target unitary matrix, is able to solve this problem optimally in many
cases. However, current bottom-up unitary synthesis algorithms are limited by
their exponentially growing run times. We show how applying machine learning to
unitary datasets permits drastic speedups for synthesis algorithms. This paper
presents QSeed, a seeded synthesis algorithm that employs a learned model to
quickly propose resource efficient circuit implementations of unitaries. QSeed
maintains low gate counts and offers a speedup of in synthesis time
over the state of the art for a 64 qubit modular exponentiation circuit, a core
component in Shor's factoring algorithm. QSeed's performance improvements also
generalize to families of circuits not seen during the training process.Comment: 11 pages, 10 figure
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