89 research outputs found
Readiness of Quantum Optimization Machines for Industrial Applications
There have been multiple attempts to demonstrate that quantum annealing and,
in particular, quantum annealing on quantum annealing machines, has the
potential to outperform current classical optimization algorithms implemented
on CMOS technologies. The benchmarking of these devices has been controversial.
Initially, random spin-glass problems were used, however, these were quickly
shown to be not well suited to detect any quantum speedup. Subsequently,
benchmarking shifted to carefully crafted synthetic problems designed to
highlight the quantum nature of the hardware while (often) ensuring that
classical optimization techniques do not perform well on them. Even worse, to
date a true sign of improved scaling with the number of problem variables
remains elusive when compared to classical optimization techniques. Here, we
analyze the readiness of quantum annealing machines for real-world application
problems. These are typically not random and have an underlying structure that
is hard to capture in synthetic benchmarks, thus posing unexpected challenges
for optimization techniques, both classical and quantum alike. We present a
comprehensive computational scaling analysis of fault diagnosis in digital
circuits, considering architectures beyond D-wave quantum annealers. We find
that the instances generated from real data in multiplier circuits are harder
than other representative random spin-glass benchmarks with a comparable number
of variables. Although our results show that transverse-field quantum annealing
is outperformed by state-of-the-art classical optimization algorithms, these
benchmark instances are hard and small in the size of the input, therefore
representing the first industrial application ideally suited for testing
near-term quantum annealers and other quantum algorithmic strategies for
optimization problems.Comment: 22 pages, 12 figures. Content updated according to Phys. Rev. Applied
versio
Towards Quantum Belief Propagation for LDPC Decoding in Wireless Networks
We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based
decoder design for Low Density Parity Check (LDPC) error control codes, which
have found many useful applications in Wi-Fi, satellite communications, mobile
cellular systems, and data storage systems. QBP reduces the LDPC decoding to a
discrete optimization problem, then embeds that reduced design onto quantum
annealing hardware. QBP's embedding design can support LDPC codes of block
length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits.
We evaluate performance on real quantum annealer hardware, performing
sensitivity analyses on a variety of parameter settings. Our design achieves a
bit error rate of in 20 s and a 1,500 byte frame error rate of
in 50 s at SNR 9 dB over a Gaussian noise wireless channel.
Further experiments measure performance over real-world wireless channels,
requiring 30 s to achieve a 1,500 byte 99.99 frame delivery rate at
SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft
belief propagation LDPC decoder, by reaching a bit error rate of and
a frame error rate of at an SNR 2.5--3.5 dB lower. In terms of
limitations, QBP currently cannot realize practical protocol-sized
( Wi-Fi, WiMax) LDPC codes on current QA processors. Our
further studies in this work present future cost, throughput, and QA hardware
trend considerations
Experimental investigation of performance differences between Coherent Ising Machines and a quantum annealer
Physical annealing systems provide heuristic approaches to solving NP-hard
Ising optimization problems. Here, we study the performance of two types of
annealing machines--a commercially available quantum annealer built by D-Wave
Systems, and measurement-feedback coherent Ising machines (CIMs) based on
optical parametric oscillator networks--on two classes of problems, the
Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer
outperforms the CIMs on MAX-CUT on regular graphs of degree 3. On denser
problems, however, we observe an exponential penalty for the quantum annealer
() relative to CIMs () for fixed anneal times, on both the SK model and on 50%-edge-density
MAX-CUT, where the coefficients and
are problem-class-dependent. On instances with over vertices, a
several-orders-of-magnitude time-to-solution difference exists between CIMs and
the D-Wave annealer. An optimal-annealing-time analysis is also consistent with
a significant projected performance difference. The difference in performance
between the sparsely connected D-Wave machine and the measurement-feedback
facilitated all-to-all connectivity of the CIMs provides strong experimental
support for efforts to increase the connectivity of quantum annealers.Comment: 12 pages, 5 figures, 1 table (main text); 14 pages, 12 figures, 2
tables (supplementary
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